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Downscaled seasonal forecasts using an ensemble of regional models
NASA Astrophysics Data System (ADS)
The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional effort to evaluate the usefulness of dynamically downscaled global seasonal forecasts. Seven regional climate models have downscaled 10-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) for each winter season (December-April) of 1982-2003. The target region for downscaling is the continental United States. MRED investigators also have developed methods and metrics for analysis of downscaled forecasts. These include an Added Value Index that quantifies skill improvement of a downscaled forecast compared to the corresponding global forecast. Results show that added value from downscaling depends on location, forecast variable, and lead time. Locations with added value are generally in the western United States, and added value tends to be greater for precipitation than for temperature. Downscaled forecasts have greatest skill for seasonal precipitation anomalies in strong El Niño events such as 1982-83 and 1997-98. In most circumstances area averaged seasonal precipitation for the regional models closely tracks the corresponding results for the global model, though with an offset that varies considerably amongst the regional models. There is large spread amongst the 15 CFS ensemble members and this carries through to the corresponding downscaled forecasts. Because of the strong dependence of downscaled results on the global model, future experiments should test the use of multiple global models downscaled by multiple regional models.
Arritt, R.
Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill
NASA Astrophysics Data System (ADS)
assess the value of dynamical versus statistical downscaling of National Centers for Environmental Prediction's (NCEP) Climate Forecast System (CFS) winter season forecasts for seasonal hydrologic forecasting. Dynamically downscaled CFS forecasts for 1 December to 30 April of 1982-2003 were obtained from the Multi-RCM Ensemble Downscaling (MRED) project that used multiple Regional Climate Models (RCMs) to downscale CFS forecasts. Statistical downscaling of CFS forecasts was achieved by a much simpler bias correction and spatial downscaling method. We evaluate forecast accuracy of runoff (RO), soil moisture (SM), and snow water equivalent produced by a hydrology model forced with dynamically (the MRED forecasts) and statistically downscaled CFS forecasts in comparison with predictions of those variables produced by forcing the same hydrology model with gridded observations (reference data set). Our results show that the MRED forecasts produce modest skill beyond what results from statistical downscaling of CFS. Although the improvement in hydrologic forecast skill associated with the ensemble average of the MRED forecasts (Multimodel) relative to statistical downscaled CFS forecasts is field significant for RO and SM forecasts with up to 3 months lead, the region of improvement is mainly limited to parts of the northwest and north central U.S. In general, one or more RCMs outperform the other RCMs as well as the Multimodel. Hence, we argue that careful selection of RCMs (based on their hindcast skill over any given region) is critical to improving hydrologic forecast skill using dynamical downscaling.
Shukla, Shraddhanand; Lettenmaier, Dennis P.
The ENSEMBLES Statistical Downscaling Portal
NASA Astrophysics Data System (ADS)
The demand for high-resolution seasonal and ACC predictions is continuously increasing due to the multiple end-user applications in a variety of sectors (hydrology, agronomy, energy, etc.) which require regional meteorological inputs. To fill the gap between the coarse-resolution grids used by global weather models and the regional needs of applications, a number of statistical downscaling techniques have been proposed. Statistical downscaling is a complex multi-disciplinary problem which requires a cascade of different scientific tools to access and process different sources of data, from GCM outputs to local observations and to run complex statistical algorithms. Thus, an end-to-end approach is needed in order to link the outputs of the ensemble prediction systems to a range of impact applications. To accomplish this task in an interactive and user-friendly form, a Web portal has been developed within the European ENSEMBLES project, integrating the necessary tools and providing the appropriate technology for distributed data access and computing. In this form, users can obtain their downscaled data testing and validating different statistical methods (from the categories weather typing, regression or weather generators) in a transparent form, not worrying about the details of the downscaling techniques and the data formats and access.
Cofino, Antonio S.; San-Martín, Daniel; Gutiérrez, Jose M.
Precipitation intensity in an ensemble of downscaled seasonal forecasts
NASA Astrophysics Data System (ADS)
Dynamical downscaling using regional climate models (RCMs) has the potential to produce improved representation of precipitation intensity and frequency in seasonal forecasts, owing to improved spatial resolution of dynamical processes and topography compared with global models. Ensemble forecasts also can improve on single-model forecasts by accounting for uncertainties in initial conditions and model formulation. This study examines results from an ensemble of downscaled seasonal forecasts, focusing on monthly and seasonal statistics of the frequency and intensity of precipitation. We present results from seven RCMs run as part of the Multi-Regional climate model Ensemble Downscaling (MRED) project. These RCMs downscaled forecasts produced by the National Centers for Environmental Prediction's (NCEP) Climate Forecast System (CFS) version 1 from 1983-2003 for the winter season (January through April) over the continental United States. Preliminary comparisons show a general overforecast in frequency and intensity of extreme precipitation (50 mm per day or greater) in the RCMs compared to observations, particularly in January and April. When comparing frequency and intensity of precipitation between months, some RCMs exhibit variations from month to month. For instance, the MM5 regional model had more frequent, intense precipitation than the other RCMs and observations in January, but by April was closer to observations. Two versions of the Regional Spectral Model (RSM) tended toward more frequent, intense precipitation by April. It is likely that the choice of the model configuration, such as representation of moist physics, plays a significant role in these differences. While the CFS global model performs reasonably well for lighter precipitation events compared to observations, it does not forecast many extreme precipitation events. The RCMs simulate these extreme precipitation events, albeit sometimes too intense or too frequent.
Ansorge, A. J.; Arritt, R. W.
A method for statistical downscaling of seasonal ensemble predictions
A model output statistics based method for downscaling seasonal ensemble predictions is outlined, and examples of ensemble predictions of precipitation and 2-m temperature are verified against observing stations in Scandinavia, Europe, north-western America, the contiguous United States and Australia. The downscaling from seasonal ensemble predictions from coupled ocean\\/atmosphere general circulation models to daily precipitation time series for individual observing stations
Henrik Feddersen; Uffe Andersen
A method for statistical downscaling of seasonal ensemble predictions
A model output statistics based method for downscaling of seasonal ensemble predictions is outlined, and examples of ensemble predictions of precipitation and 2m-temperature are veri- fied against observing stations in Scandinavia, Europe, northwestern America, the contiguous United States and Australia. The downscaling from seasonal ensemble predictions from coupled ocean\\/atmosphere general circulation models to daily precipitation time series for individual observing
Henrik Feddersen
Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts
NASA Astrophysics Data System (ADS)
Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.
Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève
A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China
NASA Astrophysics Data System (ADS)
This study evaluated the performance of three frequently applied statistical downscaling tools including SDSM, SVM, and LARS-WG, and their model-averaging ensembles under diverse moisture conditions with respect to the capability of reproducing the extremes as well as mean behaviors of precipitation. Daily observed precipitation and NCEP reanalysis data of 30 stations across China were collected for the period 1961-2000, and model parameters were calibrated for each season at individual site with 1961-1990 as the calibration period and 1991-2000 as the validation period. A flexible framework of multi-criteria model averaging was established in which model weights were optimized by the shuffled complex evolution algorithm. Model performance was compared for the optimal objective and nine more specific metrics. Results indicate that different downscaling methods can gain diverse usefulness and weakness in simulating various precipitation characteristics under different circumstances. SDSM showed more adaptability by acquiring better overall performance at a majority of the stations while LARS-WG revealed better accuracy in modeling most of the single metrics, especially extreme indices. SVM provided more usefulness under drier conditions, but it had less skill in capturing temporal patterns. Optimized model averaging, aiming at certain objective functions, can achieve a promising ensemble with increasing model complexity and computational cost. However, the variation of different methods' performances highlighted the tradeoff among different criteria, which compromised the ensemble forecast in terms of single metrics. As the superiority over single models cannot be guaranteed, model averaging technique should be used cautiously in precipitation downscaling.
Duan, Kai; Mei, Yadong
A robust framework for probabilistic precipitations downscaling from an ensemble of climate predictions applied to Switzerland
NASA Astrophysics Data System (ADS)
Rainfall is poorly modeled by general circulation models (GCMs) and requires appropriate downscaling for local-scale hydrological impact studies. Such downscaling methods should be robust and accurate (to handle, e.g., extreme events and uncertainties), but the noncontinuous and highly nonlinear nature of rainfall makes this task particularly challenging. This paper brings together and extends state-of-the-art methods into an integrated and robust probabilistic methodology to downscale local daily rainfall series from an ensemble of climate simulations. The downscaling is based on generalized linear models (GLMs) that relate monthly GCM-scale atmospheric variables to local-scale daily rainfall series. A cross-validation step ensures that the fitted models are correctly conditioned by the climate variables, and a statistical procedure is proposed to test whether the statistical relationships identified for the reference period also hold in a future perturbed climate (i.e., to test the stationarity assumption). Additionally, we propose a strategy to downweigh poorly performing GCM-GLM couples. The methodology is assessed at 27 locations covering Switzerland and is shown to perform well in reproducing historical rainfall statistics including extremes and interannual variability. Furthermore, the projections are consistent with the simulations of physically based dynamical models. Using an original visualization method based on heat maps, we show that although the downscaling models were fitted at each of the 27 sites independently, their projections follow a spatially coherent pattern and that regions exhibiting different climate change impacts can be identified.
Beuchat, X.; Schaefli, B.; Soutter, M.; Mermoud, A.
Downscaling a perturbed physics ensemble over the CORDEX Africa domain
NASA Astrophysics Data System (ADS)
We present here the methodology and the results of 5-member ensemble simulation of the climate of Africa for the period 1950-2100 using climate modelling system PRECIS over the CORDEX Africa domain. The boundary conditions for the regional model simulations were selected from a 17-member perturbed physics ensemble based on the HadCM3 global climate model (Murphy et al. 2007) following the methodology described in McSweeney et al 2012. Such an approach was selected in order to provide a good representation of the overall ensemble spread over a number of sub regions while at the same time avoiding members which have demonstrate particularly unrealistic characteristics in their baseline climate. In the simulations a special attention was given to the representation of some inland water bodies, such as lake Victoria, whose impact on the regional climate was believed to be significant thus allowing for the representation of some regional processes (e.g. land-lake breezes) that were not represented in the global models. In particular the SSTs of the lakes were corrected to better represent the local climatological values. The results suggest that RCM simulations improve the fit to observations of precipitation and temperature in most of the African sub-regions (e.g. North Africa, West Sahel). Also, the range of RCM projections is often different to those from the GCMs in these regions. We discuss the reasons for and links between these results and their implications for use in informing adaptation policy at regional level.
Buontempo, Carlo; Williams, Karina; McSweeney, Carol; Jones, Richard; Mathison, Camilla; Wang, Chang
Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series
NASA Astrophysics Data System (ADS)
This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.
Fatichi, S.; Ivanov, V. Y.; Caporali, E.
Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States
NASA Astrophysics Data System (ADS)
This study compares two approaches, dynamical and statistical downscaling, for their potential to improve regional seasonal forecasts for the United States (U.S.) during the cold season. In the MultiRCM Ensemble Downscaling (MRED) project, seven regional climate models (RCMs) are used to dynamically downscale the Climate Forecast System (CFS) seasonal prediction over the conterminous U.S. out to 5 months for the period of 1982-2003. The simulations cover December to April of next year with 10 ensemble members from each RCM with different initial and boundary conditions from the corresponding ensemble members. These dynamically downscaled forecasts are compared with statistically downscaled forecasts produced by two bias correction methods applied to both the CFS and RCM forecasts. Results of the comparison suggest that the RCMs add value in seasonal prediction application, but the improvements largely depend on location, forecast lead time, variables, and skill metrics used for evaluation. Generally, more improvements are found over the Northwest and North Central U.S. for the shorter lead times. The comparison results also suggest a hybrid forecast system that combines both dynamical and statistical downscaling methods have the potential to maximize prediction skill.
Yoon, Jin-Ho; Ruby Leung, L.; Correia, James, Jr.
Dynamical downscaling of snow trends in Nothern Iberia based on ENSEMBLES regional simulations
NASA Astrophysics Data System (ADS)
A recent study reported a significant decreasing trend of snow occurrence (-4.6 days/decade) in the Northern Iberian Peninsula since the mid seventies (Pons et al. 2009). This study was based on observations of annual snow frequency (measured as the annual number of snow days) from a network of 33 stations ranging from 60 to 1350 meters. In the present work we analyze the skill of dynamical downscaling methods to reproduce this trend in present climate conditions and also to further project it into the future from A1B-scenario global simulations. In particular, we consider the regional simulation dataset from the ENSEMBLES project, consisting in ten state-of-the-art Regional Climate Models (RCMs) at 25km resolution run with different forcing/boundary conditions. To this aim we first test the regional models with perfect boundaries considering ERA40; it is shown that after correcting the bias, all the RCMs appropriately reproduce the interannual variability and the observed trends (e.g., the ensemble mean presents a trend of -5.8 days/decade). Then we analyze the results for the present climate 20c3m-scenario global simulations. In this case, the results are quite variable with the larger uncertainty being associated with the particular GCM used (ECHAM5, CNRM or HadCM) with trend ranging from -6.7 to -1.8 days/decade. Finally, the trends obtained for the future 2010-2040 A1B runs ranged from -5.7 to -1.4 days/decade, indicating a continuous decreasing of snow frequency in this region. References: Pons, M.R., D. San-Martín, S. Herrera and J.M. Gutiérrez (2009), Snow Trends in Northern Spain. Analysis and simulation with statistical downscaling methods, International Journal of Climatology, DOI. 10.1002/joc.2016A recent study reported a significant decreasing trend of snow occurrence (-4.6 days/decade) in the Northern Iberian Peninsula since the mid seventies (Pons et al. 2009). This study was based on observations of annual snow frequency (measured as the annual number of snow days) from a network of 33 stations ranging from 60 to 1350 meters. In the present work we analyze the skill of dynamical downscaling methods to reproduce this trend in present climate conditions and also to further project it into the future from A1B-scenario global simulations. In particular, we consider the regional simulation dataset from the ENSEMBLES project, consisting in ten state-of-the-art Regional Climate Models (RCMs) at 25km resolution run with different forcing/boundary conditions. To this aim we first test the regional models with perfect boundaries considering ERA40; it is shown that after correcting the bias, all the RCMs appropriately reproduce the interannual variability and the observed trends (e.g., the ensemble mean presents a trend of -5.8 days/decade). Then we analyze the results for the present climate 20c3m-scenario global simulations. In this case, the results are quite variable with the larger uncertainty being associated with the particular GCM used (ECHAM5, CNRM or HadCM) with trends ranging from -6.7 to -1.8 days/decade. Finally, the trends obtained for the future 2010-2040 A1B runs range from -5.7 to -1.4 days/decade, indicating a continuous decrease of snow frequency in this region. References: Pons, M.R., D. San-Martín, S. Herrera and J.M. Gutiérrez (2009), Snow Trends in Northern Spain. Analysis and simulation with statistical downscaling methods, International Journal of Climatology, DOI. 10.1002/joc.2016
Herrera, S.; Pons, M. R.; Sordo, C. M.; Gutiérrez, J. M.
Precipitation Intensity in Downscaled Seasonal Forecasts
NASA Astrophysics Data System (ADS)
We examine precipitation intensity in downscaled seasonal forecasts over the continental United States produced using six regional climate models (RCMs). The RCMs downscaled winter seasonal forecasts from the global NCEP Climate Forecast System (CFS) version 1 from 1982-2003 as part of the Multi-RCM Ensemble Downscaling (MRED) project. We assess model performance for the January-February-March (JFM) and February-March-April (FMA) seasons over the Interior West, Southwest, Gulf Coast, and Ohio Valley regions. The latter three regions have well-known influences from El Niño-Southern Oscillation (ENSO). We examine model skill for the seasonal mean as well as daily precipitation frequency and intensity. The statistical gamma distribution was used to concisely summarize the precipitation intensity distribution through two fitted parameters, commonly termed the shape and scale parameters. Downscaling is found to improve the representation of winter precipitation intensity, especially in the higher part of the range. For the Southwest, Gulf Coast, and Ohio Valley, changes in the seasonal mean generally result from an increase or decrease in both the shape and scale parameters. This implies that changes in the mean are accompanied by consistent changes in the nature of the intensity distribution. In contrast, the shape and scale parameters vary inversely in the Interior West. We find significant inter-model spread in both the seasonal mean and the daily intensity distribution amongst the RCMs, despite the fact that they all use the same large-scale boundary conditions from the CFS. We show that models having the same skill in the mean can differ in their ability to predict observed precipitation intensity. The gamma distribution provides a compact approach for identifying such cases.
Arritt, R. W.; Ansorge, A.
Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble
NASA Astrophysics Data System (ADS)
Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung
Six-month lead downscaling prediction of winter-spring drought in South Korea based on multi-model ensemble
NASA Astrophysics Data System (ADS)
Given the changing climate, advance information on hydrological extremes such as droughts will help in planning for disaster mitigation and facilitate better decision making for water availability management. A deficit of precipitation for long-term time scales beyond 6 months has impacts on the hydrological sectors such as ground water, streamflow, and reservoir storage. The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center (APCC) 1-Tier multi-model ensemble (MME) was downscaled for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both of precipitation and temperature, and can incorporate the impact of global warming on the balance between precipitation and evapotranspiration. It was found that 1-Tier MME has difficulties in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the downscaled MME (DMME). In conjunction with variance inflation, DMME can give reasonably skillful six-month-lead forecasts of SPEI for the winter-to-spring period. The results could potentially improve hydrological extreme predictions using meteorological forecasts for policymaker and stakeholders in water management sector for better climate adaption.
Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung
Downscaling precipitation or bias-correcting streamflow? Some implications for coupled general circulation model (CGCM)-based ensemble seasonal hydrologic forecast
NASA Astrophysics Data System (ADS)
The progress in forecasting seasonal climate by using coupled atmosphere-ocean-land general circulation models (CGCMs) has increased the use of CGCM-based hydrologic forecasting in recent years. A common procedure is to downscale the meteorological forcings and use them as inputs to hydrologic models to provide ensemble forecasts. Less attention has been paid to bias correcting the hydrologic forecasts directly generated by CGCM. In this study, we show that either downscaling precipitation for hydrologic model or directly bias-correcting CGCM streamflow increases the efficiency skill score greatly as compared to the original CGCM streamflow forecast, and bias correcting the streamflow from hydrologic model with downscaled precipitation leads to a further skill increase. Bias-correcting CGCM streamflow is more skillful and reliable than downscaling precipitation for hydrologic modeling in terms of ensemble forecasts, as verified by the ranked probability skill score and the rank histogram. While bias-correcting streamflow from CGCM can provide useful forecasts, combining the downscaled CGCM forcings and bias-corrected hydrologic output through the CGCM-hydrology forecasting approach does gain additional skill of accuracy and discrimination.
Yuan, Xing; Wood, Eric F.
Simulation of SEU Cross-sections using MRED under Conditions of Limited Device Information
NASA Technical Reports Server (NTRS)
This viewgraph presentation reviews the simulation of Single Event Upset (SEU) cross sections using the membrane electrode assembly (MEA) resistance and electrode diffusion (MRED) tool using "Best guess" assumptions about the process and geometry, and direct ionization, low-energy beam test results. This work will also simulate SEU cross-sections including angular and high energy responses and compare the simulated results with beam test data for the validation of the model. Using MRED, we produced a reasonably accurate upset response model of a low-critical charge SRAM without detailed information about the circuit, device geometry, or fabrication process
Lauenstein, J. M.; Reed, R. A.; Weller, R. A.; Mendenhall, M. H.; Warren, K. M.; Pellish, J. A.; Schrimpf, R. D.; Sierawski, B. D.; Massengill, L. W.; Dodd, P. E.; Shaneyfelt, M. R.; Felix, J. A.; Schwank, J. R.
NSDL National Science Digital Library
Ensemble is a new NSF NSDL Pathways project working to establish a national, distributed digital library for computing education. Our project is building a distributed portal providing access to a broad range of existing educational resources for computing while preserving the collections and their associated curation processes. We want to encourage contribution, use, reuse, review and evaluation of educational materials at multiple levels of granularity and we seek to support the full range of computing education communities including computer science, computer engineering, software engineering, information science, information systems and information technology as well as other areas often called "computing + X" or 'X informatics."
New statistical downscaling for Canada
NASA Astrophysics Data System (ADS)
This poster will document the production of a set of statistically downscaled future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) downscaling method evaluation, (2) scenarios selection, (3) production of statistically downscaled results, and (4) applications of results. We build upon a previous downscaling evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of downscaling used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical downscaling was carried out on the NARCCAP ensemble and a subset of the CMIP5 ensemble. We produced downscaled scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have superior skill in interpolating RCM data over North America (McGinnis et al. 2012). An early application of the new dataset was to provide projections of climate extremes for adaptation planning by the British Columbia Ministry of Transportation and Infrastructure. Recently, certain stretches of highway have experienced extreme precipitation events resulting in substantial damage to infrastructure. As part of the planning process to refurbish or replace components of these highways, information about the magnitude and frequency of future extreme events are needed to inform the infrastructure design. The increased resolution provided by downscaling improves the representation of topographic features, particularly valley temperature and precipitation effects. A range of extreme values, from simple daily maxima and minima to complex multi-day and threshold-based climate indices were computed and analyzed from the downscaled output. Selected results from this process and how the projections of precipitation extremes are being used in the context of highway infrastructure planning in British Columbia will be presented.
Murdock, T. Q.; Cannon, A. J.; Sobie, S.
Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment
NASA Astrophysics Data System (ADS)
Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used. However, the downscaling results may contain considerable uncertainty which needs to be quantified before making the results available. Among the variables usually downscaled, precipitation downscaling is quite challenging and is more prone to uncertainty issues than other climatological variables. This paper addresses the uncertainty analysis associated with statistical downscaling of a watershed precipitation (Clutha River above Balclutha, New Zealand) using results from three well reputed downscaling methods and Bayesian weighted multi-model ensemble approach. The downscaling methods used for this study belong to the following downscaling categories; (1) Multiple linear regression; (2) Multiple non-linear regression; and (3) Stochastic weather generator. The results obtained in this study have shown that this ensemble strategy is very efficient in combining the results from multiple downscaling methods on the basis of their performance and quantifying the uncertainty contained in this ensemble output. This will encourage any future attempts on quantifying downscaling uncertainties using the multi-model ensemble framework.
Hashmi, M. Z.; Shamseldin, A. Y.; Melville, B. W.
An extreme comparison of two downscaling approaches using Bayes factors
NASA Astrophysics Data System (ADS)
Extreme rainfall events are the long-standing hydrological interest of flood defence and water resources management. Although traditional extreme value theory allows stationary extreme assessment, recent development of rainfall downscaling approaches driven by projections of Global Climate models (GCMs) facilitates non-stationary extreme assessments. Additionally, using stochastic downscaling, the downscaled rainfall series can be probabilistic so that the inherent uncertainty of the used approaches can be explicitly presented. However, there is little work on performance benchmarking of extremes simulated by alternative downscaling approaches. In the United Kingdom (UK), two independently developed downscaling methodologies are (1) the UK climate projections (UKCP09) weather generators and (2) the Generalised linear model (GLM) approach. Both downscaling approaches can provide daily rainfall series at catchment scale. As a quantitative benchmark, Bayes factors are proposed as a tool for comparing ensemble extremes generated from the two UK models. Using Monte Carlo Integration and Laplace's approximation, Bayes factors for the 30th largest annual event within a 30 year period of the two methods are approximated for six catchments in the UK. Despite their similar average monthly statistics (i.e. mean, variance, autocorrelation and skewness), results show that the preferred approach for extreme results is catchment specific. The implications and possible interpretations of diverse extreme results from different downscaling approaches are discussed.
Chun, K.; Wheater, H. S.; Onof, C. J.
Survey of statistical downscaling techniques.
National Technical Information Service (NTIS)
The derivation of regional information from integrations of coarse-resolution General Circulation Models (GCM) is generally referred to as downscaling. The most relevant statistical downscaling techniques are described here and some particular examples ar...
E. Zorita H. Storch
An application of hybrid downscaling model to forecast summer precipitation at stations in China
NASA Astrophysics Data System (ADS)
A pattern prediction hybrid downscaling method was applied to predict summer (June–July–August) precipitation at China 160 stations. The predicted precipitation from the downscaling scheme is available one month before. Four predictors were chosen to establish the hybrid downscaling scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid downscaling scheme (HD-4P) has better prediction skill than a conventional statistical downscaling model (SD-2P) which contains two predictors derived from the output of GCMs, although two downscaling schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P downscaling predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P downscaling model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid downscaling prediction should be effective to improve the prediction skill of summer rainfall at stations in China.
Liu, Ying; Fan, Ke
Downscaling of inundation extents
NASA Astrophysics Data System (ADS)
The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do downscale GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third downscaling method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the downscaling. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and downscaling of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012.
Aires, Filipe; Prigent, Catherine; Papa, Fabrice
Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe
NASA Astrophysics Data System (ADS)
Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods often used in climate change impact studies. Four methods are based on change factors, three are bias correction methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from fifteen regional climate models (RCMs) from the ENSEMBLES project for eleven catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the statistical downscaling methods vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between change factor and bias correction methods. The performance of the bias correction methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and statistical downscaling methods indicates that up to half of the total variance is derived from the statistical downscaling methods. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need of considering an ensemble of both statistical downscaling methods and climate models.
Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriau?i?nien?, J.; Loukas, A.; Osuch, M.; Yücel, I.
Downscaling of Precipitation over Vancouver Island using a Synoptic Typing Approach
A statistical downscaling technique is employed to link atmospheric circulation produced by an ensemble of global climate model (GCM) simulations over the twenty-first century to precipitation recorded at weather stations on Vancouver Island. Relationships between the different spatial scales are established with synoptic typing, coupled with non-homogeneous Markov models to simulate precipitation intensity and occurrence. Types are generated from daily
Stephen R. Sobie; Andrew J. Weaver
Downscaling of Precipitation over Vancouver Island using a Synoptic Typing Approach
A statistical downscaling technique is employed to link atmospheric circulation produced by an ensemble of global climate model (GCM) simulations over the twenty-first century to precipitation recorded at weather stations on Vancouver Island. Relationships between the different spatial scales are established with synoptic typing, coupled with non-homogeneous Markov models to simulate precipitation intensity and occurrence. Types are generated from daily
Stephen R. Sobie; Andrew J. Weaver
Multimodel output statistical downscaling prediction of precipitation in the Philippines and Thailand
Six dynamical seasonal model outputs, which are currently used in the APEC Climate Center Multimodel Ensemble (MME) prediction system, are employed for statistical downscaling prediction of station-scale precipitation in the Philippines and Thailand. Correlation analysis and Singular Value Decomposition Analysis are used to reveal atmosphere dynamic linkage based on the observed data other than model data. The observed linkage provides
Hongwen Kang; Kyong-Hee An; Chung-Kyu Park; Ana Liza S. Solis; Kornrawee Stitthichivapak
The Ensembl project (http://www.ensembl.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of Ensembl release 64 (September 2011). Of these, 55 species appear on the main Ensembl website and six species are provided on the Ensembl preview site (Pre!Ensembl; http://pre.ensembl.org) with preliminary support. The past year has also seen improvements across the project.
Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kahari, Andreas K.; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y. Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernandez-Suarez, Xose M.; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.
The Ensembl project (http://www.ensembl.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of Ensembl release 64 (September 2011). Of these, 55 species appear on the main Ensembl website and six species are provided on the Ensembl preview site (Pre!Ensembl; http://pre.ensembl.org) with preliminary support. The past year has also seen improvements across the project. PMID:22086963
Flicek, Paul; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kähäri, Andreas K; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R S; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P; Aken, Bronwen L; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernández-Suarez, Xosé M; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J P; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M J
regional statistical downscaling of wind
NASA Astrophysics Data System (ADS)
In a complex terrain, accurate and rapid determination of wind components is a challenge for applications like evaluation, forecast and future projections of wind hazards, wind energy and pollutant transport, among others. This work presents an extension of a statistical method used to downscale local wind components in a mountainous environment in southern France. Our approach is originally based on Generalized Additive Model (GAM) which relates large-scale atmospheric variables to local wind. In a previous work (Salameh et al. 2008), downscaling was performed at station location. Now, applying our model to specific types of regional circulations allows to downscale winds in regions void of any measurements. This new approach is evaluated by removing measurements obtained at station locations from a meteorological station network used to calibrate the model and evaluate the downscaled wind with the measurements excluded from the dataset. This comparison shows that our method has a considerable improvement and a reduction of the bias of about 10 times. We also assess the sensitivity of our model to initial conditions data type, by comparing the downscaling of ERA-40 and IPCC climate run between 1991 and 2001.
Salameh, T.; Vrac, M.; Drobinski, P.
Uncertainty analysis of statistical downscaling methods
Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance
Mohammad Sajjad Khan; Paulin Coulibaly; Yonas Dibike
Atmospheric Downscaling using Genetic Programming
NASA Astrophysics Data System (ADS)
Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and downscaling procedures. Subject of our work is the downscaling scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current downscaling scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for downscaling surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require downscaling, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for downscaling surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and optimization as genetic operators. Next we have worked on an improvement of the downscaling rule for the two-meter-temperature. We have added an if-function with four input arguments to the function set. Since this has shown to increase bloat we have additionally modified our fitness function by including penalty terms for both the size of the solutions and the number intron nodes, i.e program parts that are never evaluated. Starting from the known downscaling rule for the two-meter temperature, which linearly exploits the orography anomalies allowed or disallowed by a certain temperature gradient, our GP system has been able to find an improvement. The rule produced by the GP clearly shows a better performance concerning the reproduced small-scale variability.
Zerenner, Tanja; Venema, Victor; Simmer, Clemens
Statistical downscaling of river flows
NASA Astrophysics Data System (ADS)
SummaryAn extensive statistical 'downscaling' study is done to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in SW France for 51 gauging stations ranging from nival (snow-dominated) to pluvial (rainfall-dominated) river-systems. This study helps to select the appropriate statistical method at a given spatial and temporal scale to downscale hydrology for future climate change impact assessment of hydrological resources. The four proposed statistical downscaling models use large-scale predictors (derived from climate model outputs or reanalysis data) that characterize precipitation and evaporation processes in the hydrological cycle to estimate summary flow statistics. The four statistical models used are generalized linear (GLM) and additive (GAM) models, aggregated boosted trees (ABT) and multi-layer perceptron neural networks (ANN). These four models were each applied at two different spatial scales, namely at that of a single flow-gauging station (local downscaling) and that of a group of flow-gauging stations having the same hydrological behaviour (regional downscaling). For each statistical model and each spatial resolution, three temporal resolutions were considered, namely the daily mean flows, the summary statistics of fortnightly flows and a daily 'integrated approach'. The results show that flow sensitivity to atmospheric factors is significantly different between nival and pluvial hydrological systems which are mainly influenced, respectively, by shortwave solar radiations and atmospheric temperature. The non-linear models (i.e. GAM, ABT and ANN) performed better than the linear GLM when simulating fortnightly flow percentiles. The aggregated boosted trees method showed higher and less variable R2 values to downscale the hydrological variability in both nival and pluvial regimes. Based on GCM cnrm-cm3 and scenarios A2 and A1B, future relative changes of fortnightly median flows were projected based on the regional downscaling approach. The results suggest a global decrease of flow in both pluvial and nival regimes, especially in spring, summer and autumn, whatever the considered scenario. The discussion considers the performance of each statistical method for downscaling flow at different spatial and temporal scales as well as the relationship between atmospheric processes and flow variability.
Tisseuil, Clement; Vrac, Mathieu; Lek, Sovan; Wade, Andrew J.
Statistical Downscaling Considering Non-stationarities
NASA Astrophysics Data System (ADS)
The present study aims to introduce a novel downscaling approach which explicitly takes non-stationarities into account. For the illustration of this approach the Mediterranean area is chosen, because it shows a wide range of different climatic characteristics, from humid conditions in the western, northern and eastern Mediterranean regions in winter to arid conditions in the southern and eastern Mediterranean regions in summer. Precipitation in the Mediterranean area is assessed by using a combined circulation- and transfer-function-based approach. Daily station data for the Mediterranean area is used as local precipitation predictand. As large-scale predictors geopotential heights of the 700hPa level in the area 20°N-70°N, 70°W-50°E are selected to include large-scale atmospheric regimes showing inter-annual to decadal variability. To account for daily to inter-annual influences on precipitation 700hPa-geopotential heights are used, again, but now within the scope to obtain circulation patterns within station-specific predictor domains. Furthermore, 700hPa-relative humidity, zonal and meridional wind components of the 700hPa level and convective inhibition are included to describe within-type characteristics of the circulation patterns. At first the statistical models are established using the whole time period available for a particular station. Subsequently, 31-year sub-periods are used to detect non-stationarities in the predictors-predictand-relationships. As a measure of performance the bias and its confidence interval limits are used for error analysis of the distributional mean. The (non-)overlaps of the bootstrap confidence interval of the mean model performance (derived by averaging the performances of all calibration/verification periods) and the bootstrap confidence intervals of the individual model errors are used to identify (non-)stationary model performance. If non-stationarities are detected, the varying predictors-predictand-relationships are analysed for the underlying reasons and statistical model ensembles are built to capture the range of observed relationships. In case of the absence of non-stationarities the statistical downscaling approach follows a conventional split-sampling approach for verification. Finally the statistical models and model ensembles are used to predict mean daily precipitation in the Mediterranean area until the end of the 21st century under increased greenhouse warming conditions. This research project is funded by the German Research Foundation DFG.
Hertig, Elke; Jacobeit, Jucundus
Downscaling GCM-simulated precipitation for the last millennium
NASA Astrophysics Data System (ADS)
Climate variability in the pre-instrumental period can be estimated either from climate proxy data or from numerical simulations. Both approaches still have considerable uncertainties and consistency tests are crucial for identifying robust features. One of the problems when comparing simulations with proxy-based reconstructions are potential scale mismatches. If the proxy-based reconstructions represent regional climate a direct comparison with simulated variables from global climate models, which in palaeoclimate applications are run with coarse resolutions, can lead to misleading results for two reasons: (i) the climate model might be biased even on large spatial scales, and (ii) small-scale spatial variability cannot be represented by the climate model. This problem can be expected to be particularly relevant for precipitation because of its high spatial variability. One way of addressing this problem is by applying downscaling techniques to the simulations. We have applied a statistical downscaling and correction method to precipitation from a simulation for the last millennium with the MPI for Meteorology Earth System Model, which uses ECHAM5-T31 as the atmosphere component. Our downscaling method, which is based on model output statistics (MOS), has been shown to outperform more standard (so-called perfect-prog) statistical downscaling methods when applied to simulated precipitation from the second half of the twentieth century, but it has not yet been applied to palaeoclimate simulations. Our aim is two-fold: to assess (a) whether downscaling using MOS yields additional information about long-term changes in regional climate and (b) to what extent the downscaled simulations may be in greater agreement with proxy-based reconstructions than raw model output. Two MOS downscaling methods, based on local scaling and principal component regression, are calibrated 'event-wise' (i.e. between contemporaneous sequences of simulated and observed events) using precipitation from a simulation of ECHAM5 (nudged to ERA-40) and gridded observations. Both methods are then applied to simulated precipitation for the last millennium. Our findings show that, under cross-validation for the period 1958-2001, downscaling with MOS from the T31 resolution to a 0.5° x 0.5° target grid produces precipitation estimates that generally match the temporal variability of the observed record in large parts of Europe. MOS also shows good skill in estimating monthly precipitation amounts at small scales that are more realistic than raw model output. In comparison with a multi-proxy gridded reconstruction (Pauling et al., 2006) it is shown that reconstructed precipitation falls within the range of the downscaled ensemble spread in some parts of Europe. However, in many areas MOS fails to produce downscaled estimates that are in agreement with either the temporal evolution or magnitude indicated by the proxy record. Ultimately, this inconsistency limits the potential for such a comparison to be used as a validation tool except in individual cases.
Eden, Jonathan; Widmann, Martin; Smith, Richard
The Ensembl project (http:\\/\\/www.ensembl.org) is a comprehensive genome information system fea- turing an integrated set of genome annotation, data- bases, and other information for chordate, selected model organism and disease vector genomes. As of release 51 (November 2008), Ensembl fully supports 45 species, and three additional species have pre- liminary support. New species in the past year include orangutan and
Tim J. P. Hubbard; B. L. Aken; Sarah C. Ayling; Benoit Ballester; Kathryn Beal; E. Bragin; S. Brent; Yuan Chen; P. Clapham; Laura Clarke; G. Coates; S. Fairley; S. Fitzgerald; J. Fernandez-banet; L. Gordon; Stefan Gräf; Syed Haider; Martin Hammond; Richard C. G. Holland; Kevin L. Howe; Andrew M. Jenkinson; N. Johnson; Andreas Kähäri; Damian Keefe; S. Keenan; R. Kinsella; Felix Kokocinski; Eugene Kulesha; Daniel Lawson; I. Longden; Karine Megy; Patrick Meidl; B. Overduin; A. Parker; B. Pritchard; D. Rios; M. Schuster; Guy Slater; Damian Smedley; William Spooner; G. Spudich; S. Trevanion; Albert J. Vilella; J. Vogel; S. White; S. Wilder; Arek Zadissa; Ewan Birney; Fiona Cunningham; Val Curwen; Richard Durbin; X. M. Fernandez-suarez; Javier Herrero; Arek Kasprzyk; Glenn Proctor; James Smith; Stephen M. J. Searle; Paul Flicek
Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa
NASA Astrophysics Data System (ADS)
The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.
Roberts, J. B.; Robertson, F. R.; Bosilovich, M. G.; Lyon, B.
Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa
NASA Technical Reports Server (NTRS)
The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.
Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield
Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa
NASA Technical Reports Server (NTRS)
The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period
Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris
A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms
NASA Astrophysics Data System (ADS)
The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical downscaling approach in combination with dynamical downscaling is introduced to derive storm related gust speeds on a high-resolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical downscaling. This new tool can be easily applied to large ensembles of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections.
Haas, R.; Pinto, J. G.
The Ensembl project (http://www.ensembl.org) provides genome information for sequenced chordate genomes with a particular focus on human, mouse, zebrafish and rat. Our resources include evidenced-based gene sets for all supported species; large-scale whole genome multiple species alignments across vertebrates and clade-specific alignments for eutherian mammals, primates, birds and fish; variation data resources for 17 species and regulation annotations based on ENCODE and other data sets. Ensembl data are accessible through the genome browser at http://www.ensembl.org and through other tools and programmatic interfaces.
Flicek, Paul; Ahmed, Ikhlak; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Garcia-Giron, Carlos; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Juettemann, Thomas; Kahari, Andreas K.; Keenan, Stephen; Komorowska, Monika; Kulesha, Eugene; Longden, Ian; Maurel, Thomas; McLaren, William M.; Muffato, Matthieu; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sheppard, Daniel; Sobral, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen; White, Simon; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Johnson, Nathan; Kinsella, Rhoda; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.
NASA Astrophysics Data System (ADS)
Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall "ensemble" decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of "votes" (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been proposed to explain the characteristics and the successful application of ensembles to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of ensembles of learning machines in the framework of large margin classifiers [4,177], Kleinberg in the context of stochastic discrimination theory [112], and Breiman and Friedman in the light of the bias-variance analysis borrowed from classical statistics [21,70]. Empirical studies showed that both in classification and regression problems, ensembles improve on single learning machines, and moreover large experimental studies compared the effectiveness of different ensemble methods on benchmark data sets [10,11,49,188]. The interest in this research area is motivated also by the availability of very fast computers and networks of workstations at a relatively low cost that allow the implementation and the experimentation of complex ensemble methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use ensembles of learning machines, motivated by the intrinsic characteristics of the ensemble methods. The main aim of this chapter is to introduce ensemble methods and to provide an overview and a bibliography of the main areas of research, without pretending to be exhaustive or to explain the detailed characteristics of each ensemble method. The paper is organized as follows. In the next section, the main theoretical and practical reasons for combining multiple learners are introduced. Section 26.3 depicts the main taxonomies on ensemble methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised ensemble methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of ensemble methods are only marginally considered, but a specific section on some relevant applications of ensemble methods in astronomy and astrophysics has been added (Section 26.6). The conclusion (Section 26.7) ends this pap
Re, Matteo; Valentini, Giorgio
A new Multi-Scale Data Assimilation Algorithm to Downscale Satellite-Based Soil Moisture Data
NASA Astrophysics Data System (ADS)
The study focuses on downscaling of soil moisture from coarse remote sensing footprints to finer scales. Two approaches are proposed for soil moisture downscaling. The first approach provides the probability distribution functions at the finer scales with no information about the spatial organization of soil moisture fields. The second approach implements a multiscale ensemble Kalman filter (EnKF) that assimilates remotely sensed coarse scale soil moisture footprint, attributes of fine scale geophysical parameters/variables (i.e., soil texture: %sand, vegetation: NDVI, topography: slope, and precipitation) and coarse/fine scale simulation into a spatial characterization of soil moisture evolution at the finer scales. To downscale the remotely sensed coarse scale soil moisture to another spatial scale, the multiscale EnKF uses a bridging model. The bridging model infers the pixel-specific scaling coefficient from the compatible geophysical parameters/variables that influence the soil moisture evolution process at that particular spatial scale. Data from diverse hydroclimatic regions from the semiarid Arizona, the agricultural landscape of Iowa, and the grassland/rangeland of Oklahoma are used in the study to implement the multiscale downscaling algorithm. The results demonstrate that the bridging model of multiscale EnKF helps to characterize the evolution of soil moisture within the remotely sensed footprint. Validation conducted at the finest scale also shows reasonable agreement between the measured field data and the simulated downscaled soil moisture evolution.
Das, N. N.; Mohanty, B. P.; Efendiev, Y.
The Requirement for Pneumococcal MreC and MreD Is Relieved by Inactivation of the Gene Encoding PBP1a ?†
MreC and MreD, along with the actin homologue MreB, are required to maintain the shape of rod-shaped bacteria. The depletion of MreCD in rod-shaped bacteria leads to the formation of spherical cells and the accumulation of suppressor mutations. Ovococcus bacteria, such as Streptococcus pneumoniae, lack MreB homologues, and the functions of the S. pneumoniae MreCD (MreCDSpn) proteins are unknown. mreCD are located upstream from the pcsB cell division gene in most Streptococcus species, but we found that mreCD and pcsB are transcribed independently. Similarly to rod-shaped bacteria, we show that mreCD are essential in the virulent serotype 2 D39 strain of S. pneumoniae, and the depletion of MreCD results in cell rounding and lysis. In contrast, laboratory strain R6 contains suppressors that allow the growth of ?mreCD mutants, and bypass suppressors accumulate in D39 ?mreCD mutants. One class of suppressors eliminates the function of class A penicillin binding protein 1a (PBP1a). Unencapsulated ?pbp1a D39 mutants have smaller diameters than their pbp1a+ parent or ?pbp2a and ?pbp1b mutants, which lack other class A PBPs and do not show the suppression of ?mreCD mutations. Suppressed ?mreCD ?pbp1a double mutants form aberrantly shaped cells, some with misplaced peptidoglycan (PG) biosynthesis compared to that of single ?pbp1a mutants. Quantitative Western blotting showed that MreCSpn is abundant (?8,500 dimers per cell), and immunofluorescent microscopy (IFM) located MreCDSpn to the equators and septa of dividing cells, similarly to the PBPs and PG pentapeptides indicative of PG synthesis. These combined results are consistent with a model in which MreCDSpn direct peripheral PG synthesis and control PBP1a localization or activity.
Land, Adrian D.; Winkler, Malcolm E.
A Stochastic Technique for Error Correction and Spatial Downscaling of Global Gridded Precipitation Products
NASA Astrophysics Data System (ADS)
Deriving flood maps requires an accurate characterization of precipitation variability at high spatio-temporal resolution. Most of the available global-scale gridded precipitation products are available at resolutions (e.g., 25 km) not directly applicable to flood modeling. An error correction and spatial downscaling method based on a two-dimensional satellite rainfall error model (SREM2D) is tested in this study based on a long-term (2001-2010) dataset. Specifically, the model is applied on two rainfall datasets: a satellite precipitation product (TRMM-3B42.V7 at 0.25 degree) and a global land-atmosphere re-analysis product (GLDAS-CLM at 1 degree), to produce error corrected rainfall ensembles at 0.05 degree spatial resolution. The NCEP hourly, 4-km resolution multi-sensor precipitation product (WSR-88D stage IV gauge-adjusted radar-rainfall product) is used as the reference rainfall dataset. The Hillslope River Routing (HRR) hydrologic model is forced with the downscaled ensemble rainfall data to produce an ensemble of runoff values. The Susquehanna River basin is the study area, consisting of 1000 sub-basins ranging from 39 to 67,000 square kilometers including complex terrain and high latitude locations. There are 437 significant storm events selected over the study area based on the 10-year database. The analysis performed is based on 60 percent of events in each season kept for model calibration and 40 percent for validation. The statistical analysis consists of two parts: (1) evaluation of error metrics (relative standard deviation and efficiency coefficient) to quantify improvements in rainfall and runoff simulations as function of basin size and storm severity, and (2) ensemble verification (exceedance probability and mean uncertainty ratio) of the rainfall and runoff ensembles to assess the accuracy of the ensemble-based uncertainty characterization. The study investigates how well the ensemble of downscaled and error-corrected rainfall data performs relative to reference radar-rainfall data in terms of hydrologic simulations. The results will demonstrate the basin scale dependence of downscaled precipitation ensemble as well as the effect of seasonality on the method's performance.
Seyyedi, H.; Kaheil, Y.; Anagnostou, E. N.; McCollum, J.; Beighley, E.
Automated regression-based statistical downscaling tool
Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach inspired by the SDSM method
Masoud Hessami; Philippe Gachon; Taha B. M. J. Ouarda; André St-hilaire
User's Manual for Downscaler Fusion Software
Recently, a series of 3 papers has been published in the statistical literature that details the use of downscaling to obtain more accurate and precise predictions of air pollution across the conterminous U.S. This downscaling approach combines CMAQ gridded numerical model output...
Probabilistic Networks For Statistical Downscaling and Spatialisation of Meteorological Data
NASA Astrophysics Data System (ADS)
In this work we deal with the estimation of high-resolution meteorological informa- tion needed as input for agrometeorological models in a seasonal forecasting frame. To this aim, a statistical downscaling method based on probabilistic Networks (Bayesian networks) is introduced to connect the information generated by the seasonal fore- casting model (DEMETER) with the high-resolution historical information available in the Spanish institute of meteorology INM's stations network (which contains soil observations of ETP, precipitation, etc.). In the training phase we use ERA15/INM data to get a graphical model (a directed acyclic graph) which establishes the dependence structure and the statistical parame- ters that links the low-res ERA atmospheric patterns with the high-res soil INM vari- ables into a consistent and tractable probabilistic framework. The probabilistic model contains both the spatial dependencies among the stations and the local dependencies between the stations and the gridded ERA patterns. Once the model is ready, it can be used to spatialise meteorological information in two ways: 1. DEMETER outputs (seasonal ensemble predictions) are plugged as evidence in the network, obtaining the corresponding conditional probabilities of the soil variables used in agrometeoro- logical models. 2. Low-resolution information of soil variables (a reduced number of stations) can be also plugged as evidence, obtaining the high-resolution probabilities of the remaining stations. The main advantage of this method is that it gathers all the available statistical infor- mation for downscaling purposes into a single probabilistic model which contains the true linear and nonlinear dependencies among the variables given by the data. Note the simple downscaling analog method is a particular case of this methodology where an ad-hoc dependency structure is imposed to the variables (this may lead to spatial and physical inconsistencies in the obtained results).
Cano, R.; Cofiño, A. S.; Gutiérrez, J. M.; Sordo, C.
Statistical downscaling using K-nearest neighbors
Statistical downscaling provides a technique for deriving local-scale information of precipitation and temperature from numerical weather prediction model output. The K-nearest neighbor (K-nn) is a new analog-type approach that is used in this paper to downscale the National Centers for Environmental Prediction 1998 medium-range forecast model output. The K-nn algorithm queries days similar to a given feature vector in this
Subhrendu Gangopadhyay; Martyn Clark; Balaji Rajagopalan
Downscaling daily extreme temperatures with genetic programming
NASA Astrophysics Data System (ADS)
A context-free genetic programming (GP) method is presented that simulated local scale daily extreme (maximum and minimum) temperatures based on large scale atmospheric variables. The method evolves simple and optimal models for downscaling daily temperature at a station. The advantage of the context-free GP method is that both the variables and constants of the candidate models are optimized and consequently the selection of the optimal model. The method is applied to the Chute-du-Diable weather station in Northeastern Canada along with the National Center for Environmental Prediction (NCEP) reanalysis datasets. The performance of the GP based downscaling models is compared to benchmarks from a commonly used statistical downscaling model. The experiment results show that the models evolved by the GP are simpler and more efficient for downscaling daily extreme temperature than the common statistical method. The different model test results indicate that the GP approach significantly outperforms the statistical method for the downscaling of daily minimum temperature, while for the maximum temperature the two methods are almost equivalent. However, the GP method remains slightly more effective for maximum temperature downscaling than the statistical method.
Coulibaly, Paulin
Statistical downscaling based on dynamically downscaled predictors: Application to monthly precipitation in Sweden
NASA Astrophysics Data System (ADS)
A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical downscaling. The method uses predictors that are upscaled from a dynamical downscaling instead of predictors taken directly from a GCM simulation. The method is applied to downscaling of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the downscaled precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of downscaled precipitation. Due to the cost of the method and the limited improvements in the downscaling results, the three-step method is not justified to replace the one-step method for downscaling of Swedish precipitation.
Hellström, Cecilia; Chen, Deliang
Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale
NASA Astrophysics Data System (ADS)
The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.
Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob
Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin\\/Puget Sound in an Ensemble Regional Climate Simulation for the Mid-Century
This study examines an ensemble of climate change projections simulated by a global climate model (GCM) and downscaled with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three ensemble future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from
L. Ruby Leung; Yun Qian
Statistical Downscaling for Hydroclimate Applications (Invited)
NASA Astrophysics Data System (ADS)
Statistical downscaling and temporal disaggregation methods have been indispensable tools for creating regional climate projections from coarse-scale monthly-mean global climate model output, suitable for climate change assessment. Among other methods, the Bias Correction and Spatial Downscaling (BCSD) method has been used extensively in applications over the western United States, especially in developing hydroclimatic scenarios in combination with distributed hydrologic models. However, as climate impacts applications have expanded to address diverse stakeholder planning needs (terrestrial and aquatic ecosystems, water management, human health, energy, etc.), data are required at finer temporal and spatial scales. These advancements have demanded ongoing development of BCSD-based downscaling methods. This talk will describe recent projects to develop future hydroclimatic scenarios for large river basins of the western United States (Columbia, Upper Missouri, Colorado) and discuss the interaction between downscaling methodology, stakeholder planning, and application requirements. This talk will address methods for increasing spatial resolution and for disaggregating monthly-mean model output to daily time step data. Particular emphasis will be placed on downscaling considerations to properly simulated extreme statistics for both high and low flow conditions and on producing consistent results applicable across scales from small watersheds to major river basins.
Salathe, E. P.
Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling
NASA Astrophysics Data System (ADS)
Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is established between the principal components and observed rainfall over training period and predictions are obtained for testing period. The validations show high improvements in correlation coefficient between observed and predicted data (0.25 to 0.55). The results speak in favour of statistical downscaling methodology which shows the capability to reduce the gap between observed data and predictions. A detailed study is required to be carried out by applying different downscaling techniques to quantify the improvements in predictions.
Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal
Can Quantile Mapping be Used for Downscaling?
NASA Astrophysics Data System (ADS)
Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. But if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here I show for daily precipitation, that such quantile mapping based downscaling is not feasible but introduces similar problems as inflation of perfect prog downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is over-corrected, area mean extremes are over-estimated and trends are affected. To overcome these problems, stochastic bias correction is required. D Maraun, Bias Correction, Quantile Mapping and Downscaling. Revisiting the Inflation Issue. J Climate, in press, 2013
Maraun, Douglas
Hydrological responses to dynamically and statistically downscaled climate model output
Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction\\/ National Center for Atmospheric Research (NCEP\\/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic simulations of basin scale
Robert L. Wilby; Lauren E. Hay; William J. Gutowski Jr.; Raymond W. Arritt; Eugene S. Takle; Zaitao Pan; George H. Leavesley; Martyn P. Clark
Research and operational applications in multi-center ensemble forecasting
NASA Astrophysics Data System (ADS)
The North American Ensemble Forecast System (NAEFS) was built up in 2004 by the Meteorological Service of Canada (MSC), the National Meteorological Service of Mexico (NMSM), and the US National Weather Service (NWS) as an operational multi-center ensemble forecast system. Currently it combines the 20-member MSC and NWS ensembles to form a joint ensemble of 40 members twice a day. The joint ensemble forecast, after bias correction and statistical downscaling, is used to generate a suite of products for CONUS, North America and for other regions of the globe. The THORPEX Interactive Grand Global Ensemble (TIGGE) project has been established a few years ago to collect operational global ensemble forecasts from world centers, and distribute to the scientific community, to encourage research leading to the acceleration of improvements in the skill and utility of high impact weather forecasts. TIGGE research is expected to advise the development of the operational NAEFS system and eventually the two projects are expected to converge into a single operational system, the Global Interactive Forecast System (GIFS). This presentation will review recent developments, the current status, and plans related to the TIGGE research and NAEFS operational multi-center ensemble projects.
Zhu, Y.; Toth, Z.
Precipitation Downscaling Products for Hydrologic Applications (Invited)
NASA Astrophysics Data System (ADS)
Hydrologists and engineers require climate data on high-resolution grids (4-12km) for many water resources applications. To get such data from climate models, users have traditionally relied on statistical downscaling techniques, with only limited use of dynamic downscaling techniques. Statistical techniques utilize a variety of assumptions, data, and methodologies that result in statistical artifacts that may impact hydroclimate representations. These impacts are often pronounced when downscaling precipitation. We will discuss four major statistical downscaling techniques: Bias Corrected Constructed Analogue (BCCA), Asynchronous Regression (AR), and two forms of Bias Corrected Spatial Disaggregation (BCSD.) The hydroclimate representations within many statistical methods often have too much drizzle, too small extreme events, and an improper representation of spatial scaling characteristics. These scaling problems lead some statistical methods substantially over estimate extreme events at hydrologically important scales (e.g., basin totals.) This can lead to large errors in future hydrologic predictions. In contrast, high-resolution dynamic downscaling using the Weather Research and Forecasting model (WRF) provides a better representation of precipitation in many respects, but at a much higher computational cost. This computational constraint prevents the use of high-resolution WRF simulations when examining the range of possible future scenarios generated as part of the Coupled Model Intercomparison Project (CMIP.) Finally, we will present a next generation psuedo-dynamical model that provides dynamic downscaling information for a fraction of the computational requirements. This simple weather model uses large scale circulation patterns from a GCM, for example wind, temperature and humidity, but performs advection and microphysical calculations on a high-resolution grid, thus permitting topography to be adequately represented. This model is capable of generating changes in spatial patterns of precipitation related to atmospheric processes in a future climate. The pseudo-dynamical model may provide both the opportunity to better represent precipitation as well as being efficient in application to utilize a range of potential futures in a manner that would support water resources planning and management in the future.
Gutmann, E. D.; Pruitt, T.; Liu, C.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Rasmussen, R.
Downscaling Climate Data from Distributed Archives
NASA Astrophysics Data System (ADS)
Model refinement -- numerical estimates of climate change at higher resolution than climate models are currently capable of producing -- is an essential weapon in the arsenal of decision makers and researchers in climate change. We describe here steps toward a general-purpose system for model refinement. We envision a system wherein multiple climate models, alone or in combination, can be used as predictors; multiple refinement methods, alone or in combination, can be deployed and trained, including evaluation within a perfect-model framework, described below; time periods and locations of training can be chosen at will; and providing all of these options as standard web services within the Earth System Grid Federation (ESGF) global data infrastructure for the distribution of climate model output. The perfect-model framework for systematic testing of model refinement using empirical-statistical downscaling (ESD) schemes is being developed at NOAA/GFDL under the National Climate Predictions and Projections Platform (NCPP) project. It uses the approach that Laprise and collaborators call the "big-brother" framework for evaluating dynamical downscaling. High-resolution model output is used as a "nature run" and used in place of observations to train the ESD scheme under testing. The data is interpolated to a coarse grid (the "little brother") and the ESD scheme attempts to downscale and bias-correct the "future", i.e beyond the period of training. The output of ESD can then be rigorously compared to the original nature run on a chosen list of metrics. Initial work was performed in collaboration with Texas Tech University: the high-resolution time-slice models that GFDL submitted to CMIP5 are used as training sets for the downscaling methods developed by Katharine Hayhoe and collaborators. The approach is being extended to using other downscaling schemes, such as BCSD, Delta, quantile mapping, constructed analogs, and machine learning algorithms; and in future to using other model output for training datasets as well. Initial results were first presented at the Quantitative Evaluation of Downscaling 2013 Workshop (QED-2013). We will describe a software infrastructure wherein: a) any CMIP5 high-resolution model output can be used as a training set; b) any ESD scheme can be deployed using a standard template or API developed under the ExArch project; c) the outputs of downscaling will also conform to CMIP5 standards and be capable of being analyzed on the same footing as any CMIP5 output; d) analysis services computing the chosen metrics can be run on the downscaled output; e) the infrastructure can be deployed "in-house" by the ESD group, or potentially run as a web service on any ESGF node.
Radhakrishnan, A.; Guentchev, G.; Cinquini, L.; Schweitzer, R.; Nikonov, S.; Balaji, V.
Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model
NASA Astrophysics Data System (ADS)
We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June-July-August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.
Ying, Liu; Ke, Fan
Assessing the performance of dynamical and statistical downscaling techniques to simulate crop yield in West Africa
NASA Astrophysics Data System (ADS)
Global circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. It therefore requires the use of downscaling methods. This study analyzes the performance of both dynamical and statistical downscaling techniques in simulating crop yield at local scale. A detailed case study is conducted using historical weather data for Senegal, applied to the crop model SARRAH for simulating several tropical cereals (sorghum, millet, maize) at local scale. This control simulation is used as a benchmark to evaluate a set of Regional Climate Models (RCM) simulations, forced by ERA-Interim, from the ENSEMBLES project and a statistical downscaling method, the CDF-Transform, used to correct biases in RCM outputs. We first evaluate each climate variable that drives the simulated yield in the control simulation (radiation, rainfall, temperatures). We then simulate crop yields with RCM outputs (with or without applying the CDG-Transform) and evaluate the performance of each RCM in regards to crop yield simulations.
Sultan, B.; Oettli, P.; Vrac, M.; Baron, C.
Ensemble forecast post-processing over Belgium: Comparison of deterministic-like and ensemble regression methods
NASA Astrophysics Data System (ADS)
A comparison of the benefits of post-processing ECMWF ensemble forecasts based on a deterministic-like and a regression technique is performed for Belgium. The former is a Linear Model Output Statistics technique (EVMOS) recently developed to allow for providing an appropriate ensemble variability at all lead times (Vannitsem 2009) and the latter is the Non-homogeneous Gaussian Regression, NGR, (Gneiting et al, 2005). The training of the post-processing techniques is based on the reforecast dataset of ECMWF which covers a period from 1991 to 2007. The EVMOS approach is mainly providing a correction of the systematic error and does not enhance substantially the spread of the ensemble. The application of the NGR method provides an ensemble which encompasses the observations, unlike the EVMOS scheme. However, by taking into account the observational error, the analysis suggests that the ensemble based on the EVMOS post-processing scheme is also found to be consistent. This apparent contradiction is clarified and it turns out that both schemes are valuable depending on the specific purpose, the evaluation of the uncertainty of large scale flows or the downscaling of the temperature uncertainty at the level of the local observations.
Vannitsem, Stéphane; Hagedorn, Renate
Statistical downscaling based on dynamically downscaled predictors: Application to monthly precipitation in Sweden
A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation\\u000a Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining\\u000a the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical\\u000a downscaling. The method uses predictors that
Cecilia Hellström; Deliang Chen
Development of a European Multimodel Ensemble System for Seasonal-To Prediction (demeter).
NASA Astrophysics Data System (ADS)
A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole.
Palmer, T. N.; Alessandri, A.; Andersen, U.; Cantelaube, P.; Davey, M.; Délécluse, P.; Déqué, M.
Statistical downscaling of GCM simulations to Streamflow
NASA Astrophysics Data System (ADS)
A multi-tiered forecast procedure is employed to simulate real-time operational seasonal forecasts of categorized (below-normal, near-normal and above-normal) streamflow at the inlets of twelve dams of the Vaal and upper Tugela river catchments in South Africa. Forecasts are made for the December to February (DJF) season over an 8-year independent period from 1987/1988 to 1994/1995. A physically based model of the atmosphere system, known as a general circulation model (GCM), is used to simulate atmospheric variability over southern Africa, the output of which is statistically downscaled to streamflow. The GCM used is the COLA T30, and is forced at the boundary with predicted monthly-mean global sea-surface temperatures. The monthly-mean sea-surface temperature fields are first predicted over lead-times of several months using a canonical correlation analysis (CCA) model. GCM simulations are then obtained for an area including most of southern Africa and adjacent oceans. The GCM simulations are downscaled to catchment level from coarse resolution gridded climate variables, using a perfect prognosis approach: bias-corrected GCM simulations are substituted into the perfect prognosis equations to provide the downscaled categorized streamflow forecasts. Although surface characteristics of each catchment that affect the variability of streamflow are not considered in the proposed downscaling system, successful forecasts of streamflow categories were obtained for some of the years forecast independently. The scheme's operational utility is thus demonstrated, albeit over short lead-times.
Landman, Willem A.; Mason, Simon J.; Tyson, Peter D.; Tennant, Warren J.
Testing statistical downscaling methods in simulated climates
The consistency of two statistical downscaling methods and two different predictors to estimate past (last millennium) and future (21st century) precipitation in the Iberian and Scandinavian Peninsulas is assessed in the surrogate climate of a coupled climate model simulation. The methods are based on canonical correlation analysis and the search for analogs, with sea level pressure (SLP) and 500 mb
M. D. Frías; E. Zorita; J. Fernández; C. Rodríguez-Puebla
Downscaling GCM Output with Genetic Programming Model
NASA Astrophysics Data System (ADS)
Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.
Shi, X.; Dibike, Y. B.; Coulibaly, P.
Downscaling Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing
NASA Technical Reports Server (NTRS)
Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation ensembles is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional Downscaling Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each downscaling system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each downscaling system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the downscaling, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.
Druyan, Leonard M.; Fulakeza, Matthew B.
World Music Ensemble: Kulintang
ERIC Educational Resources Information Center
As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…
Beegle, Amy C.
Simulation of an ensemble of future climate time series with an hourly weather generator
NASA Astrophysics Data System (ADS)
There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).
Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.
Evaluation of the applicability in the future climate of a statistical downscaling method in France
NASA Astrophysics Data System (ADS)
The uncertainties in climate projections during the next decades generally remain large, with an important contribution of internal climate variability. To quantify and capture the impact of those uncertainties in impact projections, multi-model and multi-member approaches are essential. Statistical downscaling (SD) methods are computationally inexpensive allowing for large ensemble approaches. The main weakness of SD is that it relies on a stationarity hypothesis, namely that the statistical relation established in the present climate remains valid in the climate change context. In this study, the evaluation of SD methods developed for a future study of hydrological changes during the next decades over France is presented, focusing on precipitation. The SD methods are all based on the analogs method which is quite simple to set up and permits to easily test different combinations of predictors, the only changing parameter in the methods discussed in this presentation. The basic idea of the analogs method is that for a same large scale climatic state, the state of local variables will be identical. In a climate change context, the statistical relation established on past climate is assumed to remain valid in the future climate. In practice, this stationarity assumption is impossible to verify until the future climate is effectively observed. It is possible to evaluate the ability of SD methods to reproduce the interannual variability in the present climate, but this approach does not guarantee their validity in the future climate as the mechanisms that play in the interannual and climate change contexts may not be identical. Another common approach is to test whether a SD method is able to reproduce observed, as they may be partly caused by climate changes. The observed trends in precipitation are compared to those obtained by downscaling 4 different atmospheric reanalyses with analogs methods. The uncertainties in downscaled trends due to renalyses are very large compared to the magnitude of observed trends. Moreover some spurious trends in downscaled precipitation associated with temporal inconsistencies in reanalyses variables as surface humidity are noted. It is therefore difficult to assess the applicability of the downscaling methods in the future climate and their respective skill based on trends. Because of those difficulties, a perfect model approach is developed. In the surrogate world of a regional climate model (RCM), the statistical downscaling relation is established in its present climate and then applied to downscale its future projection. It is finally possible to compare future climate change simulated by the RCM and the result of the SD to test the stationarity hypothesis. To obtain robust results, the perfect model framework is applied to 12 RCMs from the ENSEMBLES project. Several analogs methods using different combination of predictors are tested. Some methods, very skillful for present-day interannual variability, are unable to reproduce correctly changes simulated by the RCMs. Another method with similar skill in the present climate, which only differs by the inclusion of the specific humidity at 850 hPa as predictor, is generally applicable in the future climate.
Dayon, G.; Boé, J.; Martin, E.
Multifractal downscaling of a GCM rainfield
NASA Astrophysics Data System (ADS)
In order to get a more efficient production management of reservoirs, it would be helpful to apply long-term meteorological forecasts to hydrological models. Unfortunately, the explicit scales of present GCM's are quite larger (e.g. 243kmx243kmx32 days) than those of hydrological models (e.g. 1 kmx1kmx1day). Therefore it is indispensable to proceed to a downscaling of the output of the former in order to obtain an input for the latter. In this paper, we present a multifractal downscaling procedure. The site of the study is the area of Doubs river, with the help of a dense local hydrological network, but in order to get a larger spatial scale ratio we extend our multifractal analysis to France, with the help of Météo-France PRECIP data base. We first argue that it is indispensable to consider a multifractal downscaling procedure in order to respect the scaling properties of the hydro-meteorological fields. We performed time, scale and time-space multifractal analysis of the available data and evaluate the corresponding universal exponents, as well as the anisotropy/dynamical exponent of the time-space generalized scale. We show that these exponents are quite robust. We compare our analysis to similar works, but restricted to the use of Log-Poison cascade and space-time isotropy. We show both theoretically and empirically that these restrictions are untenable, in particular with respect to the extremes. We also show simulations should be done with the help of continuous (in scale) and causal cascade models, not with ad-hoc time-space cascades, and present the corresponding numerical simulations. of space-time downscaling of (meso-scale) GCM data down to (micro-scale) hydrological scales. We greatly acknowledge the financial support from Electricité de France, as well as Météo-France for providing access to its PRECIP data base.
Biaou, A.; Hubert, P.; Schertzer, D.; Hendrickx, F.; Tchiguirinskaia, I.
Statistical-dynamical downscaling of wind climatologies
A statistical-dynamical downscaling procedure is applied for an investigation into the availability of wind power over a region of 80 × 87 km which covers flat and hilly terrain. The approach is based on the statistical coupling of a regionally representative wind climate with a numerical atmospheric mesoscale model. The large-scale wind climatology is calculated by a cluster-analysis of a
Heinz-Theo Mengelkamp; Hartmut Kapitza; Ulrich Pflüger
Downscaling of solid propellant pyrotechnical microsystems
This paper reports on the downscaling of solid propellant pyrotechnical microsystems. Sub-millimeter scale micro-thrusters were fabricated in silicon to investigate the limit of integration of this technology. The dimensions of the micro-thrusters chamber and of the igniter were reduced and a highly energetic solid-state propellant was used, the ZPP. Deep reactive ion etching (DRIE) of silicon was used to fabricate
P. Q. Pham; D. Briand; C. Rossi; N. F. de Rooij
Statistical downscaling of precipitation using quantile regression
NASA Astrophysics Data System (ADS)
SummaryStatistical downscaling of precipitation is required as part of many climate change studies. Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation. In this paper, we present a new technique for downscaling precipitation. The proposed method employs quantile regression rather than traditional linear regression models to determine the conditional distribution for a given day. This eliminates the need for some of the assumptions required in standard linear regression, including the assumption of normally-distributed errors with constant variance. The quantile regression model also allows considerable flexibility in selecting predictor variables in that different subsets of predictors can be used for different parts of the conditional distribution. A Bayesian method adapted to quantile regression is used to select predictor variables. The method is illustrated through an application to five weather stations in Canada. It is found that the proposed method has distinct advantages over the conventional regression model for predicting summer precipitation, while for winter precipitation there is not much difference between the two methods.
Tareghian, Reza; Rasmussen, Peter F.
Statistical-dynamical downscaling for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections
NASA Astrophysics Data System (ADS)
A statistical-dynamical downscaling (SDD) approach for the regionalisation of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily MSLP fields with the central point being located over Germany. 77 weather classes based on the associated circulation weather type and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamical downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different datasets the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes results of SDD are compared to wind observations and to simulated Eout of purely dynamical downscaling (DD) methods. For the present climate SDD is able to simulate realistic PDFs of 10m-wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD simulated Eout. In terms of decadal hindcasts results of SDD are similar to DD simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout timeseries of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the MPI-ESM decadal prediction system. Long-term climate change projections in SRES scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to results of other studies using DD methods, with increasing Eout over Northern Europe and a negative trend over Southern Europe. Despite some biases it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.
Pinto, Joaquim G.; Reyers, Mark; Mömken, Julia
Temporal neural networks for downscaling climate variability and extremes.
This paper presents an application of temporal neural networks for downscaling global climate models (GCMs) output. Because of computational constraints, GCMs are usually run at coarse grid resolution (in the order of 100s of kilometres) and as a result they are inherently unable to present local sub-grid scale features and dynamics. Consequently, outputs from these models cannot be used directly in many climate change impact studies. This research explored the issues of 'downscaling' the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The downscaling models are developed and validated using large-scale predictor variables derived from the National Center for Environmental Prediction (NCEP) reanalysis data set. The performance of the temporal neural network downscaling model is also compared to a regression-based statistical downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The downscaling results for the base period (1961-2000) suggest that the TNN is an efficient method for downscaling both daily precipitation as well as daily maximum and minimum temperature series. Furthermore, the different model test results indicate that the TNN model mostly outperforms the statistical models for the downscaling of daily precipitation extremes and variability. PMID:16527456
Dibike, Yonas B; Coulibaly, Paulin
Joint Variable Spatial Downscaling (JVSD): A New Downscaling Method with Application to the Southeast US
NASA Astrophysics Data System (ADS)
Joint Variable Spatial Downscaling (JVSD) is a new downscaling method developed to produce high resolution gridded hydrological datasets suitable for regional watershed modeling and assessments. JVSD differs from other statistical downscaling methods in that multiple climatic variables are downscaled simultaneously to produce realistic and consistent climate fields. JVSD includes two major steps: bias correction and spatial downscaling. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. Bias correction is then based on quantile-to-quantile mapping of these stationary frequency distributions probability space. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons with 20th Century Climate in Coupled Models (20C3M) data show that JVSD reproduces the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with the North American regional climate change assessment program (NARCCAP) and other statistical downscaling methods over the southeastern US. The results show that JVSD performs favorably. JVSD is applied for all A1B and A2 CMIP3 GCM scenarios in the Apalachicola-Chattahoochee-Flint River Basin (southeast US) with the following general findings: (i) Mean monthly temperature exhibits increasing trends over the ACF basin for all seasons and all A1B and A2 scenarios; Most significant are the A2 temperature increases in the 2050 - 2099 time periods; (ii) In the southern ACF watersheds, mean precipitation generally exhibits a mild decline in early spring and summer and increases in late winter; For the northern ACF watersheds, mean precipitation decreases in summer and increases mildly in winter (as in the south); (iii) In addition to mean trends, the precipitation distributions stretch on both ends with higher highs (floods) and lower lows (droughts). The downscaled temperature and precipitation scenarios are the basis of a comprehensive hydrologic and water resources assessment (reported elsewhere) assessing significant water, agricultural, energy, and environmental sector impacts and underscoring the need for mitigation and adaptation measures.
Zhang, F.; Georgakakos, A. P.
Producing information for Vulnerability, Impacts and Adaptation work: The COordinated Regional Downscaling EXperiment (CORDEX) (Invited)
NASA Astrophysics Data System (ADS)
Regional climate information is needed for use in Vulnerability, Impacts and Adaptation (VIA) studies. This information can be obtained either from Global Climate Model (GCM) simulations or from different downscaling techniques that regionally enhance the GCM fields to produce fine scale climate information. Downscaling techniques include both dynamical (i.e. Regional Climate Models, or RCMs) and statistical methods, and can be applied in a variety of contexts, such as process studies and regional to local climate change projections. One of the key issues in producing climate information for VIA application is that of suitably characterizing underlying uncertainties. In fact, there are several sources of uncertainty in climate projections: limitations and systematic errors in GCMs and downscaling tools, greenhouse gas (GHG) emission and concentration scenarios, response of different models (physics and configurations) to GHG forcing, internal decadal to multidecadal variability of the climate system. In order to characterize these uncertainties, large ensembles of model projections are needed, a task that is best approached in a mullti-model, multi-laboratory international context. These premises have lead to the inception of the COordinated Regional Downscaling EXperiment (CORDEX), under the auspices of the World Climate Research program (WGRP). The purpose of CORDEX is threefold: 1) to evaluate and possibly improve regional downscaling techniques (both dynamical and statistical); 2) to produce a new generation of regional climate change projections for regions worldwide based on a multi-model approach; 3) to foster the interactions across the climate and VIA research communites. The CORDEX Phase I framework has been designed and implemented, and related activities have been strongly growing in the last 1-2 years with a wide international participation. This paper will review the status of CORDEX, especially drawing from the results of a major pan-CORDEX conference taking place on 4-7 November 2013 in Brussels. In particular, the paper will summarize lessons learned from the CORDEX Phase I activities and discuss future directions and areas in need of strengthening in view of the development of the CORDEX Phase II framework.
Giorgi, F.
A regional climate modelling projection ensemble experiment - NARCliM
NASA Astrophysics Data System (ADS)
Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensembles members that can be simulated such that choices must be made concerning which Global Climate Models (GCMs) to downscale from, and which Regional Climate Models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCMs and RCMs, as well as spanning the range of future climate projections present in the full GCM ensemble. The created ensemble provides a more robust view of future regional climate changes.
Evans, J. P.; Ji, F.; Lee, C.; Smith, P.; Argüeso, D.; Fita, L.
Exploring Ensemble Visualization
An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.
Phadke, Madhura N.; Pinto, Lifford; Alabi, Femi; Harter, Jonathan; Taylor, Russell M.; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.
Introduction to Ensemble Prediction
NSDL National Science Digital Library
This webcast is a shorter companion to the Ensemble Prediction Explained module, focusing more directly on immediate operational needs. Introductory content includes the role of ensemble forecasts, presentation of basic ensemble forecasting terms, and discussion of how ensemble prediction systems (EPSs) are created. The largest section is focused on common ensemble forecast products, including how they differ from traditional NWP products, how we interpret ensemble forecast products, the advantages and limitations of each product, how EPS products are verified, and how to use ensemble products in conjunction with one another to increase your understanding of forecast uncertainty. Finally, three brief cases from cold and warm seasons illustrate the use of ensemble products in the forecast process.
Spangler, Tim
Meteorological ensemble forecasts aim at quantifying the uncertainty of a forecast by of- fering several scenarios of the future development of the weather. Ideally, we would think of the ensembles as samples from a probability distribution function reflecting the uncertainty of the unperturbed forecast. In this paper we address the problems of (i) transforming the mete- orological ensembles to wind
Henrik Aalborg Nielsen; Henrik Madsen; Torben Skov Nielsen; Jake Badger; Gregor Giebel; Lars Landberg; Kai Sattler; Henrik Feddersen
Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods
A precipitation downscaling method is presented using precipitation from a general circulation model (GCM) as predictor. The method extends a previous method from monthly to daily temporal resolution. The simplest form of the method corrects for biases in wet-day frequency and intensity. A more sophisticated variant also takes account of flow-dependent biases in the GCM. The method is flexible and
Jürg Schmidli; Christoph Frei; Pier Luigi Vidale
"Going the Extra Mile in Downscaling: Why Downscaling is not jut "Plug-and-Play"
This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Comm...
Input Needs for Downscaling of Climage Data, Discussion Paper.
National Technical Information Service (NTIS)
This report addresses the external data-input needs for climate data downscaling, with emphasis on the U.S. western region and a focus on producing high-spatial-resolution climate data for the future. Three downscaling methods are considered: two types of...
T. M. L. Wigley
Statistical atmospheric downscaling for rainfall estimation in Kyushu Island, Japan
The present paper develops linear regression models based on singular value decomposition (SVD) with the aim of downscaling atmospheric variables statistically to estimate average rainfall in the Chikugo River Basin, Kyushu Island, southern Japan, on a 12-hour basis. Models were designed to take only significantly correlated areas into account in the downscaling procedure. By using particularly precipitable water in combination
C. Bertacchi Uvo; Jonas Olsson; Osamu Morita; Kenji Jinno; Akira Kawamura; Koji Nishiyama; Nobukazu Koreeda; Takanobu Nakashima
Statistical precipitation downscaling in central Sweden with the analogue method
Most climate predictions show significant consequences globally and regionally, but many of its critical impacts will occur at sub-regional and local scales. Downscaling methods are, thus, needed to assess effects of large-scale atmospheric circulation on local parameters such as precipitation and runoff. This study aims at evaluating the analogue method (AM) as a benchmark method for precipitation downscaling in northern
Fredrik Wetterhall; Sven Halldin; Chong-Yu Xu
On the Use of "Inflation" in Statistical Downscaling
The technique of ''inflating'' in downscaling, which makes the downscaled climate variable have the right variance, is based on the assumption that all local variability can be traced back to large-scale variability. For practical situations this assumption is not valid, and inflation is an inappropriate technique. Instead, additive, randomized approaches should be adopted.
Hans von Storch
Nonstationary probabilistic downscaling of extreme precipitation
NASA Astrophysics Data System (ADS)
Reanalysis data and general circulation model outputs typically provide information at a coarse spatial resolution, which cannot directly be used for local impact studies. Downscaling methods have been developed to overcome this problem, and to obtain local-scale information from large-scale atmospheric variables. The deduction of local-scale extremes still is a challenge. Here a probabilistic downscaling approach is presented where the cumulative distribution functions (CDFs) of large- and local-scale extremes are linked by means of a transfer function. In this way, the CDF of the local-scale extremes is obtained for a projection period, and statistical characteristics, like return levels, are inferred. The input series are assumed to be distributed according to an extreme value distribution, the Generalized Pareto distribution (GPD). The GPD parameters are linked to further explanatory variables, hence defining a nonstationary model. The methodology (XCDF-t) results in a parametric CDF, which is as well a GPD. Realizations generated from this CDF provide confidence bands. The approach is applied to downscale National Centers for Environmental Prediction reanalysis precipitation in winter. Daily local precipitation at five stations in southern France is obtained. The calibration period 1951-1985 is used to infer precipitation over the validation period 1986-1999. The applicability of the approach is verified by using observations, quantile-quantile plots, and the continuous ranked probability score. The stationary XCDF-t approach shows good results and outperforms the nonparametric CDF-t approach or quantile mapping for some stations. The inclusion of covariate information improves results only sometimes; therefore, covariates have to be chosen with care.
Kallache, M.; Vrac, M.; Naveau, P.; Michelangeli, P.-A.
A Dynamical Downscaling Experiment over East Asia
NASA Astrophysics Data System (ADS)
To assess future climate change for Korea due to anthropogenic greenhouse gas and aerosol emissions, dynamical downscaling are carried out with MM5 at 18 km resolution over Korea driven at the lateral boundaries by meteorological field from atmospheric model ECHAM4 T106. Sea surface temperatures are from corresponding simulation with the ECHO-G. We analyze two 22-year regional climate simulations, one for present day conditions (1979-2000) and one for future condition (2079-2100) under SRES A1B Scenario. The simulated present day climate by the time-slice experiment with the high-resolution model ECHAM4 T106 show successful performance in simulating the northward migration and the local of the maximum rainfall during the rainy season over East Asia, although its rainfall amount was somewhat weak compared to the observation. Change of East Asian summer monsoon rainfall in the future tends to be increased especially over the east of Japan during July and September. Downscaled mean temperature over Korea during the period of 1979-2000 reproduce the realistic features although the results have cold bias. Simulated daily mean temperature will increase about 3.3É by the end of the 21st century compared with present day and, seasonally the rising is projected to be larger in winter than in summer. Also, simulated precipitation will increase about 15% by the end of of the 21st century compared with present day. These downscaled future climate scenario will be used for studies on impact, adapatation, and vulnerability of climate change over Korea.
Baek, H.; Kwon, W.; Choi, D.; Kim, C.; Cha, Y.
Mid-Century Ensemble Regional Climate Change Scenarios for the Western United States
To study the impacts of climate change on water resources in the western U.S., global climate simulations were produced using the National Center for Atmospheric Research\\/Department of Energy (NCAR\\/DOE) Parallel Climate Model (PCM). The Penn State\\/NCAR Mesoscale Model (MM5) was used to downscale the PCM control (20 years) and three future(2040–2060) climate simulations to yield ensemble regional climate simulations at
L. Ruby Leung; Yun Qian; Xindi Bian; Warren M. Washington; Jongil Han; John O. Roads
Physically Based Global Downscaling: Regional Evaluation
The climate simulated by a global atmosphere/land model with a physically-based subgrid orography scheme is evaluated in ten selected regions. Climate variables simulated for each of multiple elevation classes within each grid cell are mapped according the high-resolution distribution of surface elevation in each region. Comparison of the simulated annual mean climate with gridded observations leads to the following conclusions. At low to moderate elevations the downscaling scheme correctly simulates increasing precipitation, decreasing temperature, and increasing snow with increasing elevation within regions smaller than 100 km. At high elevations the downscaling scheme correctly simulates a decrease in precipitation with increasing elevation. Too little precipitation is simulated on the windward side of mountain ranges and too much precipitation is simulated on the lee side. The simulated sensitivity of surface air temperature to surface elevation is too strong, particularly in valleys influenced by drainage circulations. Observations show little evidence of a "snow shadow", so the neglect of the subgrid rainshadow does not produce an unrealistic simulation of the snow distribution. Summertime snow area, which is a proxy for land ice, is much larger than observed. Summertime snow water equivalent is far less than the observed thickness of glaciers because a 1 m upper bound on snow water is applied to the simulations and because snow transport by slides is neglected. The 1 m upper bound on snow water equivalent also causes an underestimate of seasonal snow water during late winter, compared with gridded station measurements. Potential solutions to these problems are discussed.
Ghan, Steven J.; Shippert, Timothy R.; Fox, Jared
Statistical and dynamical downscaling of GCM output to estimate future flood hazards for the Upper Severn catchment in UK
NASA Astrophysics Data System (ADS)
The problems of using Global Climate Models (GCMs) in local impact studies are well known, especially concerning events on tails of the distribution. The development of GCMs in terms of resolution and physical processes is progressing, but there is still a need today to downscale the output from the large scale to the local scale. The two main tools for this are classically dynamical downscaling through Regional Climate Models (RCMs), and statistical downscaling (SD) through a transfer function. However, also RCMs have biases in there output variables, originating both from the driving GCM and from limitations in the model itself, and the output often has to be modified before it can be used in impact studies, so called model output statistics (MOS). SD methods are usually good at capturing the statistical properties, but it is not straight-forward to model spatial and temporal correlations between variables. In this study, RCM and SD methods were applied to precipitation from the HadCM3 ensemble runs from the UKCP09 under the future scenario A1B from 1950-2099. A simple MOS was also applied to the RCM. The precipitation together with modeled temperature was then used to drive two hydrological models, LISFLOOD-RR and HBV to analyse future flood producing patterns in discharge. The results show the benefit of using two methods, and thereby assessing more of the uncertainty in climate impact studies.
Wetterhall, F.; Pappenberger, F.; He, Y.; Freer, J.; Cloke, H.; Wilson, M.; McGregor, G.
NASA Astrophysics Data System (ADS)
Classical messages can be sent via a noisy quantum channel in various ways, corresponding to various choices of ensembles of signal states of the channel. Previous work by Holevo and by Schumacher and Westmoreland relates the capacity of the channel to the properties of the signal ensemble. Here we describe some properties characterizing the ensemble that maximizes the capacity, using the relative entropy ``distance'' between density operators to give the results a geometric flavor.
Schumacher, Benjamin; Westmoreland, Michael D.
Downscaling of RCM outputs for representative catchments in the Mediterranean region, for the 1951-2100 time-frame
NASA Astrophysics Data System (ADS)
Within the activities of the EU FP7 CLIMB project (www.climb-fp7.eu), we developed downscaling procedures to reliably assess climate forcing at hydrologically relevant scales, and applied them to six representative hydrological basins located in the Mediterranean region: Riu Mannu and Noce in Italy, Chiba in Tunisia, Kocaeli in Turkey, Thau in France, and Gaza in Palestine. As a first step towards this aim, we used daily precipitation and temperature data from the gridded E-OBS project (www.ecad.eu/dailydata), as reference fields, to rank 14 Regional Climate Model (RCM) outputs from the ENSEMBLES project (http://ensembles-eu.metoffice.com). The four best performing model outputs were selected, with the additional constraint of maintaining 2 outputs obtained from running different RCMs driven by the same GCM, and 2 runs from the same RCM driven by different GCMs. For these four RCM-GCM model combinations, a set of downscaling techniques were developed and applied, for the period 1951-2100, to variables used in hydrological modeling (i.e. precipitation; mean, maximum and minimum daily temperatures; direct solar radiation, relative humidity, magnitude and direction of surface winds). The quality of the final products is discussed, together with the results obtained after applying a bias reduction procedure to daily temperature and precipitation fields.
Deidda, Roberto; Marrocu, Marino; Pusceddu, Gabriella; Langousis, Andreas; Mascaro, Giuseppe; Caroletti, Giulio
Extreme winds over Europe in the ENSEMBLES regional climate models
NASA Astrophysics Data System (ADS)
Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate projections of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe following the SRES A1B scenario from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES). It investigates the projected changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the generalised Pareto distribution. The models show that, for much of Europe, the 50 yr return wind is projected to change by less than 2 m s-1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s-1 between two different downscalings.
Outten, S. D.; Esau, I.
Extreme winds over Europe in the ENSEMBLES regional climate models
NASA Astrophysics Data System (ADS)
Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate predictions of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES), and investigates the predicted changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the Generalised Pareto Distribution. The models show that for much of Europe the 50 yr return wind is projected to change by less than 2 m s-1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s-1 between two different downscalings.
Outten, S. D.; Esau, I.
Downscaling climate information for local disease mapping.
The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical downscaling using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the downscaling of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage. PMID:16881400
Bernardi, M; Gommes, R; Grieser, J
Statistical-dynamical downscaling procedure for global climate simulations.
National Technical Information Service (NTIS)
A statistical-dynamical downscaling procedure for global climate simulations is described. The procedure is based on the assumption that any regional climate is associated with a specific frequency distribution of classified large-scale weather situations...
A. Frey-Buness D. Heimann R. Sausen U. Schumann
Statistical Downscaling Method for Climate Data to preserve Statistical Properties
NASA Astrophysics Data System (ADS)
Climate projections produced from the general circulation models (GCMs) need to be spatially and temporally downscaled before applied to the land surface models. Dynamic and statistical downscaling methods have been widely used. The U.S. Bureau of Reclamation and the Lawrence Livermore National Laboratory (LLNL) have developed a Bias Corrected Spatially Downscaled (BCSD) precipitation and temperature data in a monthly scale from the 112 World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) datasets using a statistical downscaling method described by Maurer et al. (2007). However, we need to downscale the monthly CMIP3 BCSD data to daily scale for the hydrologic model simulations. We applied the constant bias (anomaly) correction for the temperature, while we used the four downscaling schemes for precipitation. Four methods are constant bias correction method, ratio method, Gamma-Gamma transformation, and Gamma-Gamma transformation with ratio adjustment method. We evaluates if statistical downscaling methods can preserve the statistical properties such as mean, variance, frequency, and minimum and maximum. Gamma-Gamma distribution is derived for each month using the monthly values of historical data (1950-1999) at each cell and the monthly value of climate projection is found from the Gamma-Gamma distribution function. We further use the nearest neighbor search technique to assign temporal precipitation pattern when there is no event in the daily historical data during the month while the monthly climate data shows any precipitation. We simulate the streamflow using the Variable Infiltration Capacity (VIC) model and assess the performance of the downscaling methods. The Gamma-Gamma transformation with ratio adjustment method preserves the statistical properties, while other methods partially preserve statistical properties.
Park, G.; Song, T.
High-resolution precipitation and temperature downscaling for glacier models
The spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates\\u000a the need for climate downscaling. For impact studies that involve glacier modelling, the sparsity or complete absence of climate\\u000a monitoring activities within the regions of interest presents a substantial additional challenge. Downscaling methods for\\u000a this application must be independent of climate observations
Alexander H. JaroschFaron; Faron S. Anslow; Garry K. C. Clarke
A statistical downscaling method for monthly total precipitation over Turkey
Researchers are aware of certain types of problems that arise when modelling interconnections between general circulation and regional processes, such as prediction of regional, local-scale climate variables from large-scale processes, e.g. by means of general circulation model (GCM) outputs. The problem solution is called downscaling. In this paper, a statistical downscaling approach to monthly total precipitation over Turkey, which is
Hasan Tatli; H. Nüzhet Dalfes; Sibel Mente
Ensemble Forecasting Explained
NSDL National Science Digital Library
This module, the latest in our series on Numerical Weather Prediction, covers the theory and use of ensemble prediction systems (EPSs). The module will help forecasters develop an understanding of the basis for EPSs, the skills to interpret ensemble products, and strategies for their use in the forecast process. It contains six sections: an Introduction that briefly presents background theory; Generation, which describes how ensemble systems are constructed; Statistical Concepts, which provides a brief refresher on knowledge required for ensemble product interpretation; Summarizing Data, which describes common ensemble forecast products; Verification, which discusses how EPSs performance is assessed and documented; and Case Applications, which provides links to a number of forecast cases illustrating the use of EPSs in the forecast process. Questions and Exercises are offered throughout to help you test your learning and provide practical examples. The module also includes a pre-assessment and module final quiz.
Spangler, Tim
A general method for downscaling earth resource information
NASA Astrophysics Data System (ADS)
A programme scripted for use in an R programming environment called dissever is presented. This programme was designed to facilitate a generalised method for downscaling coarsely resolved earth resource information using available finely gridded covariate data. Under the assumption that the relationship between the target variable being downscaled and the available covariates can be nonlinear, dissever uses weighted generalised additive models (GAMs) to drive the empirical function. An iterative algorithm of GAM fitting and adjustment attempts to optimise the downscaling to ensure that the target variable value given for each coarse grid cell equals the average of all target variable values at the fine scale in each coarse grid cell. A number of outputs needed for mapping results and diagnostic purposes are automatically generated from dissever. We demonstrate the programs' functionality by downscaling a soil organic carbon (SOC) map with 1-km by 1-km grid resolution down to a 90-m by 90-m grid resolution using available covariate information derived from a digital elevation model, Landsat ETM+ data, and airborne gamma radiometric data. dissever produced high quality results as indicated by a low weighted root mean square error between averaged 90-m SOC predictions within their corresponding 1-km grid cell (0.82 kg m-3). Additionally, from a concordance between the downscaled map and another map created using digital soil mapping methods there was a strong agreement (0.94). Future versioning of dissever will investigate quantifying the uncertainty of the downscaled outputs.
Malone, Brendan P.; McBratney, Alex B.; Minasny, Budiman; Wheeler, Ichsani
To Downscale or not to Downscale? That's the question. A flood forecasting perspective.
NASA Astrophysics Data System (ADS)
There is a growing body of literature investigating the subject of rainfall downscaling. The research subject has been sparked by the need to link the predictions of climate models, that are typically ran on tens of kilometer grids, to distributed watershed models, that typically require input at the sub-kilometer scale. This obvious disparity seems to imply that techniques and algorithms need to be developed to scale down the coarse grid information keeping as much of physical reality of the reconstructed fine grid fields. However, the benefits or downscaling rainfall may be less important than previously expected. Our group has been developing and testing multiscale distributed watershed models for flood predictions for several years and we consistently find that finer resolution rainfall may not imply better flood prediction capabilities. At the heart of this issue is the existence of the self-similar network that aggregates flows in the landscape and that ultimately determines the occurrence of floods in a particular basin outlet. We present examples of how rainfall inputs with different resolution impact our flood prediction accuracy across multiple spatial scales. We show for example, using precipitation fields on a daily 12 km grid and a 5 minute 500 m grid, that basins larger than 1000 km2, are insensitive to the resolution of the input product. We show that the sensitivity to the input product is largely determined by the equations that describe the rainfall runoff transformation (linear vs nonlinear). However, we also show that prediction accuracy, with different input grids, increases with increasing scale of the basin (e.g. 30,000 km2). The answer to the question for downscaling or not in flood prediction becomes, "what size is your basin?"
Krajewski, Witold F.; Mantilla, Ricardo; Ayalew, Tibebu B.; Small, Scott J.
Evaluation of Soil Moisture Downscaling Algorithms for the SMAP Mission
NASA Astrophysics Data System (ADS)
The Soil Moisture Active Passive (SMAP) satellite is scheduled for launch by NASA in November 2014, with the aim to provide a medium-resolution soil moisture product at the global scale and with 2-3 days revisit frequency. The rationale behind this mission is that the synergy between 3 km resolution active (radar) and 36 km resolution passive (radiometer) observations can be used in a downscaling approach to overcome the individual limitations of each observation, ultimately providing soil moisture data at a resolution suitable for hydro-meteorological applications, on the order of ~9 km. Two soil moisture downscaling approaches were tested in this study: i) the baseline downscaling algorithm proposed for SMAP, which is based on an assumption of linear relationship between radiometer and radar observations, with the downscaled radiometer data then converted to a soil moisture product using the passive microwave retrieval method; ii) the optional downscaling algorithm for SMAP, which is based on an assumption of a directly linear relationship between soil moisture and the radar observations. Data used to evaluate these two approaches were collected from the Soil Moisture Active Passive Experiments (SMAPEx) in south-eastern Australia, which closely simulate the SMAP data stream using airborne observations for a single SMAP radiometer pixel over a 3-week interval. Both approaches were compared to a reference soil moisture map retrieved from 1 km resolution radiometer data. Results indicated that radar observations at vv-polarization had the best correlation with radiometer observations or soil moisture data than hh- or hv-polarization, thus having best performance during downscaling procedure. These two downscaling approaches showed similar performance in terms of accuracy, with a Root-Mean-Square Error (RMSE) in downscaled soil moisture data around 0.02 cm3/cm3, when downscaled to 9 km resolution. This increased to 0.043 cm3/cm3 when applied at 1 km resolution. Results indicated both downscaling methods had the ability to fulfill the error target of SMAP, with a RMSE less than 0.04 cm3/cm3 at 9 km resolution.
Wu, Xiaoling; Walker, Jeffrey; Rüdiger, Christoph; Panciera, Rocco
Dynamic downscaling of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States
NASA Astrophysics Data System (ADS)
This study evaluates the UCLA-ETA regional model's dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day-1, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement.
De Sales, Fernando; Xue, Yongkang
Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow
NASA Astrophysics Data System (ADS)
A Member-to-Member ensemble forecasting system is developed for inflows to hydroelectric reservoirs that incorporates multiple numerical weather prediction models and multiple distributed hydrological models linked by a variety of downscaling schemes. Each hydrological model uses multiple differently-optimized parameter sets and begins each daily forecast from several different initial conditions. The ensemble thereby attempts to sample all sources of error in the modeling chain. The importance of sampling all sources of error is illustrated by comparing this ensemble with an ensemble comprised of single 'best' parameterization for each hydrological model. Degree-of-mass-balance bias correction schemes trained using data windows of varying lengths are applied to the individual ensemble members. Based on examination of various verification metrics, we determine that a bias corrector that uses a linearly-weighted combination of past errors calculated over a three-day moving window is able to significantly improve forecast quality for the flashy case study watershed in southwestern British Columbia, Canada. Incorporation of all sources of modeling uncertainty is found to greatly improve ensemble resolution and discrimination. The full potential for these improvements using ensembles is only realized after removal of bias.
Bourdin, Dominique R.; Stull, Roland B.
NASA Astrophysics Data System (ADS)
During the past 10 years ensemble forecasting has established itself as an important component in numerical weather prediction. Global ensemble prediction systems have been operational at the European Centre for Medium-Range Weather Forecasts (ECMWF) and at the National Meteorological Center for Environmental Prediction (NOAA/NWS/NCEP) since December 1992, and at the Meterological Service of Canada (MSC/CMC) since February 1998. In this talk, the similarities and differences among the three operational global ensemble forecast systems are discussed. The performance of the three systems is illustrated and compared over a three month period (May-July) in 2002. Also reviewed are open issues, ongoing research projects, and future directions related to ensemble forecasting efforts at the three centers.
Toth, Z.; Buizza, R.; Houtekamer, P.
NASA Technical Reports Server (NTRS)
Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
Oza, Nikunj C.
Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods
NASA Astrophysics Data System (ADS)
A precipitation downscaling method is presented using precipitation from a general circulation model (GCM) as predictor. The method extends a previous method from monthly to daily temporal resolution. The simplest form of the method corrects for biases in wet-day frequency and intensity. A more sophisticated variant also takes account of flow-dependent biases in the GCM. The method is flexible and simple to implement. It is proposed here as a correction of GCM output for applications where sophisticated methods are not available, or as a benchmark for the evaluation of other downscaling methods.Applied to output from reanalyses (ECMWF, NCEP) in the region of the European Alps, the method is capable of reducing large biases in the precipitation frequency distribution, even for high quantiles. The two variants exhibit similar performances, but the ideal choice of method can depend on the GCM/reanalysis and it is recommended to test the methods in each case. Limitations of the method are found in small areas with unresolved topographic detail that influence higher-order statistics (e.g. high quantiles). When used as benchmark for three regional climate models (RCMs), the corrected reanalysis and the RCMs perform similarly in many regions, but the added value of the latter is evident for high quantiles in some small regions.
Schmidli, Jürg; Frei, Christoph; Vidale, Pier Luigi
\\u000a Bayesian and decision tree classifiers are among the most popular classifiers used in the data mining community and recently\\u000a numerous researchers have examined their sufficiency in ensembles. Although, many methods of ensemble creation have been proposed,\\u000a there is as yet no clear picture of which method is best. In this work, we propose Bagged Voting using different subsets of\\u000a the
Sotiris B. Kotsiantis; Panayiotis E. Pintelas
Dynamic Downscaling of CMIP5 Climate Scenarios for Alaska Region
NASA Astrophysics Data System (ADS)
Alaska and its surrounding coastal areas are prominent geographical features that are largely covered by sea ice on a seasonal basis over the ocean and exhibit sharply varied topography on land. The complex geographical features significantly complicate Alaska regional climate systems. Thus, the representation of complex topography in high-resolution regional model is needed for accurate assessment of regional responses to global climate change. This study used the state-of-the-art high-resolution regional model WRF to conduct dynamical downscaling of GCM simulated and projected climate change scenarios within the framework of the 5th Coupled Model Intercomparison Project (CMIP5). The CCSM4 simulated 20th century climate (1990-2005) was first downscaled. The downscaled high-resolution data was then verified against observations, which shows correctable systematic biases. The bias-reduced downscaling results for the present-day climate compare favorably to the observations. The bias-reduced downscaling of future climate during 2005-2100 under RCP6 scenario suggests a strong winter warming and an increased surface wind speed. The annual mean precipitation will likely increase significantly over the Alaska Range.
Zhang, J.; Hock, R. M.; Lu, C.; Krieger, J. R.; Bhatt, U. S.; Zhang, X.
Predictor selection for downscaling GCM data with LASSO
NASA Astrophysics Data System (ADS)
Over the last 10 years, downscaling techniques, including both dynamical (i.e., the regional climate model) and statistical methods, have been widely developed to provide climate change information at a finer resolution than that provided by global climate models (GCMs). Because one of the major aims of downscaling techniques is to provide the most accurate information possible, data analysts have tried a number of approaches to improve predictor selection, which is one of the most important steps in downscaling techniques. Classical methods such as regression techniques, particularly stepwise regression (SWR), have been employed for downscaling. However, SWR presents some limits, such as deficiencies in dealing with collinearity problems, while also providing overly complex models. Thus, the least absolute shrinkage and selection operator (LASSO) technique, which is a penalized regression method, is presented as another alternative for predictor selection in downscaling GCM data. It may allow for more accurate and clear models that can properly deal with collinearity problems. Therefore, the objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Québec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.
Hammami, Dorra; Lee, Tae Sam; Ouarda, Taha B. M. J.; Lee, Jonghyun
Downscaling extremes with EDS, TreeGen, and BCSD
NASA Astrophysics Data System (ADS)
We conduct an intercomparison of the ability of three statistical downscaling methods, Expanded Downscaling (EDS), TreeGen, and the Bias-Corrected Spatial Disaggregation (BCSD) scheme, to represent the statistics of climatic extremes. These three methods represent fairly different approaches to the downscaling problem: EDS is based on regression, TreeGen uses synoptic weather types, and BCSD combines quantile mapping and resampling. EDS and TreeGen are driven by daily atmospheric predictor fields and therefore allow for the verification of single extreme events using daily analysis fields (ECMWF, NCEP) and station data. BCSD is driven by monthly fields, while daily data are generated semi-stochastically by resampling the historical record. BCSD simulated events are therefore not directly comparable to observed ones. Long-term statistics are, however, so we compare appropriate measures for local extremes from all three methods, using an independent analysis period. Extreme event statistics are taken from ClimDex, a set of impact-oriented indices derived from the STARDEX project. The downscaling will be applied to several areas in British Columbia that are representative of the provinces' major climatic zones. The resulting methodological uncertainty will be assessed against the natural uncertainty stemming from different realizations of the present climate, as downscaled from analyses or from simulations forced by present-day greenhouse gas concentrations (20C3M) using three different climate models.
Buerger, G.; Murdock, T.; Werner, A. T.
Spatiostatistical downscaling of soil moisture in an assimilation framework
NASA Astrophysics Data System (ADS)
The scale reconciliation issue has gained in extra attention with remote sensing data coming in and the shift towards the distributed approach for hydrologic modeling. The purpose of the current research is to develop a method to disaggregate coarse resolution remote sensing data to fine resolutions more appropriate in hydrologic studies. Disaggregation is done with the help of point measurements on the ground. The downscaling of remote sensing data is achieved by three main steps namely: initialization, spatial pattern mimicking, and assimilation. The assimilation step also excerpts the information coming from the point measurements. These three steps provide means of capturing both spatial trend and physics of the process at multiple resolution levels while downscaling. The approach has been applied and validated by downscaling images for two cases. In the first case a synthetically generated random field based on the statistical properties of point measurements is reproduced at fine scale and coarse resolutions. The algorithm was able to account for spatial and vertical properties for this synthetic case. In the second case a soil moisture field from SGP 97 experiments is downscaled from a resolution of 800m x 800m to a resolution of 50m x 50m. It is also shown that how the assimilation step helped to improve the approximation of the downscaled fields.
Kaheil, Y. H.; Gill, M.; McKee, M.; Bastidas, L.
Linking Global Climate Models to an Integrated Hydrologic Model: Using an Individual Station Downscaling Approach
This paper implements an individual station-based downscaled approach based on a quantil- quantile bias correction mapping to further downscale regional gridded simulated output into individual station locations. We describe and propose a framework to optimize the usefulness of this downscaling approach over small watersheds in mountain regions, where downscale gridded data (~10km) is still too coarse for use in sub-regional
John F. Mejia; Justin Huntington; Benjamin Hatchett; Darko Koracin; Richard G. Niswonger
Numerical modeling of hailstorm using downscaling
NASA Astrophysics Data System (ADS)
Global warming of about 0.6^oC has been observed during the last century with a marked increase of the mean temperature of the lower atmosphere during the last 10 years. If anthropogenic emissions would not be reduced, current global climate models (GCM) predict a global warming of about 2^oC between 1990 and 2100. Such warming may increase the frequency and the intensity of extreme climatic events (i.e., windstorms, heavy rain, tornadoes, thunderstorm days, and hail). The increase of severe weather events may have important impacts on societies, such as loss of life and property damage. However, due to the limited resolutions of GCMs that resolve only large and synoptic atmospheric features, they cannot predict how. To compensate for the limited resolutions of the GCMs, regional climate models (RCM) have been developed during the last decade for downscaling GCM simulations at regional scales. Such technique may be used to simulate severe local weather events, such as tornadoes, thunderstorm and hailstorm events. The purpose of this study is to assess the ability of the Canadian regional climate model (CRCM-2) to help predicting hailstorm events. The CRCM-2, which is a limited-area grid-point non-hydrostatic model using a three-time-level semi-Lagrangian semi-implicit time marching scheme, has been applied to the simulation of a hailstorm event observed in Switzerland in 1993. Sensitivity experiments of the model's results to the nested domain and to two moist convection schemes are also presented.
Koffi, E. N.; Goyette, S.; Wuest, M.; Perroud, M.
Operational Downscaling of Soil Moisture Fields Using Ancillary Data
NASA Astrophysics Data System (ADS)
The scaling analysis of large-scale soil moisture data from the Southern Great Plains Hydrology experiment (SGP'97) showed that the scaling behavior of soil moisture is multifractal varying with the scale of observations and hydroclimatological conditions which can be explained with scaling behavior of soil hydraulic properties. These results suggested that it should be possible to use the spatial patterns of ancillary data at high resolution such as the sand content of soils as spatial basis functions for downscaling. This hypothesis was investigated by applying a modified fractal interpolation method for downscaling soil moisture from the SGP'97 experiment using ancillary data. The methodology should be especially useful for downscaling large-scale remotely-sensed estimates of soil moisture (e.g. AMSR) to the scales of operational hydrologic models.
Kim, G.; Barros, A. P.
An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate
An assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 European stations during 1983-94. A statistical downscaling technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical downscaling techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of
James Murphy
A comparison of statistical and dynamical downscaling for surface temperature in North America
Projections from general circulation model (GCM) simulations must be downscaled to the high spatial resolution needed for assessing local and regional impacts of climate change, but uncertainties in the downscaling process are difficult to quantify. We employed a multiple linear regression model and the MM5 dynamical model to downscale June, July, and August monthly mean surface temperature over eastern North
Scott Spak; Tracey Holloway; Barry Lynn; Richard Goldberg
A standardized framework for evaluating the skill of regional climate downscaling techniques
Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly downscaled using a variety of statistical and dynamical techniques. Despite the essential role of downscaling in regional assessments, there is no standard approach to evaluating various downscaling methods. Hence, impact communities often
Katharine Anne Hayhoe
Predicting future wind power generation and power demand in France using statistical downscaling methods developed for hydropower applications
NASA Astrophysics Data System (ADS)
For many applications in the energy sector, it is crucial to dispose of downscaling methods that enable to conserve space-time dependences at very fine spatial and temporal scales between variables affecting electricity production and consumption. For climate change impact studies, this is an extremely difficult task, particularly as reliable climate information is usually found at regional and monthly scales at best, although many industry oriented applications need further refined information (hydropower production model, wind energy production model, power demand model, power balance model…). Here we thus propose to investigate the question of how to predict and quantify the influence of climate change on climate-related energies and the energy demand. To do so, statistical downscaling methods originally developed for studying climate change impacts on hydrological cycles in France (and which have been used to compute hydropower production in France), have been applied for predicting wind power generation in France and an air temperature indicator commonly used for predicting power demand in France. We show that those methods provide satisfactory results over the recent past and apply this methodology to several climate model runs from the ENSEMBLES project.
Najac, Julien
On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia
NASA Astrophysics Data System (ADS)
The ability of an ensemble of six GCMs, downscaled to a 0.1° lat/lon grid using the Conformal Cubic Atmospheric Model over Tasmania, Australia, to simulate observed extreme temperature and precipitation climatologies and statewide trends is assessed for 1961-2009 using a suite of extreme indices. The downscaled simulations have high skill in reproducing extreme temperatures, with the majority of models reproducing the statewide averaged sign and magnitude of recent observed trends of increasing warm days and warm nights and decreasing frost days. The warm spell duration index is however underestimated, while variance is generally overrepresented in the extreme temperature range across most regions. The simulations show a lower level of skill in modelling the amplitude of the extreme precipitation indices such as very wet days, but simulate the observed spatial patterns and variability. In general, simulations of dry extreme precipitation indices are underestimated in dryer areas and wet extremes indices are underestimated in wetter areas. Using two SRES emissions scenarios, the simulations indicate a significant increase in warm nights compared to a slightly more moderate increase in warm days, and an increase in maximum 1- and 5- day precipitation intensities interspersed with longer consecutive dry spells across Tasmania during the twenty-first century.
White, Christopher J.; McInnes, Kathleen L.; Cechet, Robert P.; Corney, Stuart P.; Grose, Michael R.; Holz, Gregory K.; Katzfey, Jack J.; Bindoff, Nathaniel L.
NASA Technical Reports Server (NTRS)
Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.
Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)
Stochastic space-time downscaling of GCM precipitation
NASA Astrophysics Data System (ADS)
A new downscaling algorithm is presented. The algorithm is a composite scheme consisting of 2 sub-models, a Stochastic Space-Time Disaggregation Model (SSTDM), which accounts for the hierarchical structure of the spatial and temporal statistical dependencies of precipitation and an Intermittent Random Cascade Model (IRCM) which accounts for the scale invariant feature and reproduces self-similarity structure and spatial intermittent cluster formation. The model is applied to downscale rainfall output of the Canadian Global Climate Model II, whose spatial resolution is approximately 3.7degree(about 400km), to the 2*2km-scale field. For SSTDM, Valencia and Schaake's general disaggregation model(1973), which was originally developed for multi-site, multi-season streamflow disaggregation, was modified in this work for use in the downscaling of grid precipitation. IRCM adopted the mass re-distribution concept of the multiplicative random cascade structure (Kang and Ramirez, 2002, Kang and Ramirez, 2001, Over and Gupta, 1996). This model was tested first on the aggregated(256*256km) and original(2*2km) daily NEXRAD precipitation field of July, 1997. The spatial correlation of the simulated precipitation fields reasonably reproduces the patterns of the observations. The lag-1(day) temporal correlation of the simulation, as a function of scale, reproduces the observed behavior adequately. However, the intermittency parameter, beta, appears to be underestimated by the model. This model is then applied for downscaling output of CGCMII produced by CCCma(Canadian Center for Climate modeling and analysis).
Kang, B.; Ramirez, J. A.
Statistical Downscaling of Daily Temperature in Central Europe
Statistical downscaling methods and potential large-scale predictors are intercompared for winter daily mean temperature in a network of stations in central and western Europe. The methods comprise (i) canonical correlation analysis (CCA), (ii) singular value decomposition analysis, (iii) multiple linear regression (MLR) of predictor principal components (PCs) with stepwise screening, (iv) MLR of predictor PCs without screening (i.e., all PCs
Radan Huth
Statistical downscaling of hydrometeorological variables using general circulation model output
Empirical relationships between daily hydrometeorological variables for a catchment in Nagano prefecture, Japan and three indices of regional atmospheric circulation are examined with a view to assessing their use in General Circulation Model (GCM) downscaling. The indices (vorticity, flow strength and angular direction of airflow) were calculated by using daily grid-point sea-level pressure data derived from: (a) the National Centers
Robert L. Wilby; Hany Hassan; Keisuke Hanaki
Classifier Ensembles for Changing Environments
We consider strategies for building classier ensembles for non-stationary environments where the classication task changes dur- ing the operation of the ensemble. Individual classier models capable of online learning are reviewed. The concept of \\\\forgetting\\
Ludmila I. Kuncheva
Stepwise analogue downscaling for hydrology (SANDHY): validation experiments over France
NASA Astrophysics Data System (ADS)
Statistical downscaling aims at finding relationships between local precipitation (predictand) and large-scale predictor fields, in various contexts, from medium-term forecasting to climate change impact studies. One of the challenges of statistical downscaling in a climate change context is that the predictor-predictand relationship should still be valid under climate change conditions. A minimum requirement is therefore to test the performance of the downscaling method on independent data under current climate conditions. The downscaling method considered is the Stepwise ANalog Downscaling method for HYdrology (SANDHY). ERA-40 reanalysis data are used as large scale predictors and daily precipitation from the French near surface reanalysis (Safran) as predictand. Two 20-year periods have been selected from the common archive period of the two data sources: 1958-1978 ('early') and 1982-2002 ('late'). SANDHY has been optimised over the late period in terms of geopotential predictor domains individually for 608 target zones covering France. The validation setup consists of 4 experiments, that all use the parameters as optimised for the late period and that are compared in terms of continous ranked probability skill score (CRPSS) with climatology as reference: Reference simulation. A simulation of the late period is performed using the late period as an archive for searching the analogue dates, thus representing the best possible case. The CRPSS shows a spatial distribution similar to the one of the mean precipitation. Out-of-sample validation. The early period is simulated using the late period as an archive for searching the analogue dates. The idea is to simulate a period whose local data is not 'known' by the model as it would be the case in any application. The average skill loss compared to the reference simulation is reasonable with some more skill loss in the northern part of the country and no loss in the southeastern part. Alternative archive. The late period is simulated using the early period as an archive for the analogue search. Using the alternative archive leads to small and spatially uniform skill loss compared to the reference simulation. Imperfect predictor domains. The early period is simulated using the early period as an archive for the analogue search. The results are very similar to the out-of-sample validation in terms of mean skill loss and spatial distribution. The results of experiment 2 indicate that SANDHY is quite robust at most locations. Experiment 3 shows that both archives are suitable for downscaling. Experiment 4 shows that the skill loss observed in experiment 2 stems rather from the imperfect predictor domains than from the imperfect archive. Overall the results increase the confidence in applying SANDHY for downscaling in various contexts over France.
Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume
Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and architecture. It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks
Lars Kai Hansen; Peter Salamon
Music Ensemble: Course Proposal.
ERIC Educational Resources Information Center
A proposal is presented for a Music Ensemble course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…
Kovach, Brian
Evolutionary Ensemble for Stock Prediction
We propose a genetic ensemble of recurrent neural networks for stock prediction model. The genetic algorithm tunes neural networks in a two-dimensional and parallel framework. The ensemble makes the decision of buying or selling more conservative. It showed notable im- provement on the average over not only the buy-and-hold strategy but also other traditional ensemble approaches.
Yung-keun Kwon; Byung-Ro Moon
Statistical downscaling of daily precipitation over Llobregat river basin in Catalonia (Spain) using three downscaling methods.
NASA Astrophysics Data System (ADS)
Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).
Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.
Downscaling of land surface temperatures from SEVIRI
NASA Astrophysics Data System (ADS)
Land surface temperature (LST) determines the radiance emitted by the surface and hence is an important boundary condition of the energy balance. In urban areas, detailed knowledge about the diurnal cycle in LST can contribute to understand the urban heat island (UHI). Although the increased surface temperatures compared to the surrounding rural areas (surface urban heat island, SUHI) have been measured by satellites and analysed for several decades, an operational SUHI monitoring is still not available due to the lack of sensors with appropriate spatiotemporal resolution. While sensors on polar orbiting satellites are still restricted to approx. 100 m spatial resolution and coarse temporal coverage (about 1-2 weeks), sensors on geostationary platforms have high temporal (several times per hour) and poor spatial resolution (>3 km). Further, all polar orbiting satellites have a similar equator crossing time and hence the SUHI can at best be observed at two times a day. A downscaling DS scheme for LST from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard the geostationary meteorological Meteosat 8 to spatial resolutions between 100 and 1000 m was developed and tested for Hamburg. Various data were tested as predictors, including multispectral data and derived indices, morphological parameters from interferometric SAR and multitemporal thermal data. All predictors were upscaled to the coarse resolution approximating the point spread function of SEVIRI. Then empirical relationships between the predictors and LST were derived which are then transferred to the high resolution domain, assuming they are scale invariant. For validation LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Enhanced Thematic Mapper Plus (ETM+) for two dates were used. Aggregated parameters from multi-temporal thermal data (in particular annual cycle parameters and principal components) proved particularly suitable. The results for the highest resolution of 100 m showed a high explained variance (R^2 = 0.71) and relatively low root mean square errors (RMSE = 2.2 K) for the ASTER scene and slightly higher errors (R^2 = 0.73, RMSE = 2.53) for the ETM+ scene. A considerable percentage of the error was systematic due to the different viewing geometry of the sensors (the high resolution LST was overestimated about 1.3 K for ASTER and 0.66 K for ETM+). This shows that DS of SEVIRI LST is possible up to a resolution of 100 m for urban areas and that multitemporal thermal data are particularly suitable as predictors. Further, the scheme was used to produce an entire diurnal cycle in high resolution. While essential characteristics of the diurnal cycle were well reproduced, certain artefacts resulting from the multitemporal predictors from different seasons (like phenology and different water surface temperatures) were generated. Eventually, the bias and its dependence on the viewing geometry and topography are currently investigated.
Bechtel, B.; Zaksek, K.
Downscaling an Eddy-Resolving Global Model for the Continental Shelf off South Eastern Australia
NASA Astrophysics Data System (ADS)
The Australian Bluelink collaboration between CSIRO, the Bureau of Meteorology and the Royal Australian Navy has made available to the research community the output of BODAS (Bluelink ocean data assimilation system), an ensemble optimal interpolation reanalysis system with ~10 km resolution around Australia. Within the Bluelink project, BODAS fields are assimilated into a dynamic ocean model of the same resolution to produce BRAN (BlueLink ReANalysis, a hindcast of water properties around Australia from 1992 to 2004). In this study, BODAS hydrographic fields are assimilated into a ~ 3 km resolution Princeton Ocean Model (POM) configuration of the coastal ocean off SE Australia. Experiments were undertaken to establish the optimal strength and duration of the assimilation of BODAS fields into the 3 km resolution POM configuration for the purpose of producing hindcasts of ocean state. It is shown that the resultant downscaling of Bluelink products is better able to reproduce coastal features, particularly velocities and hydrography over the continental shelf off south eastern Australia. The BODAS-POM modelling system is used to provide a high-resolution simulation of the East Australian Current over the period 1992 to 2004. One of the applications that we will present is an investigation of the seasonal and inter-annual variability in the dispersion of passive particles in the East Australian Current. The practical outcome is an estimate of the connectivity of estuaries along the coast of southeast Australia, which is relevant for the dispersion of marine pests.
Roughan, M.; Baird, M.; MacDonald, H.; Oke, P.
Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods
NASA Astrophysics Data System (ADS)
Three statistical downscaling methods are compared with regard to their ability to downscale summer (June-September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046-2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.
Hu, Yurong; Maskey, Shreedhar; Uhlenbrook, Stefan
Stochastic Cascade Dynamical Downscaling of Precipitation over Complex Terrain
NASA Astrophysics Data System (ADS)
Global Climate Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called downscaling techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what decision-makers require. Among the most important impacts of regional-scale prediction of climate change is to assess how food production and security will be affected. Regional scale precipitation and temperature simulations are crucial to understand how global warming will affect fresh water storage and the ability to grow agricultural crops. Precipitation and temperature downscaling improve the coarse resolution and poor local representation of global climate models and help decision-makers to assess the likely hydrological impacts of climate change, and it would also help crop modelers to generate more realistic climatic-change scenarios. Thus, a spatial downscaling method was developed based on the multiplicative random cascade disaggregation theory, considering a ?-lognormal model describing the rainfall precipitation distribution and using the Mandelbrot-Kahane-Peyriere (MKP) function. In this paper, gridded 15 km resolution rainfall data over a 220 x 220 km section of the Andean Plateau and surroundings, generated by the Weather Research and Forecasting model (WRF), were downscaled to gridded 1 km layers with the Multifractal downscaling technique, complemented by a local heterogeneity filter. The process was tested for daily data over a period of five years (01/01/2001 - 12/31/2005). Specifically, The model parameters were estimated from 5 years of observed daily rainfall data from 18 rain gauges located in the region. A detailed testing of the model was undertaken on the basis of a comparison of the statistical characteristics of the spatial and temporal variability of rainfall between the rainfall fields obtained from the rain gauge network and those generated by the simulation model. The potential advantages of this methodology are discussed.Stochastic Cascade Dynamical Downscaling of Precipitation over Complex Terrain
Posadas, A.; Duffaut, L. E.; Jones, C.; Carvalho, L. V.; Carbajal, M.; Heidinger, H.; Quiroz, R.
Tailored Random Graph Ensembles
NASA Astrophysics Data System (ADS)
Tailored graph ensembles are a developing bridge between biological networks and statistical mechanics. The aim is to use this concept to generate a suite of rigorous tools that can be used to quantify and compare the topology of cellular signalling networks, such as protein-protein interaction networks and gene regulation networks. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies of random graph ensembles constrained with degree distribution and degree-degree correlation. We also construct an ergodic detailed balance Markov chain with non-trivial acceptance probabilities which converges to a strictly uniform measure and is based on edge swaps that conserve all degrees. The acceptance probabilities can be generalized to define Markov chains that target any alternative desired measure on the space of directed or undirected graphs, in order to generate graphs with more sophisticated topological features.
Roberts, E. S.; Annibale, A.; Coolen, A. C. C.
A statistical-dynamical downscaling procedure for global climate simulations
A statistical-dynamical downscaling procedure for global climate simulations is described. The procedure is based on the assumption that any regional climate is associated with a specific frequency distribution of classified large-scale weather situations. The frequency distributions are derived from multi-year episodes of low resolution global climate simulations. Highly resolved regional distributions of wind and temperature are calculated with a regional
F. Frey-Buness; D. Heimann; R. Sausen
Stochastic downscaling of precipitation with neural network conditional mixture models
NASA Astrophysics Data System (ADS)
We present a new class of stochastic downscaling models, the conditional mixture models (CMMs), which builds on neural network models. CMMs are mixture models whose parameters are functions of predictor variables. These functions are implemented with a one-layer feed-forward neural network. By combining the approximation capabilities of mixtures and neural networks, CMMs can, in principle, represent arbitrary conditional distributions. We evaluate the CMMs at downscaling precipitation data at three stations in the French Mediterranean region. A discrete (Dirac) component is included in the mixture to handle the "no-rain" events. Positive rainfall is modeled with a mixture of continuous densities, which can be either Gaussian, log-normal, or hybrid Pareto (an extension of the generalized Pareto). CMMs are stochastic weather generators in the sense that they provide a model for the conditional density of local variables given large-scale information. In this study, we did not look for the most appropriate set of predictors, and we settled for a decent set as the basis to compare the downscaling models. The set of predictors includes the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalyses sea level pressure fields on a 6 × 6 grid cell region surrounding the stations plus three date variables. We compare the three distribution families of CMMs with a simpler benchmark model, which is more common in the downscaling community. The difference between the benchmark model and CMMs is that positive rainfall is modeled with a single Gamma distribution. The results show that CMM with hybrid Pareto components outperforms both the CMM with Gaussian components and the benchmark model in terms of log-likelihood. However, there is no significant difference with the log-normal CMM. In general, the additional flexibility of mixture models, as opposed to using a single distribution, allows us to better represent the distribution of rainfall, both in the central part and in the upper tail.
Carreau, Julie; Vrac, Mathieu
Application of Quantile Regression for Statistical Downscaling of Daily Precipitation
NASA Astrophysics Data System (ADS)
Statistical downscaling is often used in climate change studies to bridge the gap between the resolution of global climate models and the resolution required in applications, as well as to resolve issues with model biases. Conventional linear regression models have been extensively used for this purpose. In the context of statistical downscaling, it involves the development of relationships between for example daily precipitation and large-scale variables that are presumably well represented in global climate models. However, linear regression models have a number of potential shortcomings. For example, the best prediction of high, low, and medium precipitation may require use of different subsets of predictor variables, something that cannot be accomplished with traditional regression models. The error distribution may not be Gaussian, even after some transformation of variables, and the error variance may not be independent of predictors. We address these shortcomings through the use of linear quantile regression. While traditional regression models predict the mean value in the conditional distribution, quantile regression predicts user-selected quantiles in the conditional distribution. By developing quantile regression models for a range of quantile levels, one can obtain an accurate representation of the conditional distribution corresponding to given values of the predictors, and a downscaled daily precipitation value can be obtained by sampling from the conditional distribution established in this way. The issue of selecting predictor variables for quantile regression is not as straightforward as for traditional regression models. We address this issue through Bayesian model averaging, implemented using the Gibbs sampler combined with stochastic search techniques. The suitability of the approach is evaluated and compared to the traditional regression model, using climate station data from Manitoba and data from the NCEP/NCAR Global Reanalysis. While in some cases quantile regression produces results that are fairly similar to those obtained from conventional linear regression, there are a number of instances where downscaling based on quantile regression outperforms the traditional method.
Rasmussen, P.; Tareghian, R.
Evaluating the utility of dynamical downscaling in agricultural impacts projections.
Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling-nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output-to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455
Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J
Constrained dynamical downscaling for assessment of climate impacts
NASA Astrophysics Data System (ADS)
Harkey, M.; Holloway, T.
Performance assessment of three convective parameterization schemes in WRF for downscaling summer rainfall over South Africa
NASA Astrophysics Data System (ADS)
Austral summer rainfall over the period 1991/1992 to 2010/2011 was dynamically downscaled by the weather research and forecasting (WRF) model at 9 km resolution for South Africa. Lateral boundary conditions for WRF were provided from the European Centre for medium-range weather (ECMWF) reanalysis (ERA) interim data. The model biases for the rainfall were evaluated over the South Africa as a whole and its nine provinces separately by employing three different convective parameterization schemes, namely the (1) Kain-Fritsch (KF), (2) Betts-Miller-Janjic (BMJ) and (3) Grell-Devenyi ensemble (GDE) schemes. All three schemes have generated positive rainfall biases over South Africa, with the KF scheme producing the largest biases and mean absolute errors. Only the BMJ scheme could reproduce the intensity of rainfall anomalies, and also exhibited the highest correlation with observed interannual summer rainfall variability. In the KF scheme, a significantly high amount of moisture was transported from the tropics into South Africa. The vertical thermodynamic profiles show that the KF scheme has caused low level moisture convergence, due to the highly unstable atmosphere, and hence contributed to the widespread positive biases of rainfall. The negative bias in moisture, along with a stable atmosphere and negative biases of vertical velocity simulated by the GDE scheme resulted in negative rainfall biases, especially over the Limpopo Province. In terms of rain rate, the KF scheme generated the lowest number of low rain rates and the maximum number of moderate to high rain rates associated with more convective unstable environment. KF and GDE schemes overestimated the convective rain and underestimated the stratiform rain. However, the simulated convective and stratiform rain with BMJ scheme is in more agreement with the observations. This study also documents the performance of regional model in downscaling the large scale climate mode such as El Niño Southern Oscillation (ENSO) and subtropical dipole modes. The correlations between the simulated area averaged rainfalls over South Africa and Nino3.4 index were -0.66, -0.69 and -0.49 with KF, BMJ and GDE scheme respectively as compared to the observed correlation of -0.57. The model could reproduce the observed ENSO-South Africa rainfall relationship and could successfully simulate three wet (dry) years that are associated with La Niña (El Niño) and the BMJ scheme is closest to the observed variability. Also, the model showed good skill in simulating the excess rainfall over South Africa that is associated with positive subtropical Indian Ocean Dipole for the DJF season 2005/2006.
Ratna, Satyaban B.; Ratnam, J. V.; Behera, S. K.; Rautenbach, C. J. deW.; Ndarana, T.; Takahashi, K.; Yamagata, T.
Rainfall Downscaling Conditional on Upper-air Variables: Assessing Rainfall Statistics in a Changing Climate
NASA Astrophysics Data System (ADS)
Due to its intermittent and highly variable character, and the modeling parameterizations used, precipitation is one of the least well reproduced hydrologic variables by both Global Climate Models (GCMs) and Regional Climate Models (RCMs). This is especially the case at a regional level (where hydrologic risks are assessed) and at small temporal scales (e.g. daily) used to run hydrologic models. In an effort to remedy those shortcomings and assess the effect of climate change on rainfall statistics at hydrologically relevant scales, Langousis and Kaleris (2013) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables. The developed downscaling scheme was tested using atmospheric data from the ERA-Interim archive (http://www.ecmwf.int/research/era/do/get/index), and daily rainfall measurements from western Greece, and was proved capable of reproducing several statistical properties of actual rainfall records, at both annual and seasonal levels. This was done solely by conditioning rainfall simulation on a vector of atmospheric predictors, properly selected to reflect the relative influence of upper-air variables on ground-level rainfall statistics. In this study, we apply the developed framework for conditional rainfall simulation using atmospheric data from different GCM/RCM combinations. This is done using atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com), and daily rainfall measurements for an intermediate-sized catchment in Italy; i.e. the Flumendosa catchment. Since GCM/RCM products are suited to reproduce the local climatology in a statistical sense (i.e. in terms of relative frequencies), rather than ensuring a one-to-one temporal correspondence between observed and simulated fields (i.e. as is the case for ERA-interim reanalysis data), we proceed in three steps: a) we use statistical tools to establish a linkage between ERA-Interim upper-air atmospheric forecasts and climate model results, b) check and validate the stochastic downscaling scheme for the period when precipitation measurements are available, and c) simulate synthetic rainfall series based on future climate projections of upper-air indices. The obtained results shed light to the effects of climate change on the statistical structure of rainfall. Acknowledgments: The research project is implemented within the framework of the Action "Supporting Postdoctoral Researchers" of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.
Langousis, Andreas; Deidda, Roberto; Marrocu, Marino; Kaleris, Vassilios
Design of a regional climate modelling projection ensemble experiment - NARCliM
NASA Astrophysics Data System (ADS)
Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.
Evans, J. P.; Ji, F.; Lee, C.; Smith, P.; Argüeso, D.; Fita, L.
Large-Scale Weather Generator for Downscaling Precipitation
NASA Astrophysics Data System (ADS)
Well parametrized distributed precipitation-runoff models are able to correctly quantify hydrological state variables (e.g. streamflow, soil moisture, among others) for the past decades. In order to estimate future risks associated with hydrometeorological extremes, it is necessary to incorporate information about the future weather and climate. A common approach is to downscale Regional Climate Model (RCM) projections. Therefore, various statistical downscaling schemes, utilizing diverse mathematical methods, have been developed. One kind of statistical downscaling technique is the so called Weather Generator (WG). These algorithms provide meteorological time series as the realization of a stochastic process. First, single- and multi-site models were developed. Recently, however WG at sub-daily scales and on gridded spatial resolution have captured the interest because of the new development in distributed hydrological modelling. A standard approach for a multi-site WG is to sample a multivariate normal process for all locations. Doing so, it is necessary to calculate the Cholesky factor of the cross-covariance matrix to guarantee a spatially consistent sampling. In general, gridded WGs are an extension of multi-site WGs to larger domains (i.e. >10000 grid cells). On these large grids, it is not possible to accurately determine the Cholesky factor and further enhancements are required. In this work, a framework for a WG is proposed, which provides meteorological time-series on a large scale grid, e.g. 4 km grid of Germany. It employs a sequential Gaussian simulation method, conditioning the value of a grid cell only on a neighborhood, not on the whole field. This methodology is incorporated into a multi-scale downscaling scheme, which is able to provide precipitation data sets at different spatial and temporal resolutions, ranging from 4 km to 32 km, and from days to months, respectively. This framework uses a copula approach for spatial downscaling, exploiting the strong dependence between different spatial scales, and a multiplicative cascade approach for the temporal disaggregation. This study incorporates a gridded, daily data set for the domain of Germany at a 4 km resolution. The data set was interpolated by external drift kriging of station data from the German Weather Service (DWD) and spans over the time period from 1961 to 2000. The data set was aggregated to the different spatio-temporal scales resolutions investigated in this study. The proposed methodology provides precipitation time series at the resolution and grid sizes required by large hydrological application (at national level). First results indicate that the framework is able to consistently preserve precipitation statistics including variability at multiple spatio-temporal resolutions. Nevertheless, it has to be investigated, whether rainfall extremes are correctly represented.
Thober, Stephan; Samaniego, Luis; Bardossy, Andras
Precipitation Prediction in North Africa Based on Statistical Downscaling
NASA Astrophysics Data System (ADS)
Although Global Climate Models (GCM) outputs should not be used directly to predict precipitation variability and change at the local scale, GCM projections of large-scale features in ocean and atmosphere can be applied to infer future statistical properties of climate at finer resolutions through empirical statistical downscaling techniques. A number of such downscaling methods have been proposed in the literature, and although all of them have advantages and limitations depending on the specific downscaling problem, most of them have been developed and tested in developed countries. In this research, we explore the use of statistical downscaling to generate future local precipitation scenarios in different locations in Northern Africa, where available data is sparse and missing values are frequently observed in the historical records. The presence of arid and semiarid regions in North African countries and the persistence of long periods with no rain pose challenges to the downscaling exercise since normality assumptions may be a serious limitation in the application of traditional linear regression methods. In our work, the development of monthly statistical relationships between the local precipitation and the large-scale predictors considers common Empirical Orthogonal Functions (EOFs) from different NCAR/Reanalysis climate fields (e.g., Sea Level Pressure (SLP) and Global Precipitation). GCM/CMIP5 data is considered in the predictor data set to analyze the future local precipitation. Both parametric (e.g., Generalized Linear Models (GLM)) and nonparametric (e,g,, Bootstrapping) approaches are considered in the regression analysis, and different spatial windows in the predictor fields are tested in the prediction experiments. In the latter, seasonal spatial cross-covariance between predictant and predictors is estimated by means of a teleconnections algorithm which was implemented to define the regions in the predictor domain that better captures the variability of the observed local process. Also, a split-window approach is used in the cross-validation stage for comparison purposes of the monthly regression schemes, and different pre-processing alternatives of the precipitation records are implemented to reduce the strong skewness observed in the periodic distribution functions. Preliminary results show that bootstrapping approaches like those based on K-Nearest Neighbors (K-NN) resampling improves the preservation of the historical variability, for which the GLM methods exhibit important limitations. It has been also observed the important role that plays both the teleconnections analysis and the normalization pre-processing in the prediction skill. It is expected that the methodologies from this research can be extrapolated to other regions and time scales for the study of climate change impact and water resources management.
Molina, J. M.; Zaitchik, B.
Comparative Assessment of Statistical Downscaling Methods for Precipitation in Florida
NASA Astrophysics Data System (ADS)
Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by General Circulation Models (GCMs). GCMs in general are capable in capturing the large-scale circulation patterns and correctly model smoothly varying fields such as surface pressure, but it is extremely unlikely that these models properly reproduce non-smooth fields such as precipitation. This paper presents and compares different statistical downscaling methods involving Multiple Linear Regression (MLR), Positive Coefficient Regression (PCR), Stepwise Regression (SWR) and Support Vector Machine (SVM) for estimation of rainfall in the state of Florida, USA, which is considered to be a climatically sensitive region. The explanatory variables/predictors used in the current study are mean sea-level pressure, air temperature, geo-potential height, specific humidity, U-wind and V-wind. Principal Component Analysis (PCA) and Fuzzy C-Means (FCM) clustering techniques are used to reduce the dimensionality of the dataset and identify the circulation patterns on precipitation in different clusters. Downscaled precipitation data obtained from widely used Bias-Correction Spatial Disaggregation (BCSD) downscaling technique is compared along with the other downscaling methods. The performance of the models is evaluated using various performance measures and it was found that the SVM model performed better than all the other models in reproducing most monthly rainfall statistics at 18 locations. Output from the third generation Canadian Global Climate Model (CGCM3) GCM for A1B scenario was used for future precipitation projection. For the projection period 2001-2010, MLR was used and evaluated as a substitute to the traditional spatial interpolation linking the variables at the GCM grid to NCEP grid scale. It has been found that the choice of linking variables from GCM to NCEP grid by MLR yielded superior statistics at most of the stations (12 out of 18) and show a better reproduction of the monthly precipitation.
Goly, A.; Teegavarapu, R. S.
NSDL National Science Digital Library
Ensemble is a NSDL Pathways project working to establish a national, distributed digital library for computing education. The project is building a distributed portal providing access to a broad range of existing educational resources for computing while preserving the collections and their associated curation processes. The developers want to encourage contribution, use, reuse, review and evaluation of educational materials at multiple levels of granularity and seek to support the full range of computing education communities including computer science, computer engineering, software engineering, information science, information systems and information technology as well as other areas often called computing + X, or X informatics.
Orthogonal polynomial ensembles in probability theory
We survey a number of models from physics, statistical mechanics, probability theory and combinatorics, which are each described in terms of an orthogonal polynomial ensemble. The most prominent example is apparently the Hermite ensemble, the eigenvalue distribution of the Gaussian Unitary Ensemble (GUE), and other well-known ensembles known in random matrix theory like the Laguerre ensemble for the spectrum of
Wolfgang Koenig
Density of states for Gaussian unitary ensemble, Gaussian orthogonal ensemble, and interpolating ensembles through supersymmetric approach
We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal Ensemble, and then apply saddle-point analysis to give a new derivation of the 1/N-correction to Wigner's law. This extends the work of Disertori on the Gaussian Unitary Ensemble. We also apply our method to the interpolating ensembles of Mehta–Pandey.
Shamis, Mira, E-mail: firstname.lastname@example.com [Department of Mathematics, Princeton University, Princeton New Jersey 08544, USA and Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540 (United States)] [Department of Mathematics, Princeton University, Princeton New Jersey 08544, USA and Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540 (United States)
We construct the integration measure over the moduli space of an arbitrary number of N kinds of dyons of the pure SU(N) gauge theory at finite temperatures. The ensemble of dyons governed by the measure is mathematically described by a (supersymmetric) quantum field theory that is exactly solvable and is remarkable for a number of striking features: (i) The free energy has the minimum corresponding to the zero average Polyakov line, as expected in the confining phase; (ii) the correlation function of two Polyakov lines exhibits a linear potential between static quarks in any N-ality nonzero representation, with a calculable string tension roughly independent of temperature; (iii) the average spatial Wilson loop falls off exponentially with its area and the same string tension; (iv) at a critical temperature, the ensemble of dyons rearranges and deconfines; and (v) the estimated ratio of the critical temperature to the square root of the string tension is in excellent agreement with the lattice data.
Diakonov, Dmitri; Petrov, Victor [St. Petersburg NPI, Gatchina, 188 300, St. Petersburg (Russian Federation)
Multinomial logistic regression ensembles.
This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model. PMID:23611203
Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J
Stochastic downscaling of numerically simulated spatial rain and cloud fields using a transient multifractal approach
NASA Astrophysics Data System (ADS)
Atmospheric fields can be extremely variable over wide ranges of spatial scales, with a scale ratio of 109-1010 between largest (planetary) and smallest (viscous dissipation) scale. Furthermore atmospheric fields with strong variability over wide ranges in scale most likely should not be artificially split apart into large and small scales, as in reality there is no scale separation between resolved and unresolved motions. Usually the effects of the unresolved scales are modeled by a deterministic bulk formula representing an ensemble of incoherent subgrid processes on the resolved flow. This is a pragmatic approach to the problem and not the complete solution to it. These models are expected to underrepresent the small-scale spatial variability of both dynamical and scalar fields due to implicit and explicit numerical diffusion as well as physically based subgrid scale turbulent mixing, resulting in smoother and less intermittent fields as compared to observations. Thus, a fundamental change in the way we formulate our models is required. Stochastic approaches equipped with a possible realization of subgrid processes and potentially coupled to the resolved scales over the range of significant scale interactions range provide one alternative to address the problem. Stochastic multifractal models based on the cascade phenomenology of the atmosphere and its governing equations in particular are the focus of this research. Previous results have shown that rain and cloud fields resulting from both idealized and realistic numerical simulations display multifractal behavior in the resolved scales. This result is observed even in the absence of scaling in the initial conditions or terrain forcing, suggesting that multiscaling is a general property of the nonlinear solutions of the Navier-Stokes equations governing atmospheric dynamics. Our results also show that the corresponding multiscaling parameters for rain and cloud fields exhibit complex nonlinear behavior depending on large scale parameters such as terrain forcing and mean atmospheric conditions at each location, particularly mean wind speed and moist stability. A particularly robust behavior found is the transition of the multiscaling parameters between stable and unstable cases, which has a clear physical correspondence to the transition from stratiform to organized (banded) convective regime. Thus multifractal diagnostics of moist processes are fundamentally transient and should provide a physically robust basis for the downscaling and sub-grid scale parameterizations of moist processes. Here, we investigate the possibility of using a simplified computationally efficient multifractal downscaling methodology based on turbulent cascades to produce statistically consistent fields at scales higher than the ones resolved by the model. Specifically, we are interested in producing rainfall and cloud fields at spatial resolutions necessary for effective flash flood and earth flows forecasting. The results are examined by comparing downscaled field against observations, and tendency error budgets are used to diagnose the evolution of transient errors in the numerical model prediction which can be attributed to aliasing.
Nogueira, M.; Barros, A. P.; Miranda, P. M.
A seasonal hydrologic ensemble prediction system for water resource management
NASA Astrophysics Data System (ADS)
A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.
Luo, L.; Wood, E. F.
Downscaling of Minimum Surface Temperature in the Semi-arid Great Basin Region and Implications for Bio-geophysical Processes
NASA Astrophysics Data System (ADS)
This study addresses downscaling methodology for monthly surface air temperature from global climate model (GCM) horizontal grid resolutions (> 100 km) to regional scales (< 10 km) appropriate for climate impact studies. Preliminary hindcast analysis for the period 1950-2008 indicated that the minimum temperatures extracted from the GCMs at 46 individual stations in Nevada show correct seasonal trends, but the monthly mean minima are significantly underestimated compared to three observational networks (Western Regional Climate Center (WRCC), DRI), National Climate Data Center (NCDC), and Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data sets. The daily mean surface air temperature, from the three GCMs (NCAR-CCSM3, ECHAM5, and CSIRO-Mk3.5) and a regional climate model (RCM) using the Weather Research and Forecasting (WRF) model forced by the CCSM3 outputs, is generally under-predicted with root-mean-square errors as large as 6 K on an annual scale. The underlying premise of this study is that changes in minimum temperature are manifested on the landscape via changes in hydrological parameters viz., runoff timing and evapotranspiration rates, ecological parameters viz., rates of invasion of exotic species and fire hazards, and socio-economic parameters viz., urban energy use. The systematic error or bias in surface minimum temperature simulated by the GCMs and their ensembles under designated Intergovernmental Panel on Climate Change (IPCC) climate change scenarios (A1B, A2, and B1) is investigated to assess and substantiate this argument. The present study employs the downscaling technique of bias correction and spatial disaggregation (BCSD) to improve GCM representation of monthly minimum temperature characteristics at local and regional scales which are critical to properly quantify for ecologic, hydrologic, and socio-economic forecasting under future climate change scenarios.
Hatchett, B. J.; Vellore, R.; Koracin, D.
Semi-supervised ensemble tracking
In this paper, we propose a semi-supervised ensemble tracking approach under the framework of particle filter. The particle filter is used not only for object searching, but also for unlabelled sample generation. By adopting the semi-supervised learning technology, these unlabelled samples which are generated online are utilized to progressively modify the classifier and make the ensemble tracker to be more
Huaping Liu; Fuchun Sun
An Iterative Ensemble Kalman Filter
The ensemble Kalman filter is a Monte Carlo method for state estimation of nonlinear models, developed as an alternative or improve- ment of the extended Kalman filter. In this technical note we introduce an iterative extension to the ensemble Kalman filter. Iterations are introduced to improve the estimates in the cases where the relationship between the model and observations is
Rolf J. Lorentzen; Geir Naevdal
Resampling the ensemble Kalman filter
NASA Astrophysics Data System (ADS)
Ensemble Kalman filters (EnKF) based on a small ensemble tend to provide collapse of the ensemble over time. It is demonstrated that this collapse is caused by positive coupling of the ensemble members due to use of the estimated Kalman gain for the update of all ensemble members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution in the conditioning step. In the analytically tractable Gauss-linear model finite sample distributions for all covariance matrix estimates involved in the Kalman gain estimate are known and hence exact Kalman gain resampling can be done. For the general nonlinear case we introduce the resampling ensemble Kalman filter (ResEnKF) algorithm. The resampling strategy in the algorithm is based on bootstrapping of the ensemble and Monte Carlo simulation of the likelihood model. We also define a semi-parametric and parametric version of the resampling ensemble Kalman filter algorithm. An empirical study demonstrates that ResEnKF provides more reliable prediction intervals than traditional EnKF, on the cost of somewhat less accuracy in the point predictions.
Myrseth, Inge; Sætrom, Jon; Omre, Henning
The Polyanalytic Ginibre Ensembles
NASA Astrophysics Data System (ADS)
For integers n, q=1,2,3,… , let Pol n, q denote the -linear space of polynomials in z and , of degree ? n-1 in z and of degree ? q-1 in . We supply Pol n, q with the inner product structure of the resulting Hilbert space is denoted by Pol m, n, q . Here, it is assumed that m is a positive real. We let K m, n, q denote the reproducing kernel of Pol m, n, q , and study the associated determinantal process, in the limit as m, n?+? while n= m+O(1); the number q, the degree of polyanalyticity, is kept fixed. We call these processes polyanalytic Ginibre ensembles, because they generalize the Ginibre ensemble—the eigenvalue process of random (normal) matrices with Gaussian weight. There is a physical interpretation in terms of a system of free fermions in a uniform magnetic field so that a fixed number of the first Landau levels have been filled. We consider local blow-ups of the polyanalytic Ginibre ensembles around points in the spectral droplet, which is here the closed unit disk . We obtain asymptotics for the blow-up process, using a blow-up to characteristic distance m -1/2; the typical distance is the same both for interior and for boundary points of . This amounts to obtaining the asymptotical behavior of the generating kernel K m, n, q . Following (Ameur et al. in Commun. Pure Appl. Math. 63(12):1533-1584, 2010), the asymptotics of the K m, n, q are rather conveniently expressed in terms of the Berezin measure (and density) [Equation not available: see fulltext.] For interior points | z|<1, we obtain that in the weak-star sense, where ? z denotes the unit point mass at z. Moreover, if we blow up to the scale of m -1/2 around z, we get convergence to a measure which is Gaussian for q=1, but exhibits more complicated Fresnel zone behavior for q>1. In contrast, for exterior points | z|>1, we have instead that , where is the harmonic measure at z with respect to the exterior disk . For boundary points, | z|=1, the Berezin measure converges to the unit point mass at z, as with interior points, but the blow-up to the scale m -1/2 exhibits quite different behavior at boundary points compared with interior points. We also obtain the asymptotic boundary behavior of the 1-point function at the coarser local scale q 1/2 m -1/2.
Haimi, Antti; Hedenmalm, Haakan
"Uncertainty in downscaling using high-resolution observational datasets"
NASA Astrophysics Data System (ADS)
In order to bridge the gap between the resolution of global climate modeling efforts and the scale that decision-makers work at statistical downscaling is often employed. The performance of any statistical downscaling is dependant on the quality of the observational data at the location(s) of downscaling (whether gridded or point-source). However, discussions of the assumptions made during statistical downscaling, such as the stationariness of the relationships between predictor(s) and predictand, normally do not acknowledge the uncertainty introduced by the observational dataset. Many observational datasets do not have the erroneous temporal discontinuities caused by non-climatic biases, such as instrument changes or station relocations, diminished by a homogenization process. Moreover stations included within the underlying networks of high-resolution gridded datasets are typically not required to meet high standards of quality. Hence we evaluated three popular observational climate datasets, of the high-resolution gridded type, for their depiction of temperature values over the span of the datasets and the continental U.S. This was done using the homogenized United States Historical Climatology Network (USHCN) dataset version 2.0. The summer average temperatures at selected stations within the USHCN were compared to those created by interpolating gridpoints to the locations of those stations. The relationships these datasets have with more traditional climate datasets (e.g. the GISS, CRU, USHCN) have not formally been evaluated. The comparisons were not to judge which dataset was closest aligned with the USHCN dataset, but rather to discuss the common features (across datasets) of the residuals (i.e. differences with the USHCN dataset). We found that the lack of homogenization was a primary cause of the residuals, but that proxies for the non-climatic biases were not as well related to the residuals as expected. This was due in part to the gridding process that spatially extends the effects of non-climatic biases. Results suggest that while the residuals were statistically significant at the continental, regional and sub-regional spatial scales; they were particularly large at the smaller scales. The residuals also varied through time, and thus the trends in these datasets were often inaccurate and the temporal average residuals depended on the time period of focus. An evaluation of these datasets' ability to bias correct and spatially disaggregate as a function of grid box size, is being undertaken and will be presented. This is facilitated by calculating per grid box the average residual and the intra-grid variance of the residuals. This uncertainty within the observational data is likely existent in any downscaling product using these datasets to facilitate the downscaling. Thus this is an uncertainty not often discussed and even less quantified.
Oswald, E.; Rood, R. B.
SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output
Hydrological impacts of climate change are assessed by downscaling the General Circulation Model (GCM) outputs of predictor variables to local or regional scale hydrologic variables (predictand). Support Vector Machine (SVM) is a machine learning technique which is capable of capturing highly nonlinear relationship between predictor and predictand and thus performs better than conventional linear regression in transfer function-based downscaling modeling.
Subimal Ghosh
The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods
The derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with
Eduardo Zorita; Hans von Storch
Statistical and dynamical downscaling of precipitation over Spain from DEMETER seasonal forecasts
Statistical and dynamical downscaling methods are tested and compared for downscaling seasonal precipitation forecasts over Spain from two DEMETER models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Meteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical
E. Díez; C. Primo; J. A. García-Moya; J. M. Gutiérrez; B. Orfila
Sensitivity of Local Daily Temperature Change Estimates to the Selection of Downscaling Models and Predictors
A number of statistical downscaling models are applied to the Canadian Climate Centre general circulation model (CCCM) outputs to provide climate change estimates for local daily surface temperature at a network of 39 stations in central and western Europe. Several different linear downscaling methods (multiple linear regression of gridded values, multiple linear regression of principal components, canonical correlation analysis) are
Radan Huth
Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps
This paper compares six statistical downscaling models (SDMs) and three regional climate models (RCMs) in their ability to downscale daily precipitation statistics in a region of complex topography. The six SDMs include regression methods, weather typing methods, a conditional weather generator, and a bias correction and spatial disaggregation approach. The comparison is carried out over the European Alps for current
J. Schmidli; C. M. Goodess; C. Frei; M. R. Haylock; Y. Hundecha; J. Ribalaygua; T. Schmith
Downscaling of rainfall in Peru using Generalised Linear Models
NASA Astrophysics Data System (ADS)
The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the downscaling model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim downscaling model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim downscaling model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.
Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.
Comparing statistical downscaling methods: From simple to complex (Invited)
NASA Astrophysics Data System (ADS)
Evaluating future regional and local climate impacts often requires high-resolution projections at the scale of human society and the natural environment. To overcome the gap between global climate model (GCM) output and user needs, a broad range of statistical downscaling methods has been developed, ranging from simple back-of-the-envelope 'delta' calculations to complex statistical regression models. Here, by treating 25km output from the GFDL-HiRAM-C360 model as 'observations' for both past and future periods and coarsened 200km versions of the same fields as 'models', we quantify the ability of both monthly and daily versions of three different statistical downscaling methods (delta method, quantile mapping, and asynchronous quantile regression) to reproduce high-resolution projections of current and future mean and extreme temperature and precipitation. For temperature, initial results indicate that all methods are comparable for downscaling mean seasonal values. For both hot and cold extremes, however, methods that do not resolve the observed and projected future distribution of daily values produce significant biases particularly at higher latitudes. For precipitation, the simple delta approach produces large biases across the entire distribution (with the exception of the western U.S.) that become largely negative by end of century. For projected changes in precipitation values below the 90th quantile, both daily and monthly quantile mapping and quantile regression perform adequately. For high precipitation extremes, monthly quantile mapping produces large positive biases relative to the high-resolution dynamical model simulations; only daily quantile regression produces significantly lower biases. These results show clear limitations on simpler methods when simulating projected changes in extreme temperature and precipitation as compared to high-resolution dynamical simulations of climate change at the regional scale.
Stoner, A. K.; Hayhoe, K.; Dixon, K. W.; Lanzante, J.; Radhakrishnan, A.
Metastability within the generalized canonical ensemble
NASA Astrophysics Data System (ADS)
We discuss a property of our recently introduced generalized canonical ensemble [M. Costeniuc, R.S. Ellis, H. Touchette, B. Turkington, The generalized canonical ensemble and its universal equivalence with the microcanonical ensemble, J. Stat. Phys. 119 (2005) 1283]. We show that this ensemble can be used to transform metastable or unstable (nonequilibrium) states of the standard canonical ensemble into stable (equilibrium) states within the generalized canonical ensemble. Equilibrium calculations within the generalized canonical ensemble can thus be used to obtain information about nonequilibrium states in the canonical ensemble.
Touchette, H.; Costeniuc, M.; Ellis, R. S.; Turkington, B.
Moroccan precipitation in a regional climate change simulation, evaluating a statistical downscaling approach
NASA Astrophysics Data System (ADS)
Morocco is located at the extreme north-west of Africa between 20 and 37° N. According to the aridity index of De Martonne classification, Moroccan climate varies from sub-humid in the north to arid in the south. The country has experienced several drought events which have had marked impacts on socio-economic sectors and national economy (1940-1945, 1980-1985, 1994-1995 …). During a dry year, the deficit of rainfall can exceed 60% of the climatological value. Rainfall amounts registered show a negative trend at national and regional scales. The drought seems to become more persistent over time. At the same time, the total number of wet days shows a negative trend revealing an increase in the annual dry day number. Many regions became more arid (According to the aridity index of De Martonne) between 1961 and 2008: namely Oujda, Taza, Kenitra, Rabat, Meknès. In order to evaluate climate change impacts on Moroccan winter precipitation, future climate conditions in Morocco under the SRES scenario A1B, are studied by using two 30-year time-slice simulations performed by the variable resolution configuration of the GCM ARPEGE-Climat. The spatial resolution ranges between 50 and 60 km over the country. This high resolution scenarios exhibit for the period 2021-2050 a change in the precipitation distribution and in extreme events. In particular, the precipitation amounts and the occurrence frequency of wet days will decrease in the scenario on all the fourteen stations considered. In terms of extreme events, the maximum dry spell length increases in nearly all the stations and the frequency of high precipitation events is projected to decrease. The evolution of highest percentiles of precipitation distribution does not go, however, in the same sense everywhere. Assessment of a range of uncertainties due to climate modelling has been done by using present-day and SRES scenario A1B data issued from 10 ENSEMBLES-RCMs. This shows that ARPEGE-Climate results are in the range covered by these RCMs for all the climate indices considered. In order to validate, in the case of Moroccan winter precipitation, a statistical downscaling approach that uses large scale fields to construct local scenarios of future climate change, the link between north Atlantic weather regimes and Moroccan local precipitation has been investigated, in terms of precipitation average, and the frequencies of occurrence of wet and intense precipitation days. The robustness of the statistical approach considered is evaluated using the outputs of ARPEGE-Climate and also those of the 10 ENSEMBLES-RCMs.
Driouech, F.; Déqué, M.; Sánchez-Gómez, E.
Regional climate model ensemble techniques: Towards higher spatial resolution probabilistic climate scenarios. (Invited)
NASA Astrophysics Data System (ADS)
In the application of regional climate models (RCMs) to assessments of climate change for a particular region, the emphasis is often on increasing spatial resolution to gain improvements over global climate models (GCMs). This methodology is employed due to the high computational cost of high spatial resolution GCM simulations. RCMs have proven a useful tool for dynamically downscaling GCM data for impacts studies. In this approach, typically only one simulation of a given climate scenario is performed, even though it has been shown that RCM simulations can be somewhat sensitive to minor changes in their boundary conditions. With ensembles of scenarios now the standard for GCMs, we investigate the use of large numbers of ensemble members with the RCM RegCM3. At a moderate spatial resolution (50 km), for a domain centered over western North America, we generated 50 ensemble members of a given climate scenario, in this case we use NCEP/DOE reanalysis as the boundary conditions data. We examine several different approaches for generating the ensemble members. In the first approach, we vary sea surface temperatures (SSTs) within ± 2% of their value (up to about 6 K) at each 6-hour boundary condition slice. In the second approach we use a more conservative ± 1 K variation in SSTs. In the third approach, we vary all boundary condition fields by ± 0.1% in addition to a ± 0.1% variation in SSTs. The results of the ensemble can then be bootstrapped to generate a probabilistic representation of that climate scenario. The application of these data to climate impacts at the regional scale offers advantages over ensembles of GCMs and single realization RCM scenarios.
Snyder, M. A.; O'Brien, T. A.
Developing Climate-Informed Ensemble Streamflow Forecasts over the Colorado River Basin
NASA Astrophysics Data System (ADS)
As the impacts of climate change are realized, the assumption of hydrometeorologic stationarity embedded within many hydrologic models is likely no longer valid over the Colorado River Basin. As such, resource managers in the region have begun to request increasingly more information to support decisions, specifically with regards to the incorporation of climate change information and operational risk. To this end, ensemble methodologies have become increasingly popular among the scientific and forecasting communities, and resource managers have begun to incorporate this information into decision support tools and operational models. Over the Colorado River Basin, reservoir operations are determined, in part, by forecasts issued by the Colorado Basin River Forecast Center (CBRFC). Traditionally, the CBRFC produces both single value and ensemble forecasts for use by resource managers in their operational decision-making process. These ensemble forecasts are currently driven by a combination of daily updating model states used as initial conditions and weather forecasts plus historical meteorological information used to generate forecasts with the assumption that past hydroclimatological conditions are representative of future hydroclimatology. Recent efforts have produced future projections of climate information over the Colorado River Basin. In this study, the historical climatology typically used as input to the CBRFC forecast model is adjusted to represent future projections of climate based on data developed by the Coupled Model Intercomparison Project 5 that has been bias-corrected and spatially downscaled. Ensemble streamflow forecasts reflecting the impacts of climate change are then developed. This ensemble forecast may then be input into a reservoir operations planning model, providing resource managers with ensemble information regarding future water supply, availability, and reservoir management aiding in the determination of possible implications for resource management.
Miller, W. P.; Werner, K.; Stokes, M.
Assessing Climate change impacts on river basins in New Zealand using model based downscaling, statistical downscaling and regional climate modelling
NASA Astrophysics Data System (ADS)
Spatial resolution of General Circulation Models (GCMs) is too coarse to represent regional climate variations at the scales required for environmental impact assessments in New Zealand. Downscaling is necessary for climate change impact analyses that seek to constrain regional climate by information from global climate models. It is particularly important in the New Zealand context, as given maritime, topographic and convective climate processes. As a result local to regional scale variability is not always well represented by the broader global scale features simulated by GCMs. Three techniques are available to generate climate change information that can be used as input of environmental models: i) Downscaling to the New Zealand Virtual Climate Station Network grid (Tait et al, 2006); ii) Semi-empirical (statistical) downscaling (SDS) of GCM outputs; and iii) Regional climate models (RCMs) nested within a GCM. In this study, we compare the downstream impact of the three techniques for three different emission scenarios as characterised in the IPCC Fourth Assessment (B1-low emission, A1B- middle of the road, and A2-high emission scenario) and two of the 12 GCM models used in New Zealand (UKMO_HADCM3 and MPI_ECHAM5). Our study will focus on surface water hydrological responses (ie discharge, infiltration, evaporation, snow storage) for a number of river basins across the North and South Island of New Zealand. The analysis will compare the current situation (1980-1999) with two future time periods (2030-2049 and 2080-2099) and will draw recommendation regarding climate change impact uncertainty and its communication to decision makers.
Zammit, C.; Diettrich, J.; Sood, A.
Statistical downscaling of extreme daily precipitation using extreme value theory
NASA Astrophysics Data System (ADS)
Although present day weather forecast models usually cannot provide realistic descriptions of local and particularly extreme weather conditions, they provide reliable forecasts of the atmospheric circulation that encompasses the sub-scale processes leading to extremes. Hence, forecasts of extreme events can only be achieved through a combination of dynamical and statistical analysis methods, where a stable and significant statistical model based on a-priori physical reasoning establishes a-posterior a statistical-dynamical model between the local extremes and the large scale circulation. Here we present the development and application of such a statistical model calibration (downscaling) on the basis of extreme value theory, in order to derive probabilistic estimates for (extreme) local precipitation. Besides a semi-parametric approach that employs censored quantile regression we use parametric extreme value distributions to derive conditional quantile estimates. The performance of two parametric approaches is compared, which use a Poisson point process with non-stationary parameters but a constant threshold, and the non-stationary generalized Pareto distribution and a variable threshold. The downscaling applies to ERA40 reanalysis, in order to derive estimates of the conditional quantiles of daily precipitation accumulations at more than 2000 German weather stations.
Friederichs, P.
Future changes in the West African Monsoon: A COSMO-CLM and RCA4 multimodel ensemble study
NASA Astrophysics Data System (ADS)
In this multi-model multi-ensemble study, we intercompare results from two regional climate simulation ensembles to see how well they reproduce the known main features of the West African Monsoon (WAM). Each ensemble was created under the ongoing CORDEX-Africa activities by using the regional climate models (RCA4 and COSMO-CLM) to downscale four coupled atmosphere ocean general circulation models (AOGCMs), namely, CNRM-CM5, HadGEM2-ES, EC-EARTH, and MPI-ESM-LR. Spatial resolution of the driving AOGCMs varies from about 1° to 3° while all regional simulations are at the same 0.44° resolution. Future climate projections from the RCP8.5 scenario are analyzed and inter-compared for both ensembles in order to assess deviations and uncertainties. The main focus in our analysis is on the projected WAM rainy season statistics. We look at projected changes in onset and cessation, total precipitation and temperature toward the end of the century (2071-2100) for different time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. Differences in the ensemble projections are linked to the parameterizations employed in both regional models and the influence of this is discussed.
Anders, Ivonne; Gbobaniyi, Emiola
The performance factors of clustering ensembles
The accomplishments on classifier ensembles originate the studies of clustering ensembles. In this study the factors on performance of clustering ensembles (clustering algorithm, the number of features used in clustering, the size of ensemble, the decision combining algorithm) are investigated and compared on 15 benchmark datasets. The decisions of clustering algorithms based on different feature subsets are combined. On the
M. Fatih Amasyali; Okan Ersoy
We survey a number of models from physics, statistical mechanics, probability theory and combinatorics, which are each described in terms of an orthogonal polynomial en- semble. The most prominent example is apparently the Hermite ensemble, the eigenvalue dis- tribution of the Gaussian Unitary Ensemble (GUE), and other well-known ensembles known in random matrix theory like the Laguerre ensemble for the
Wolfgang Konig
Ensemble Data Assimilation without Perturbed Observations
The ensemble Kalman filter (EnKF) is a data assimilation scheme based on the traditional Kalman filter update equation. An ensemble of forecasts are used to estimate the background-error covariances needed to compute the Kalman gain. It is known that if the same observations and the same gain are used to update each member of the ensemble, the ensemble will systematically
Jeffrey S. Whitaker; Thomas M. Hamill
Measuring similarity between dynamic ensembles of biomolecules.
We present a simple and general approach termed REsemble for quantifying population overlap and structural similarity between ensembles. This approach captures improvements in the quality of ensembles determined using increasing input experimental data-improvements that go undetected when conventional methods for comparing ensembles are used-and reveals unexpected similarities between RNA ensembles determined using NMR and molecular dynamics simulations. PMID:24705474
Yang, Shan; Salmon, Loïc; Al-Hashimi, Hashim M
Ensemble routing for datacenter networks
This paper describes Hash-Based Routing (HBR), an architecture that enhances Ethernet to support dynamic management for multipath networks in scalable datacenters. This work enhances HBR to support flow ensemble management for large-scale networks of arbitrary topology. Ensemble routing eliminates measurement and control for individual flows and instead manages using summary data thus providing a unique capability for reactive datacenter-wide network
Mike Schlansker; Yoshio Turner; Jean Tourrilhes; Alan Karp
Ensemble Learning With Imbalanced Data
We describe an ensemble approach to learning salient spatial regions from arbitrarily\\u000apartitioned simulation data. Ensemble approaches for anomaly detection\\u000aare also explored. The partitioning comes from the distributed processing requirements\\u000aof large-scale simulations. The volume of the data is such that classifiers\\u000acan train only on data local to a given partition. Since the data partition reflects\\u000athe needs
Larry Shoemaker
Ensemble Modeling for Biomedical Applications
In this paper we propose to use ensembles of models constructed using methods of Statistical Learning. The input data for\\u000a model construction consists of real measurements taken in physical system under consideration. Further we propose a program\\u000a toolbox which allows the construction of single models as well as heterogenous ensembles of linear and nonlinear models types.\\u000a Several well performing model
Christian Merkwirth; Jörg Wichard; Maciej Ogorza?ek
Matrix models for beta ensembles
This paper constructs tridiagonal random matrix models for general (beta>0) beta-Hermite (Gaussian) and beta-Laguerre (Wishart) ensembles. These generalize the well-known Gaussian and Wishart models for beta=1,2,4. Furthermore, in the cases of the beta-Laguerre ensembles, we eliminate the exponent quantization present in the previously known models. We further discuss applications for the new matrix models, and present some open problems.
Ioana Dumitriu; Alan Edelman
Meteorological Drought Prediction Using a Multi-Model Ensemble Approach
NASA Astrophysics Data System (ADS)
In the United States, drought is among the costliest natural hazards, with an annual average of 6 billion dollars in damage. Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Started in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the National Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the meteorological drought predictability using the retrospective NMME forecasts for the period from 1982 to 2010. Before predicting SPI, monthly-mean precipitation (P) forecasts from each model were bias corrected and spatially downscaled (BCSD) to regional grids of 0.5-degree resolution over the contiguous United States based on the probability distribution functions derived from the hindcasts. The corrected P forecasts were then appended to the CPC Unified Precipitation Analysis to form a P time series for computing 3-month and 6-month SPIs. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation and root-mean-square errors against the observations, are used to evaluate forecast skill. For P forecasts, errors vary among models and skill generally is low after the second month. All model P forecasts have higher skill in winter and lower skill in summer. In wintertime, BCSD improves both P and SPI forecast skill. Most improvements are over the western mountainous regions and along the Great Lake. Overall, SPI predictive skill is regionally and seasonally dependent. The six-month SPI forecasts are skillful out to four months. For shorter lead months, the ensemble SPI forecast skill is comparable to that based on persistence. The spread of SPI forecasts among models is small, and the predictive skill comes from the observations appended to the P forecasts. For longer lead months, model forecasts contribute to the meteorological drought predictability. The ensemble SPI forecasts have higher skill than those based on persistence and individual models.
Chen, L.; Mo, K. C.; Zhang, Q.; Huang, J.
Decadal Trends in Ensemble Projections
NASA Astrophysics Data System (ADS)
A simple method to rank multi-model ensemble members of CMIP3 simulations by their representation of phases of decadal oscillations is introduced. A period of 22 years (1979-2000) from the 20th century simulations is used to generate ensemble projections of trends for an 11-year (2001-2011) lead time for the SRES A1B scenario. Although greenhouse-gas forcing is identical for all 20th century simulations, the phases of decadal oscillations are quite different. Thus, the suggested minimum requirements for a simple selection criterion for adequate ensemble members are that (a) trends in high-, mid-, and low-latitude zones need to be treated separately and (b) information about the state of teleconnections between the zones needs to be included, when projecting decadal variability and trends in climate. The new method indicates that half (19 out of 38) ensemble members retain their rank when each GCM is treated separately without any assumptions of which model might be superior. Thus, the overall ensemble size can be reduced without a large loss of information but with a greatly reduced range of uncertainty, when only the least well performing ensemble members of each GCM are omitted for an 11-year projection.
Liess, S.; Snyder, P. K.; Kumar, A.; Kumar, V.
Downscaling of climate parameters using Active Learning Method (ALM)
NASA Astrophysics Data System (ADS)
This study is a part of main program RIMAX "risk management of extreme flood events", which concerns itself of extremes floodwater and damage potential in the Bode river basin in Germany with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate downscaling) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear downscaling based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for downscaling in the last decade. In the next steps, considering predictors from the ECHO time series As well as the predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between global and regional scales. These models are verified using checking data and then considering ECHO/REMO models and on the basis of last 1000 years of ECHO, the REMO time series as well as the local data are simulated. These simulated data are used as input-data for the runoff model ARCEGMO.
Sodoudi, S.; Reimer, E.
Impacts of high resolution model downscaling in coastal regions
NASA Astrophysics Data System (ADS)
With model development and cheaper computational resources ocean forecasts are becoming readily available, high resolution coastal forecasting is now a reality. This can only be achieved, however, by downscaling global or basin-scale products such as the MyOcean reanalyses and forecasts. These model products have resolution ranging from 1/16th - 1/4 degree, which are often insufficient for coastal scales, but can provide initialisation and boundary data. We present applications of downscaling the MyOcean products for use in shelf-seas and the nearshore. We will address the question 'Do coastal predictions improve with higher resolution modelling?' with a few focused examples, while also discussing what is meant by an improved result. Increasing resolution appears to be an obvious route for getting more accurate forecasts in operational coastal models. However, when models resolve finer scales, this may lead to the introduction of high-frequency variability which is not necessarily deterministic. Thus a flow may appear more realistic by generating eddies but the simple statistics like rms error and correlation may become less good because the model variability is not exactly in phase with the observations (Hoffman et al., 1995). By deciding on a specific process to simulate (rather than concentrating on reducing rms error) we can better assess the improvements gained by downscaling. In this work we will select two processes which are dominant in our case-study site: Liverpool Bay. Firstly we consider the magnitude and timing of a peak in tide-surge elevations, by separating out the event into timing (or displacement) and intensity (or amplitude) errors. The model can thus be evaluated on how well it predicts the timing and magnitude of the surge. The second important characteristic of Liverpool Bay is the position of the freshwater front. To evaluate model performance in this case, the location, sharpness, and temperature difference across the front will be considered. We will show that by using intelligent metrics designed with a physical process in mind, we can learn more about model performance than by considering 'bulk' statistics alone. R. M. Hoffman and Z. Liu and J-F. Louic and C. Grassotti (1995) 'Distortion Representation of Forecast Errors' Monthly Weather Review 123: 2758-2770
Bricheno, Lucy; Wolf, Judith
Projections of the Ganges-Brahmaputra precipitation-Downscaled from GCM predictors
NASA Astrophysics Data System (ADS)
Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia-the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies.
Pervez, Md Shahriar; Henebry, Geoffrey M.
Ensemble Cannonical Correlation Prediction of Seasonal Precipitation Over the US
NASA Technical Reports Server (NTRS)
This paper presents preliminary results of an ensemble cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.
Lau, William K. M.; Kim, Kyu-Myong; Shen, Samuel; Einaudi, Franco (Technical Monitor)
Fabrication and ab initio study of downscaled graphene nanoelectronic devices
NASA Astrophysics Data System (ADS)
In this paper we first present a new fabrication process of downscaled graphene nanodevices based on direct milling of graphene using an atomic-size helium ion beam. We address the issue of contamination caused by the electron-beam lithography process to pattern the contact metals prior to the ultrafine milling process in the helium ion microscope (HIM). We then present our recent experimental study of the effects of the helium ion exposure on the carrier transport properties. By varying the time of helium ion bombardment onto a bilayer graphene nanoribbon transistor, the change in the transfer characteristics is investigated along with underlying carrier scattering mechanisms. Finally we study the effects of various single defects introduced into extremely-scaled armchair graphene nanoribbons on the carrier transport properties using ab initio simulation.
Mizuta, Hiroshi; Moktadir, Zakaria; Boden, Stuart A.; Kalhor, Nima; Hang, Shuojin; Schmidt, Marek E.; Cuong, Nguyen Tien; Chi, Dam Hieu; Otsuka, Nobuo; Muruganathan, Manoharan; Tsuchiya, Yoshishige; Chong, Harold; Rutt, Harvey N.; Bagnall, Darren M.
NASA Astrophysics Data System (ADS)
CReSiBUC consists of CReSS and SiBUC. This model is able to consider land surface conditions in detail. Especially, the characteristics of urban conditions such as artificial heat and geometry of building height can be considered. In this study, the effect of the physical-based downscaling is investigated by using CReSiBUC. Simulations are carried out around Tokyo Metropolitan Area during 5 summer seasons (from 2003 to 2007). Temperatures at 3 a.m. and Temperatures at 3 p.m. are investigated. It is found that outputs of CReSiBUC are more accurate than temperatures of MANAL. This result suggests the importance of considering urban conditions in detail.
Fujii, Takahiro; Tanaka, Kenji; Souma, Kazuyoshi; Kojiiri, Toshiharu
Dynamical downscaling inter-comparison for high resolution climate reconstruction
NASA Astrophysics Data System (ADS)
In the scope of the project: "High-resolution Rainfall EroSivity analysis and fORecasTing - RESORT", an evaluation of various methods of dynamic downscaling is presented. The methods evaluated range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.
Ferreira, J.; Rocha, A.; Castanheira, J. M.; Carvalho, A. C.
Uncertainty resulting from multiple data usage in statistical downscaling
NASA Astrophysics Data System (ADS)
downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.
Kannan, S.; Ghosh, Subimal; Mishra, Vimal; Salvi, Kaustubh
Downscaling NARCCAP Model Output for Local Level Impact Assessment
NASA Astrophysics Data System (ADS)
There is a great societal interest in assessing the impacts of projected climate change on infrastructure design, such as of dams, bridges and coastal roads. Such impact assessment typically requires future projections of high-resolution time series of temperature, precipitation, solar radiation and related variables. We propose a methodology to downscale projected series from the NARCCAP regional climate model output to daily or hourly inputs at the local point scale. We focus on precipitation and extend the XCDF-T method of Kallache et al. to include estimation of precipitation return levels. We apply the method to locations of the NCDC monitoring network in the U.S. North Atlantic region using all available NARCCAP models. Different schemes for utilizing NARCCAP model output are tested and compared in terms of estimation precision, which is based on Bootstrap resampling.
Linder, E.
Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand
Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate
W. Bejranonda; M. Koch
Probabilistic precipitation forecasting based on ensemble output using generalized additive models and Bayesian model averaging
NASA Astrophysics Data System (ADS)
A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and application of this method to the downscaling of climate change scenarios were discussed.
Yang, Chi; Yan, Zhongwei; Shao, Yuehong
Entropy of stochastic blockmodel ensembles
NASA Astrophysics Data System (ADS)
Stochastic blockmodels are generative network models where the vertices are separated into discrete groups, and the probability of an edge existing between two vertices is determined solely by their group membership. In this paper, we derive expressions for the entropy of stochastic blockmodel ensembles. We consider several ensemble variants, including the traditional model as well as the newly introduced degree-corrected version [Karrer , Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.83.016107 83, 016107 (2011)], which imposes a degree sequence on the vertices, in addition to the block structure. The imposed degree sequence is implemented both as "soft" constraints, where only the expected degrees are imposed, and as "hard" constraints, where they are required to be the same on all samples of the ensemble. We also consider generalizations to multigraphs and directed graphs. We illustrate one of many applications of this measure by directly deriving a log-likelihood function from the entropy expression, and using it to infer latent block structure in observed data. Due to the general nature of the ensembles considered, the method works well for ensembles with intrinsic degree correlations (i.e., with entropic origin) as well as extrinsic degree correlations, which go beyond the block structure.
Peixoto, Tiago P.
Estimates of climate change in Southern Europe using different downscaling techniques.
National Technical Information Service (NTIS)
Three methods of downscaling have been applied to climate change experiments to obtain regional climate information for Spain and the IPCC region ''Southern Europe''. The first method (direct interpolation of the nearest gridpoints to the region analysed)...
U. Cubasch H. Storch J. Waszkewitz E. Zorita
Climate Change Impacts on the Ganges-Brahmaputra Precipitation--Downscaled from GCM Predictors
NASA Astrophysics Data System (ADS)
Downscaling GCM projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional analyses by generating specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse, river basins in South Asia--the Ganges and the Brahmaputra. We used Canadian Center for Climate Modeling version 3.1 (CGCM3.1) predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of the Day (GSOD) observed precipitation records from 43 stations and National Center for Environmental Prediction (NCEP) re-analysis large-scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcing in the basins. The precipitation of both basins were largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 hPa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 hPa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled projected precipitation with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of rural populations that rely on subsistence agriculture. Analysis indicated that the uncertainty in the downscaled precipitation was less than that of the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is recommended for comprehensive impact studies.
Pervez, M.; Henebry, G. M.
Downscaling of Global Climate Model Output with Dynamic Artificial Neural Networks
NASA Astrophysics Data System (ADS)
The issues of downscaling the outputs of global circulation model (GCM) to a scale appropriate to hydrological impact studies are investigated using functionally different methods. Three types of dynamic artificial neural networks (DANN) with different inherent representations of temporal information are investigated. Time lagged feedforward neural network (TLFN), and two types of globally recurrent neural networks (Elman and Jordan networks) are proposed for downscaling daily precipitation and temperature series for the Serpent watershed in northern Quebec (Canada). The performance of the optimal DANN model is compared to benchmarks from a statistical downscaling model and a stochastic weather generator. Overall, the downscaling results for the current period (1961-2000) suggest that the TLFN is the most efficient of the DANN models tested for downscaling both daily precipitation as well as daily temperature series. The Elman and Jordan networks performed poorly on precipitation downscaling as compared to the TDNN. Furthermore, the different model test results indicate that the optimal DANN model significantly outperforms the statistical and stochastic models for the downscaling of precipitation whatever the season. However, for minimum and maximum temperature, the TLFN and the statistical model are almost equivalent because the inherent physical process is likely less nonlinear. While changes in precipitation between the current and the future scenarios produced by the TLFN are smaller than those produced by the statistical model (except for the winter), they remain significantly larger than those suggested by the stochastic model. Thus suggesting that the TLFN can be a good trade-off alternative to the other models. Changes in streamflows between current and future periods (2020s, 2050s, and 2080s) are also compared and discussed with regard to the downscaling methods.
Coulibaly, P.; Dibike, Y.
N–PLS regression as empirical downscaling tool in climate change studies
Summary An N–PLS regression technique was tested as an empirical downscaling method. Average monthly near-ground air temperature (t), specific humidity (q), and sea-level pressure (p) fields across Central and Western Europe were used as predictors for average monthly air temperature (T), dew temperature (D), and precipitation amount (P) at 4 locations in Slovenia. The empirical downscaling models (EM) were developed
K. Bergant; L. Kajfez-Bogataj
Seasonality properties of four statistical-downscaling methods in central Sweden
Summary Daily precipitation in northern Europe has different statistical properties depending on season. In this study, four statistical\\u000a downscaling methods were evaluated in terms of their ability to capture statistical properties of daily precipitation in different\\u000a seasons. Two of the methods were analogue downscaling methods; one using principal component analysis (PCA) and one using\\u000a gradients in the pressure field (Teweles-Wobus scores,
F. Wetterhall; S. Halldin; C.-Y. Xu
Clustering Methods for Statistical Downscaling in Short-Range Weather Forecasts
In this paper an application of clustering algorithms for statistical downscaling in short-range weather forecasts is presented. The advantages of this technique compared with standard nearest-neighbors analog methods are described both in terms of computational efficiency and forecast skill. Some validation results of daily precip- itation and maximum wind speed operative downscaling (lead time 1-5 days) on a network of
J. M. Gutiérrez; A. S. Cofiño; R. Cano; M. A. Rodríguez
Predictions of climate change over Europe using statistical and dynamical downscaling techniques
Statistical and dynamical downscaling predictions of changes in surface temperature and precipitation for 2080-2100, relative to pre-industrial conditions, are compared at 976 European observing sites, for January and July. Two dynamical downscaling methods are considered, involving the use of surface temperature or precipitation simulated at the nearest grid point in a coupled ocean-atmosphere general circulation model (GCM) of resolution 300
James Murphy
Future Ozone projections for a rural mid-latitued site using a statistical downscaling approach
Using a synoptic-regression based approach, this study estimates future maximum 8 hourly mean O3 levels under the future A1B scenario for a rural background area situated in The Netherlands. The statistical downscaling tool was used to downscale the Atmospheric-Ocean Coupled General Circulation Model (AOGCM) ECHAM5-MPI\\/OM for the present-day 20 Century (20C) control run (1991-2000) and two future A1B scenario periods
M. Demuzere; N. P. M. van Lipzig
SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)
NASA Astrophysics Data System (ADS)
General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.
Wilby, R. L.; Dawson, C. W.
SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)
NASA Astrophysics Data System (ADS)
General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.
Wilby, R. L.; Dawson, C. W.
Ensemble data assimilation using passive and active microwave observations of precipitation in mountainous regions
NASA Astrophysics Data System (ADS)
The Goddard WRF ensemble data assimilation system has been developed to assimilate precipitation information into WRF model to improve QPF and QPE at high resolution. The flow-dependent forecast error covariance estimated in the assimilation procedure aims to capture the large temporal and spatial variability of precipitation and clouds. The microphysics at cloud-resolving scales and all-sky radiative transfer simulator serve as non-linear observation operators to link observables with model states. We present results of assimilating precipitation-affected microwave radiance and precipitation radar reflectivity from a pre-GPM constellation overland in the southeast US region. Observational bias correction for all-sky radiance is developed based on innovation statistics and a situation-dependent bias estimation model. The data impact is assessed with independent ground-based precipitation observations and evaluated in applications to dynamical downscaling and hydrological prediction.
zhang, S. Q.; Lin, X.; Hou, A. Y.; Barros, A. P.
A standardized framework for evaluating the skill of regional climate downscaling techniques
NASA Astrophysics Data System (ADS)
Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly downscaled using a variety of statistical and dynamical techniques. Despite the essential role of downscaling in regional assessments, there is no standard approach to evaluating various downscaling methods. Hence, impact communities often have little awareness of limitations and uncertainties associated with downscaled projections. To develop a standardized framework for evaluating and comparing downscaling approaches, I first identify three primary characteristics of a distribution directly relevant to impact analyses that can be used to evaluate a simulated variable such as temperature or precipitation at a given location: (1) annual, seasonal, and monthly mean values; (2) thresholds, extreme values, and accumulated quantities such as 24h precipitation or degree-days; and (3) persistence, reflecting multi-day events such as heat waves, cold spells, and wet periods. Based on a survey of the literature and solicitation of expert opinion, I select a set of ten statistical tests to evaluate these characteristics, including measures of error, skill, and correlation. I apply this framework to evaluate the skill of four downscaling methods, from a simple delta approach to a complex asynchronous quantile regression, in simulating daily temperature at twenty stations across North America. Identical global model fields force each downscaling method, and the historical observational record at each location is randomly divided by year into two equal parts, such that each statistical method is trained on one set of historical observations, and evaluated on an entirely independent set of observations. Biases relative to observations are calculated for the historical evaluation period, and differences between projections for the future. Application of the framework to this broad range of downscaling methods and locations is successful in that: (1) the downscaling method used is identified as a more important determinant of data quality than station location or GCM; and (2) key differences between downscaling methods are made apparent. For tests focusing on the general distribution of the variable, all methods except bias correction are relatively successful in simulating observed climate, suggesting that if an impact is most sensitive to changes in the mean, even a relatively simple downscaling approach such as "delta" will significantly improve simulation of local-scale climate. For tests that focus on the tails of the distribution, however, differences do arise between simple vs. quantile-based downscaling methods. Specifically, the latter appears less sensitive to location and more consistently able to reproduce observed climate. In terms of future projections, the most notable differences between downscaling methods becomes apparent at the right-hand tail of the distribution, where simple methods tend to simulate much greater increases (up to double the extreme heat days, for some locations) than more complex downscaling methods. I conclude by discussing how a standardized evaluation framework may advance our understanding of regional climate impact studies in understanding biases and limitations in results, as well as providing critical input into the selection of downscaling methods for future assessments. Given the potential exhibited by this initial test, I explore how this evaluation framework could be expanded in the future to make it even more useful: to the regional scale, for example, by including tests for spatial correlations and forcing relationships; or across variables, to capture interactions directly relevant to impact studies, such as heat waves (a function of temperature and humidity, affecting human health, energy demand, and agriculture) or snow amounts (a function of precipitation and temperature, affecting infrastructure and ecosystems); or to evaluate a broader selection of clim
Hayhoe, Katharine Anne
Ocean Prediction via downscaling of large-scale ocean circulation models.
NASA Astrophysics Data System (ADS)
The Hybrid Coordinate Ocean Model (HYCOM) is used to forecast the three-dimensional structure in various parts of the world ocean (the Gulf of Mexico, the northern Gulf of Mexico, the Persian Gulf, the Gulf of California, and the Hawaii region). The horizontal resolution varies between them, but the coarsest is ~3.5 km. Vertical resolution varies from 20 to 32 layers. Lateral boundary forcing is supplied by global or basin- scale versions of HYCOM, and surface wind and heat flux forcing from the Navy Operational Global Atmospheric Prediction System (NOGAPS). The lateral boundary conditions use a "buffer area" for the baroclinic mode where the fine-grid solution is relaxed towards the outer coarse grid solution. The method of Characteristics is used for the barotropic mode. Most of the models assimilate ocean observations via the Navy Coupled Ocean Data Assimilation (NCODA) system. The primary observations include satellite-derived sea surface height and temperature as well as Argo profile data. The NCODA configuration used here is based on multi-variant optimal interpolation and uses the Cooper-Haines (1983) technique for downward projection of surface observations. The forecast length varies but is typically between 3-7 days. The value-added of downscaling to higher resolution is demonstrated through various model-data comparisons, particularly data that was withheld from the data assimilation system. In the Gulf of Mexico, Loop Current Rings and (some) cyclonic rings compare favorably to independently derived thermal fronts measured with multi-channel SST's (MCSST). The northern Gulf of Mexico domain, which represents a triple- nested system, is used to generate ensembles to examine the variance associated with errors in the initial state, surface wind forcing, etc. Near-surface current patterns in the Persian Gulf are compared to drouged drifters. Several of the circulation features in the Hawaii area are compared to observations collected during a recent Navy exercise. The sea level height in the Gulf of California agrees very well with the height measured by coastal tide guage stations. This domain was also used to exhaustively investigate the sensitivity of the lateral boundary condition parameters. The general circulation features in these regions are discussed, as well as technical aspects of the assimilation and validation.
Hogan, P. J.; Smedstad, O.; Wallcraft, A. J.; Zamudio, L.; Thoppil, P. G.
Variance reduced ensemble Kalman filtering
A number of algorithms to solve large-scale Kalman filtering problems have been introduced recently. The ensemble Kalman filter represents the probability density of the state estimate by a finite number of randomly generated system states. Another algorithm uses a singular value decomposition to select the leading eigenvectors of the covariance matrix of the state estimate and to approximate the full
A. W. Heemink; M. Verlaan; A. J. Segers
Machine learning algorithms are methods developed to deal with large volumes of data with high efficiency. Adaboost has been among the most popular and promising algorithms in the last decade and has demonstrated its potential for classification of remote sensing data. Previous studies have shown that Adaboost, though less stable than bagging (another well-know ensemble classification algorithm), consistently produces higher
Jonathan Cheung-Wai Chan; Frank Canters
Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States
NASA Astrophysics Data System (ADS)
Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.
Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.
Deterministic and probabilistic optimization of analogs and weather-regimes downscaling algorithms for seasonal precipitation
NASA Astrophysics Data System (ADS)
We present two statistical-downscaling methods to estimate seasonal precipitation at predetermined stations that rely on global forecasts, using a technique to find past-analog synoptic-weather patterns and their connection to local precipitation. One of the methods utilizes a classification of the large-scale weather patterns into regimes (weather-regimes downscaling) and the other is based on the identification of closest past analogs without grouping the weather events into defined regimes (a "pure analogs" approach, analogs downscaling). Determining the closest past synoptic pattern requires the definition of a distance between the present and past states. We have chosen to work with a general definition of distance following the Minkowski metric of order p (p-norm distance). In an attempt to explain the uncertainty associated with the determination of past analogs, not only the closest state to the actual event is identified, but also the following ones, up to n, and their contributions are weighted in inverse proportion to their squared distances. The sensitivity to n and p was objectively analyzed using deterministic and probabilistic verification procedures with the aim of optimizing the algorithms. Two types of information are relevant to the end users in this study: (1) the absolute seasonal precipitation amount and (2) whether a given precipitation threshold of the climatology distribution is exceeded or not. We analyzed the ability of the downscaling algorithms to reproduce the seasonal amount by deterministically evaluating the linear relationship between the downscaled and observed seasonal precipitation amounts. Next, we checked the improvement by the downscaling method over an existing reference forecast in providing threshold exceedance information. In the absence of a downscaling algorithm, the only gauge-specific available seasonal forecast was the seasonal climatological mean of the precipitation at the site. The skill of the downscaling estimations was assessed in terms of four attributes relevant to the end user: accuracy, reliability, resolution, and discrimination relative to the observed climatological mean. To analyze these attributes we calculated Brier skill scores, their decomposition into reliability and resolution terms, and the area under the relative-operating curve. These are calculated for the probability of exceeding the 66th percentile and of not reaching the 33rd percentile. Results show that skill full deterministic and probabilistic estimates of seasonal precipitation at each site are obtained with our downscaling methods. Our analysis shows that weighting n=2,3 analogs/weather regimes results in significant improvement as compared to relying on the closest past state only, but no further improvement is attained for n>3. The sensitivity of the downscaling algorithm to p-norm is different whether the algorithm is verified deterministically or probabilistically.
Rostkier-Edelstein, Dorita; Kunin, Pavel
Dynamically Downscaling Precipitation from Extra-Tropical Cyclones
NASA Astrophysics Data System (ADS)
Recent flooding events experienced by the UK and Western Europe have highlighted the potential disruption caused by precipitation associated with extra-tropical cyclones. The question as to the effect of a warming climate on these events also needs to be addressed to determine whether such events will become more frequent or more intense in the future. The changes in precipitation can be addressed through the use of Global Climate Models (GCMs), however the resolution of GCMs are often too coarse to drive hydrological models, required to investigate any flooding that may be associated with the precipitation. The changes to the precipitation associated with extra-tropical cyclones are investigated by tracking cyclones in two resolutions of the ECHAM5 GCM, T213 and T319 for 20th and 21st century climate simulations. It is shown that the intensity of extreme precipitation associated with extra-tropical cyclones is predicted to increase in a warmer climate at both resolutions. It was also found that the increase in resolution shows an increase in the number of extreme events for several fields, including precipitation; however it is also seen that the magnitude of the response is not uniform across the seasons. The tails of the distributions are investigated using Extreme Value Theory (EVT) using a Generalised Pareto Distribution (GPD) with a Peaks over Threshold (POT) method, calculating return periods for given return levels. From the cyclones identified in the T213 resolution of the GCM a small number of cyclones were selected that pass over the UK, travelling from the South-West to the North-East. These are cyclones that are more likely to have large amounts of moisture associated with them and therefore potentially being associated with large precipitation intensities. Four cyclones from each climate were then selected to drive a Limited Area Model (LAM), to gain a more realistic representation of the precipitation associated with each extra-tropical cyclone. The suitability of the LAM for downscaling was evaluated by running the LAM for the events of June and July 2007 (UK floods) and comparing the output to observations. The results from this comparison provide confidence that the model is able of reproducing realistic intensities for extreme precipitation events. Whilst this method does not allow for a robust comparison between the climates it does for allow for an analysis of the method, and whether dynamically downscaling individual events is suitable. It was found that by nesting the LAM within the GCM, large increases in the precipitation intensities were seen, as well as gaining a greater temporal resolution. Analysis of more events will allow a more robust comparison between climates.
Champion, A.; Hodges, K.; Bengtsson, L.
Dynamical Downscaling of Tropical Storm Ivan in the Southern Appalachians
NASA Astrophysics Data System (ADS)
To understand the mechanisms associated with the spatial and temporal rainfall distribution over the Southern Appalachians during tropical storms, two-way nested high resolution dynamical downscaling of Hurricane Ivan, 2004 was conducted using WRF3.1 with the outer domain covering most of the southeast US at 3km resolution and the inner domain focusing on the trail of the Southern Appalachians at 1km grid increment. Model forcing was extracted from the North American Regional Reanalysis (NARR) and NCEP Final Operational Global Analysis (NCEP-FNL) data sets. Compared with different observations [satellite based, station measurement, combined products and the best track data from National Hurricane Center (NHC)], it is found both NARR and FNL reproduce the precipitation patterns reasonably well, but NARR is generally better than the FNL over the eastern slopes where orographic effects dominate. Timing errors are more significant in the NARR DDS because NARR underestimated the intensity of Hurricane Ivan. Rainfall intensity errors that result from underestimating localized heavy rainfall in the FNL forced experiment were attributed to the poorly resolved vertical wind shear in the FNL reanalysis. Independently of forcing, both dynamical downscaling simulations (DDS) overestimate rainfall at low elevations, and all around better performance of the DDS files vis-à-vis the original forcing fields is generally found at high elevations. Although the rainfall distribution is still dominated by the large scale forcing, how well the topography is resolved is of significance on simulating localized extreme rainfall. The early arrival of rainfall in the vicinity of NCDC raingauges is due to excessive horizontal wind speed, potentially resulting from the parameterization of land surface roughness in the model, which may not be appropriate for high wind regimes. Sensitivity experiments on boundary layer dynamics showed that by transporting moisture away from the surface faster to higher levels than the local MYJ PBL parameterization, the nonlocal YSU parameterization favors development of deep convection. In turn, the MYJ PBL scheme is more sensitive to orographic lifting. As expected, heavy localized precipitation patterns were obtained in the DDSs with the cumulus parameterization being turned off.
Sun, X.; Barros, A. P.
Radar-guided radiometer downscaling for combined soil moisture retrieval
NASA Astrophysics Data System (ADS)
Combining the advantages of both active and passive microwave measurements in a soil moisture-retrieval can dramatically increase resolution and sensitivity. Simultaneous remote sensing observations of the normalized radar cross section (?0) and emissivity (?) will be jointly used to ultimately achieve an improved soil moisture-retrieval algorithm. The ?0 values are derived from the Precipitation Radar (PR) from TRMM (product 2A21 V7) while the ? values are derived from the brightness temperatures (BTs) measured by the passive microwave radiometric system TRMM Microwave Imager (TMI) (product 1B11 V7). Emissivity values are used instead of BTs because they are more directly related to water content. The coarse-resolution passive measurements (TMI) are first downscaled to match the finer resolution of the active ones (PR) via a Kalman filter, with which the error of the TMI instrument in terms of emissivity is parameterized so that different weights will be given to the PR and TMI measurements. The downscaling is performed over the state of Oklahoma, for 'no-rain' conditions (indicated by PR), for high PR incidence angles, in order to obtain simultaneous measurements of the two instruments (because of different scanning geometries, synchronized measurements of both instruments can only be achieved at high PR incidence angles), for each TMI channel separately (not including the two high-resolution ones and the 21.3 GHz), for the early morning hours only (active and passive sensors retrieve information on soil moisture at different depths and this discrepancy becomes even greater in the late afternoon hours of the day, therefore selecting only the early-morning overpasses will mitigate this effect), and for different regions within Oklahoma. The regions are selected based on land class. Regions with homogeneous vegetation cover are examined separately from regions characterized by heterogeneous vegetation cover. Oklahoma was selected as the area of study, because of its variety of land classes and the availability of ground-based validation soil moisture data. Is there a correlation between radar backscatter and emissivity values?
Stampoulis, D.; Haddad, Z. S.; Anagnostou, E. N.
Statistical Downscaling Based on Spartan Spatial Random Fields
NASA Astrophysics Data System (ADS)
Stochastic methods of space-time interpolation and conditional simulation have been used in statistical downscaling approaches to increase the resolution of measured fields. One of the popular interpolation methods in geostatistics is kriging, also known as optimal interpolation in data assimilation. Kriging is a stochastic, linear interpolator which incorporates time/space variability by means of the variogram function. However, estimation of the variogram from data involves various assumptions and simplifications. At the same time, the high numerical complexity of kriging makes it difficult to use for very large data sets. We present a different approach based on the so-called Spartan Spatial Random Fields (SSRFs). SSRFs were motivated from classical field theories of statistical physics [1]. The SSRFs provide a different approach of parametrizing spatial dependence based on 'effective interactions,' which can be formulated based on general statistical principles or even incorporate physical constraints. This framework leads to a broad family of covariance functions [2], and it provides new perspectives in covariance parameter estimation and interpolation [3]. A significant advantage offered by SSRFs is reduced numerical complexity, which can lead to much faster codes for spatial interpolation and conditional simulation. In addition, on grids composed of rectangular cells, the SSRF representation leads to an explicit expression for the precision matrix (the inverse covariance). Therefore SSRFs could provide useful models of error covariance for data assimilation methods. We use simulated and real data to demonstrate SSRF properties and downscaled fields. keywords: interpolation, conditional simulation, precision matrix References [1] Hristopulos, D.T., 2003. Spartan Gibbs random field models for geostatistical applications, SIAM Journal in Scientific Computation, 24, 2125-2162. [2] Hristopulos, D.T., Elogne, S. N. 2007. Analytic properties and covariance functions of a new class of generalized Gibbs random fields, IEEE Transactions on Information Theory, 53(12), 4667-4679. [3] Hristopulos, D.T., Elogne, S. N. 2009. Computationally efficient spatial interpolators based on Spartan Spatial random fields, IEEE Transactions on Signal Processing, 57(9), 3475-3487.
Hristopulos, Dionissios
Exploiting Unlabeled Data to Enhance Ensemble Diversity
Ensemble learning aims to improve generalization ability by using multiple\\u000abase learners. It is well-known that to construct a good ensemble, the base\\u000alearners should be accurate as well as diverse. In this paper, unlabeled data\\u000ais exploited to facilitate ensemble learning by helping augment the diversity\\u000aamong the base learners. Specifically, a semi-supervised ensemble method named\\u000aUDEED is proposed.
Min-Ling Zhang; Zhi-Hua Zhou
Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an Ensemble Regional Climate Simulation for the Mid-Century
This study examines an ensemble of climate change projections simulated by a global climate model (GCM) and downscaled with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three ensemble future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to downscale the GCM control simulation (1995-2015) and each ensemble future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statistically insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the ensemble future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through ensemble simulations.
Leung, Lai R.; Qian, Yun
African Drum and Steel Pan Ensembles.
ERIC Educational Resources Information Center
Discusses how to develop both African drum and steel pan ensembles providing information on teacher preparation, instrument choice, beginning the ensemble, and lesson planning. Includes additional information for the drum ensembles. Lists references and instructional materials, sources of drums and pans, and common note layout/range for steel pan…
Sunkett, Mark E.
Thunderstorm-Scale Ensemble Forecasting
NASA Astrophysics Data System (ADS)
Thunderstorms present an important weather forecast problem. Both the initiation and the general behavior of convection in the atmosphere are challenging to predict. In order to resolve the gross features of individual convective elements in numerical models, grid spacing of a kilometer or less is required, much finer than the current generation of operational numerical weather forecasting models. Sensitivities to small changes in the initial conditions and the physical parameterizations in the models have been shown to have important effects on some occasions at that scale. In order to address the predictability of thunderstorms with numerical models and to experiment with techniques to provide operationally useful information for weather forecasters, an ensemble of thunderstorm simulations initalized with conditions from larger scale models has been run experimentally in near-real time since June 2001 at the National Severe Storms Laboratory for use by forecasters from the National Weather Service's Storm Prediction Center. In addition, previous cases run in a non-operational, research mode have been expanded to look at sensitivity to inclusion of ice-phase microphysics. In general, it appears that forecasters find the information from the ensemble frequently agrees with their subjective assessment of the thunderstorm potential. This suggests that if computational resources were sufficient, the ensemble could play an important role in the thunderstorm forecasting process. The ice-phase microphysics sensitivity tests have shown that, in some cases, the inclusion of ice in the ensemble has a profound impact on the estimate of the probable lifetime of storms. The sensitivity due to the changes in model formulation may be as large or larger than the sensitivity due to initial condition uncertainty. Other parameterizations have not been tested in this experiment to date, but it suggests that consideration of both initial condition uncertainty and model formulation may be necessary to develop an ensemble that provides reliable guidance about thunderstorms for weather forecasters. >http://www.spc.noaa.gov/exper/Spring_2001/elmore
Brooks, H. E.; Elmore, K. L.
Downscaling MODIS Land Surface Temperature for Urban Public Health Applications
NASA Technical Reports Server (NTRS)
This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.
Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel
Downscaling MODIS Land Surface Temperature for Urban Public Health Applications
NASA Astrophysics Data System (ADS)
This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heat-related mortality data. The current HWWS do not take into account intra-urban spatial variations in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with land surface temperature (LST) estimates derived from thermal remote sensing data. In order to further improve the assessment of intra-urban variations in risk from extreme heat, we developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. We will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.
Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.
Downscaling NASA Climatological Data to Produce Detailed Climate Zone Maps
NASA Technical Reports Server (NTRS)
The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of downscaling NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.
Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.
Downscaling of GCM rainfall: A refinement of the perturbation method
NASA Astrophysics Data System (ADS)
A popular approach for downscaling GCM rainfall is to calculate the average percentage change in GCM grid square rainfall from current to future conditions, with separate calculations for each month, and then to scale observed records of point daily rainfall by these percentage changes. This is termed the perturbation method. The main advantage of this method is that it is simple and easy to apply. However, the use of monthly GCM outputs means that the method is not sensitive to possible changes in daily extremes or the frequency of wet days, which are of great interest in hydrological impact studies. A refinement of the perturbation method is presented, which uses a pattern of change rather than the average percentage change. Transient GCM simulations are used to obtain patterns of change in ranked daily GCM rainfall from current (say 1961-1990) to future (say 2071-2100) conditions. The patterns of change are calculated separately for each calendar month. These patterns of change are then used to scale ranked historical point daily rainfall, thus obtaining a point rainfall scenario that is consistent with what the GCM is predicting about changes in daily, rather than just monthly, rainfall. In other words, the method is sensitive to the changes in extreme daily rainfalls and changes in the frequency of wet days that occur in the GCM, and produces a more realistic sequence of "climate change impacted" rainfall, compared to the commonly used approach of simply scaling all the historical rainfall values in each month by the same amount.
Harrold, T. I.; Jones, R.
Downscaling the environmental associations and spatial patterns of species richness.
We introduce a method that enables the estimation of species richness environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation. The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species-area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by downscaling richness from 2 degrees to 0.25 degrees (-250 km to -30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness-environment relationship, but accurately predicted only relative (rank) values of richness. The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness-environment relationships, and for the provision of high-resolution maps for basic science and conservation. PMID:24988779
Keil, Petr; Jetz, Walter
Statistical downscaling of daily precipitation over Sweden using GCM output
NASA Astrophysics Data System (ADS)
A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070-2100 compared to 1961-1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.
Wetterhall, Fredrik; Bárdossy, András; Chen, Deliang; Halldin, Sven; Xu, Chong-Yu
Statistical Ensemble of Large Eddy Simulations
NASA Technical Reports Server (NTRS)
A statistical ensemble of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an ensemble averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the ensemble averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the ensemble of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The ensemble averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical ensemble provided that the ensemble contains at least 16 realizations.
Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)
A Comprehensive Framework for Quantitative Evaluation of Downscaled Climate Predictions and Projections
NASA Astrophysics Data System (ADS)
The variety of methods used for downscaling climate predictions and projections is large and growing larger. Comparative studies of downscaling techniques to date are often initiated in relation to specific projects, are focused on limited sets of downscaling techniques, and hence do not allow for easy comparison of outcomes. In addition, existing information about the quality of downscaled datasets is not available in digital form. There is a strong need for systematic evaluation of downscaling methods using standard protocols which will allow for a fair comparison of their advantages and disadvantages with respect to specific user needs. The National Climate Predictions and Projections platform, with the contributions of NCPP's Climate Science Advisory Team, is developing community-based standards and a prototype framework for the quantitative evaluation of downscaling techniques and datasets. Certain principles guide the development of this framework. We want the evaluation procedures to be reproducible and transparent, simple to understand, and straightforward to implement. To this end we propose a set of open standards that will include the use of specific data sets, time periods of analysis, evaluation protocols, evaluation tests and metrics. Secondly, we want the framework to be flexible and extensible to downscaling techniques which may be developed in the future, to high-resolution global models, and to evaluations that are meaningful for additional applications and sectors. Collaboration among practitioners who will be using the downscaled data and climate scientists who develop downscaling methods will therefore be essential to the development of this framework. The proposed framework consists of three analysis protocols, along with two tiers of specific metrics and indices that are to be calculated. The protocols describe the following types of evaluation that can be performed: 1) comparison to observations, 2) comparison to a "perfect model" simulation at high resolution, and 3) idealized comparisons where an analytic solution is known. Each of these protocols addresses different questions about the data, and defines different needs for evaluation datasets. For each protocol we identify individual pathways that may depend on the particular details of a given downscaling method or the goals of the validation. For example, whether the comparison is made to gridded observational data or to a set of station observations. Complementing the protocols are two tiers of metrics -- measures of performance of the methods in many dimensions. Tier 1 aims at a general statistical evaluation of the downscaled data. Tier 1 metrics will be primarily determined in collaboration with developers of downscaling methods, and can provide direct feedback into their further development. It is envisioned that Tier 2 consists of a flexible and extensible collection of metrics that will be developed in close collaboration with climate impacts modelers and those who use downscaled data for addressing real-world problems.
Barsugli, J. J.; Guentchev, G.
Application of self-organizing maps technique in downscaling GCMs climate change projections for Same, Tanzania
NASA Astrophysics Data System (ADS)
High resolution surface climate variables are required for end-users in climate change impact studies; however, information provided by Global Climate Models (GCMs) has a coarser resolution. Downscaling techniques such as that developed at the University of Cape Town, which is based on self-organizing maps (SOMs) technique, can be used to downscale the coarse-scale GCM climate change projections into finer spatial resolution projections. The SOM downscaling technique was employed to project rainfall and temperature changes for 2046-2065 and 2080-2100 periods for Same district, Tanzania. This model was initially verified using downscaled NCEP reanalysis and observed climate data set, and between NCEP reanalysis and GCM controls. After verification the model was used to downscale climate change projections of five GCMs for 2046-2065 ( future-A) and 2080-2100 ( future-B) periods. These projections were then used to compute changes in the climate variables by comparing future-A and B to the control period (1961-2000). Verification results indicated that the NCEP downscaled climate compared well with the observed data. Also, comparison between NCEP downscaled to GCM downscaled showed that all the four GCM models (CGCM, CNRM, IPSL, and ECHAM) compared well with the NCEP downscaled temperature and rainfall data. Future projections (2046-2065) indicated 56 mm and 42 mm increase in seasonal total rainfall amounts for March-April-May (MAM) and October-November-December (OND) seasons (23% and 26% increase), respectively, and an increase of about 2 °C in temperature for both seasons. Furthermore, future projects show that during MAM there will be 2 days decrease in dry spells, and 8 days increase in seasonal length while for OND, there will also be 2 days decrease in dry spells, and 40 days increase in the seasonal length. Future-B projects 4 °C rise in temperature, and 46.5% and 35.8% increase in rainfall for MAM and OND, respectively. It is recommended to investigate the effect of increased rainfall and temperature on agricultural production.
Tumbo, S. D.; Mpeta, E.; Tadross, M.; Kahimba, F. C.; Mbillinyi, B. P.; Mahoo, H. F.
DSCOKRI: A library of computer programs for downscaling cokriging in support of remote sensing applications
NASA Astrophysics Data System (ADS)
The main purpose of this paper is to describe and provide a suite of computer programs for performing downscaling cokriging with satellite sensor images. The usual setting of downscaling cokriging is to increase the spatial resolution of multispectral images with coarse spatial resolution by fusing them with a fine spatial resolution image of a different band from the same or a different sensor. This problem has also been described as image sharpening and commonly the spatial resolution of the fused image is equal to the finest spatial resolution used in the fusion. Nevertheless, downscaling cokriging allows the spatial resolution of the predicted image to be increased beyond that of any of the input data sets, a procedure usually referred to as super-resolution sharpening. The programs provided here support all the required stages for image fusion and super-resolution sharpening by downscaling cokriging. These stages are: (i) compute the empirical variogram of each spectral band and empirical cross-variograms for each pair of spectral bands; (ii) estimate and fit a model to the point-support variogram of each spectral band and cross-variogram of each pair of spectral bands; (iii) set up the cokriging system and obtain the set of downscaling cokriging weights and (iv) obtain the sharpened image by application of the weights to the empirical remote sensing images. A case study with Landsat ETM+ images is provided to demonstrate the implementation of the method and to allow checking of the programs.
Pardo-Iguzquiza, Eulogio; Atkinson, Peter M.; Chica-Olmo, Mario
Statistical downscaling of an air quality model using Fitted Empirical Orthogonal Functions
NASA Astrophysics Data System (ADS)
Downscaling is a technique that is used to extract high-resolution information from regional scale variables produced by coarse resolution models such as Chemical Transport Models (CTMs). Statistical downscaling methods in geophysics often rely on Empirical Orthogonal Functions (EOFs). EOFs are spatial Principal Components (PCs) that display space-time modes of variability of a quantity over a region. Here we present a novel statistical downscaling method that employs Fitted Empirical Orthogonal Functions (F-EOFs) to provide local forecasts. F-EOFs differ from EOFs in that they represent space-time variations associated with a particular location through the use of inverse regression. We illustrate our downscaling method, for ozone levels over the US, with the Regional chEmical trAnsport Model (REAM) whose outputs are over 70 by 70 km grid cells. We use ground level ozone observations from monitoring stations within the south-eastern US region to downscale REAM. We select the first leading F-EOFs and regress our observations on the corresponding F-EOF loadings. We also compare our results to linear regression and PC regression. The regression on F-EOFs shows the best predictive ability. To examine the consistency of our results we repeat the analysis for different fitting and validation periods. Furthermore, in our application, PFC regression also outperforms PC regression as a dimension reduction technique.
Alkuwari, Farha A.; Guillas, Serge; Wang, Yuhang
Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling
NASA Astrophysics Data System (ADS)
Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.
Pierce, David W.; Das, Tapash; Cayan, Daniel R.; Maurer, Edwin P.; Miller, Norman L.; Bao, Yan; Kanamitsu, M.; Yoshimura, Kei; Snyder, Mark A.; Sloan, Lisa C.; Franco, Guido; Tyree, Mary
Cavity QED with atomic ensembles
NASA Astrophysics Data System (ADS)
Cavity Quantum Electrodynamics has long been a proving grounds for the study of the interaction of light with matter. Historically the objective has typically been to couple one atom to one photon as strongly as possible. While this endeavor has yielded a variety of beautiful and groundbreaking results, we take a different approach. Inspired by the quantum repeater scheme of Duan, Lukin, Cirac and Zoller, we have built a cavity-ensemble experiment, where the strong coupling between the light and the matter is achieved via the combination of the resonant enhancement of a cavity and a collective enhancement of an ensemble. We investigate the capabilities and limitations of such an approach through a number of experiments. The first experiment we describe is a very-high-quality source of photon pairs of opposite polarization, but otherwise nearly-identical spectral properties. We proceed to a high-fidelity single photon source, and carefully investigate the decoherence mechanisms that limit the performance of such a system. Next we present the cavity-mediated transfer of a single collective excitation between atomic ensembles, and deterministic entanglement generation. Lastly, we present a heralded, polarization preserving quantum memory. All of these experiments depend critically on the strong light-matter coupling afforded by the cavity-ensemble interaction, and require increasingly more sophisticated state control of the atoms. Finally, we describe our new apparatus, combining a relatively long, high-finesse optical resonator with a 2microm dipole trap. We focus on the technical details of stabilizing the narrow resonator, and discuss briefly a proposal for high efficiency Quantum Non-Demolition photon detection. We conclude with preliminary data demonstrating single-atom detection.
Simon, Jonathan
Ensemble learning incorporating uncertain registration.
This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important. PMID:23288332
Simpson, Ivor J A; Woolrich, Mark W; Andersson, Jesper L R; Groves, Adrian R; Schnabel, Julia A
Ensemble modeling of cancer metabolism.
The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. PMID:22623918
Khazaei, Tahmineh; McGuigan, Alison; Mahadevan, Radhakrishnan
Ensemble Modeling of Cancer Metabolism
The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets.
Khazaei, Tahmineh; McGuigan, Alison; Mahadevan, Radhakrishnan
Downscaling Alkaline Phosphatase Activity in a Subtropical Reservoir
NASA Astrophysics Data System (ADS)
This research was conducted by downscaling study to understand phosphorus (P)-deficient status of different plankton and the role of alkaline phosphatase activity (APA) in subtropical Feitsui Reservoir. Results from field survey showed that bulk APA (1.6~95.2 nM h-1) was widely observed in the epilimnion (0~20 m) with an apparent seasonal variations, suggesting that plankton in the system were subjected to P-deficient seasonally. Mixed layer depth (an index of phosphate availability) is the major factor influencing the variation of bulk APA and specific APA (124~1,253 nmol mg C-1 h-1), based on multiple linear regression analysis. Size-fractionated APA assays showed that picoplankton (size 0.2~3 um) contributed most of the bulk APA in the system. In addition, single-cell APA detected by enzyme-labeled fluorescence (ELF) assay indicated that heterotrophic bacteria are the major contributors of APA. Thus, we can infer that bacteria play an important role in accelerating P-cycle within P-deficient systems. Light/nutrient manipulation bioassays showed that bacterial growth was directly controlled by phosphate, while picocyanobacterial growth is controlled by light and can out-compete bacteria under P-limited condition with the aid of light. Further analysis revealed that the strength of summer typhoon is a factor responsible for the inter-annual variability of bulk and specific APA. APA study demonstrated the episodic events (e.g. strong typhoon and extreme precipitation) had significant influence on APA variability in sub-tropical to tropical aquatic ecosystems. Hence, the results herein will allow future studies on monitoring typhoon disturbance (intensity and frequency) as well as the APA of plankton during summer-to-autumn in subtropical systems.
Tseng, Y.
Downscaling ocean conditions: Experiments with a quasi-geostrophic model
NASA Astrophysics Data System (ADS)
The predictability of small-scale ocean variability, given the time history of the associated large-scales, is investigated using a quasi-geostrophic model of two wind-driven gyres separated by an unstable, mid-ocean jet. Motivated by the recent theoretical study of Henshaw et al. (2003), we propose a straightforward method for assimilating information on the large-scale in order to recover the small-scale details of the quasi-geostrophic circulation. The similarity of this method to the spectral nudging of limited area atmospheric models is discussed. Results from the spectral nudging of the quasi-geostrophic model, and an independent multivariate regression-based approach, show that important features of the ocean circulation, including the position of the meandering mid-ocean jet and the associated pinch-off eddies, can be recovered from the time history of a small number of large-scale modes. We next propose a hybrid approach for assimilating both the large-scales and additional observed time series from a limited number of locations that alone are too sparse to recover the small scales using traditional assimilation techniques. The hybrid approach improved significantly the recovery of the small-scales. The results highlight the importance of the coupling between length scales in downscaling applications, and the value of assimilating limited point observations after the large-scales have been set correctly. The application of the hybrid and spectral nudging to practical ocean forecasting, and projecting changes in ocean conditions on climate time scales, is discussed briefly.
Katavouta, A.; Thompson, K. R.
Verification of GCM-Generated Regional Seasonal Precipitation for Current Climate and of Statistical Downscaling Estimates under Changing Climate Conditions
Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the
Aristita Busuioc; Hans von Storch; Reiner Schnur
Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method
Changes in the Baiu rainband owing to global warming are assessed by the pseudo global warming downscaling method (PGW-DS). The PGW-DS is similar to the conventional dynamical downscaling method using a regional climate model (RCM), but the boundary conditions of the RCM are obtained by adding the difference between the future and present climates simulated by coupled general circulation models
Hiroaki Kawase; Takao Yoshikane; Masayuki Hara; Fujio Kimura; Tetsuzo Yasunari; Borjiginte Ailikun; Hiroaki Ueda; Tomoshige Inoue
A semiempirical downscaling approach for predicting regional temperature impacts associated with climatic change
A statistical downscaling approach is developed for generating regional temperature change predictions from GCM results. The approach utilizes GCM free atmosphere output and surface observations in a framework conceptually similar to the model output statistics approach common in the forecasting community. The appropriateness of this approach is demonstrated through a comparison of GCM and observed free atmosphere variables. Seasonal downscaling models are presented for eight sites within four community climate model (CCM) grid cells in the US. The majority of these models are capable of explaining more than 90% of the variance in the temperature time series. The results indicate a wide range of differences between downscaled climate change predictions and grid cell-level CCM predictions.
Sailor, D.J.; Li, X. [Tulane Univ., New Orleans, LA (United States). Dept. of Mechanical Engineering
Statistical Downscaling of Intensity and Frequency Predictions of Local and Regional Extreme Precipitation
NASA Astrophysics Data System (ADS)
The modeling of precipitation at local, regional and global scale remains uncertain, and for many regions, problematic. Further, the downscaling of precipitation values from gridded data to single point locations poses many challenges since precipitation in general is not a 'single variable,' but rather a composite response to a variety of atmospheric variables. Multiple linear regression using point gauge measurements (SMHI) as the dependent variable and three different sets of ECMWF reanalysis (model) output variables as independent variables for 5 locations in Sweden suggests that for statistical downscaling of modeled precipitation, three additional variables should be included in the calculation: percentage of high cloud cover, mean sea level pressure and top minus surface radiation. We investigate the suitability of usage of these variables in a downscaling procedure, develop a predictive model based on these findings, then extend the method to CMIP5 results.
Allen, M. R.; Fu, J. S.; Bozdogan, H.
NARCliM regional downscaling project in Australia: Long-term climatological analysis of the control period
NASA Astrophysics Data System (ADS)
NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modeling project for the Australian area. It will provide a comprehensive dynamically downscaled climate dataset for the CORDEX-AustralAsia region at 50km, and South-East Australia at a resolution of 10km. NARCliM data will be used by state governments to design their climate change adaptation plans. It runs an ensemble of WRF simulations using three different physical configurations and four different GCMs for the present and future periods along three different time-windows (1990-2010, 2020-2040 and 2060-2080). We will present the validation of the control period (1950-2009) using the NNRP re-analysis. Simulated climatologies are compared with observed ones from a gridded data-set (AWAP) comparing observed and simulated seasonal climatologies and long-term series based on the climatological sensitivity to different climate indices (representing modes of variability including ENSO, the Indian Ocean Dipole, and the Southern Annular Mode which affect the Australia climate). Results show that the performance of the simulated climate presents a regional (from tropical to desert areas), seasonal and variable (precipitation and minimum/maximum daily temperatures) sensitivity without a clear outperforming physical configuration. Long-term analysis (mostly by means of correlations with the time-series of the indices) shows that increasing spatial resolution has a positive impact on how the model represents the continental climate response to the large scale and improves the results from the data providing the boundary conditions (NNRP) taking the response of the observations as the reference.
Fita, Lluís; Argüeso, Daniel; Evans, Jason P.; King, Andrew D.
Downscaling Regional Circulation Model rainfall to gauge sites using recorrelation and circulation pattern dependent quantile-quantile transforms for quantifying climate change
NASA Astrophysics Data System (ADS)
We have successfully downscaled RCM rainfall to gauge scale recapturing statistics.We devised novel techniques for recorrelation and quantile transformation of data.We have recorrelated downscaled gauge rainfall to match observed correlations.We show that recorrelated gauge values recapture large-scale spatial correlations.We validated downscaled RCM rainfall at gauge sites over five regions in South Africa.
Pegram, Geoffrey; Bárdossy, András
An Assessment of Two Statistical Downscaling Techniques for Generating Daily Climate Data for Central Canada
NASA Astrophysics Data System (ADS)
General Circulation Models, or Global Climate Models (GCMs), are widely used to assess potential impacts of global climate change because they are designed to simulate the present climate and project future climate. They however are not designed for local climate change impact studies and do not permit a good estimation of hydrological responses to climate change by themselves because of their coarse spatial scales. Statistical downscaling techniques have recently emerged as useful tools to convert the GCM outputs into a scale useful for climate change impact studies. These techniques are able to generate scenarios for a local site by using a statistically based model to represent the relationship between large scale climate variables and local climate variables. To date there have been several statistical downscaling techniques proposed in the scientific literature, each having its own advantages and shortcomings. Since there are many factors which can influence a downscaling model, such as the topography of the region, it is essential that a rigorous evaluation of the different statistical downscaling methods be undertaken. This will ensure that the most suitable approach is chosen to meet the conditions of the region. The objective of this study is to test two popular statistical downscaling methods, the Statistical Downscaling Model (SDSM) and the Stochastic Weather Generator (LARS-WG) for their ability to simulate daily time series of local precipitation and temperature for meteorological stations located in Central Canada. The evaluation will not only consist of examining the models ability to simulate means but will also examine their ability to simulate the magnitude and occurrence of extremes. These models will then applied to the GCM output from the Canadian Center for Climate Modeling and Analysis (CCCma) third generation model, CGCM3 T47 using the SRESA1, SRESA1B, and SRESA2 scenarios for two future time periods (2046-2065, 2081-2091) to project future climate change scenarios for these sites.
Koenig, K. A.; Rasmussen, P. F.
Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational ?1-Norm Regularization in the Derivative Domain
NASA Astrophysics Data System (ADS)
The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ?1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.
Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.
Possible Impacts of Climate Change on Wind Gust under Downscaled Future Climate Conditions over Ontario, Canada
NASA Astrophysics Data System (ADS)
The overarching purpose of this study was to project changes in the occurrence frequency and magnitude of future wind gust events under downscaled future climate conditions over Ontario, Canada. Wind gust factors were employed to simulate hourly/daily wind gust based on hourly/daily wind speed. Regression-based downscaling methods were used to downscale future hourly/daily wind speed to each of the 14 selected cities in Ontario for eight GCM models with IPCC SRES A2 and B1 scenarios. The wind gust simulation models were then applied using downscaled future GCM wind speed data to project changes in occurrence frequency and intensity of the future hourly/daily wind gust events. Downscaling transfer functions and wind gust simulation models were validated using a cross-validation scheme and comparing data distributions and extreme-event frequencies derived from downscaled GCM control runs and observations over a comparative time period 1961-2000. The results showed that the models for all variables used in the study performed well. By comparing the current-past averaged conditions, the occurrence frequency and intensity of future wind gust events in the study area are projected to increase. The modeled results from this study found that the frequency and intensity of future wind gust events are projected to significantly increase under a changing climate in this century. This talk will introduce the research project and outline the modeling exercise and verification process. The major findings on future wind gust projections from the study will be summarized in the presentation as well. One of the major conclusions from the study is that the procedures used in the study are useful for climate change impact analysis on future wind gusts. The implication of the significant increases in future wind gust risks would be useful to be considered when revising engineering infrastructure design standards and developing adaptation strategies and policies.
Shouquan Cheng, Chad; Li, Guilong
Processing and analysing an ensemble of climate projections for the joint research project KLIWAS
NASA Astrophysics Data System (ADS)
The research programme KLIWAS, funded by the German Federal Ministry of Transport, Building and Urban affairs is focussed on climate change and its impacts on waterways and navigation for Germany in the 21th century. In order to derive sound statements about the range of possible future climate changes, KLIWAS use hydro-meteorological information derived from a wide variety of global and regional climate models. In the framework of KLIWAS emphasis is taken on the quantification of uncertainties in climate model output. Therefore, a 19-member ensemble of climate model runs was used. On the basis of the SRES-scenario A1B the probabilities of changes in air temperature, precipitation amount, global radiation and several climate indices were computed for near (2021 to 2050) and distant (2071-2100) scenario horizons. Furthermore, statistical downscaling techniques, including approved bias correction methods, were used to provide a spatial high-resolution sub-ensemble of eight climate model simulations for climate change impact investigations.
Imbery, F.; Plagemann, S.; Namyslo, J.
Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis
The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.
Flint, Lorraine E.; Flint, Alan L.
A comparison of breeding and ensemble transform vectors for global ensemble generation
NASA Astrophysics Data System (ADS)
To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors, three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration (NMC/CMA). A series of ensemble verification scores such as forecast skill of the ensemble mean, ensemble resolution, and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems. The results indicate that the ensemble transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC), which is a deterministic character of the ensemble mean, the root-mean-square error (RMSE) and spread, which are of probabilistic attributes, and the continuous ranked probability score (CRPS) and its decomposition. The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation.
Deng, Guo; Tian, Hua; Li, Xiaoli; Chen, Jing; Gong, Jiandong; Jiao, Meiyan
Climate change scenarios of temperature and precipitation over five Italian regions for the period 2021-2050 obtained by statistical downscaling models
NASA Astrophysics Data System (ADS)
Climate change scenarios of seasonal maximum, minimum temperature and precipitation in five Italian regions, over the period 2021-2050 against 1961-1990 are assessed. The regions selected by the AGROSCENARI project are important from the local agricultural practises and are situated as follows: in the Northern Italy - Po valley and hilly area of Faenza; in Central part of Italy- Marche, Beneventano and Destra Sele, and in Sardinia Island - Oristano. A statistical downscaling technique applied to the ENSEMBLES global climate simulations, A1B scenario, is used to reach this objective. The method consists of a multivariate regression, based on Canonical Correlation Analysis, using as possible predictors mean sea level pressure, geopotential height at 500hPa and temperature at 850 hPa. The observational data set (predictands) for the selected regions is composed by a reconstruction of minimum, maximum temperature and precipitation daily data on a regular grid with a spatial resolution of 35 km, for 1951-2009 period (managed by the Meteorological and Climatological research unit for agriculture - Agricultural Research Council, CRA - CMA). First, a set-up of statistical model has been made using predictors from ERA40 reanalysis and the seasonal indices of temperature and precipitation from local scale, 1958-2002 period. Then, the statistical downscaling model has been applied to the predictors derived from the ENSEMBLES global climate models, A1B scenario, in order to obtain climate change scenario of temperature and precipitation at local scale, 2021-2050 period. The projections show that increases could be expected to occur under scenario conditions in all seasons, in both minimum and maximum temperature. The magnitude of changes is more intense during summer when the changes could reach values around 2°C for minimum and maximum temperature. In the case of precipitation, the pattern of changes is more complex, different from season to season and over the regions, a reduction of precipitation could be expected to occur during summer. The temperature and precipitation projections from hilly area of Faenza are then used as input in a weather generator, in order to produce a synthetic series of daily data. These series feed a water balance and crop growth model (CRITERIA) to evaluate the impact of climate change scenario in irrigation crop water needs, for 2021-2050 period. As reference crop the kiwifruit, which is characterised by high water need and widespread in this area, has been selected.
Tomozeiu, R.; Tomei, F.; Villani, G.; Pasqui, M.
Controlling balance in an ensemble Kalman filter
NASA Astrophysics Data System (ADS)
We present a method to control unbalanced fast dynamics in an ensemble Kalman filter by introducing a weak constraint on the imbalance in a spatially sparse observational network. We show that the balance constraint produces significantly more balanced analyses than ensemble Kalman filters without balance constraints and than filters implementing incremental analysis updates (IAU). Furthermore, our filter with the weak constraint on imbalance produces good rms error statistics which outperform those of ensemble Kalman filters without balance constraints for the fast fields.
Gottwald, G. A.
Multivariate Ensemble Sensitivity with Localization
NASA Astrophysics Data System (ADS)
So far in the literature, covariance localization (tapering) has not been applied when performing ensemble sensitivity analysis. Sampling error in computing the sensitivities via lagged covariances leads to an over-estimation of the impact of a perturbation. Most commonly when computing sensitivities, the analysis covariance is approximated with the corresponding diagonal matrix. Two consequences follow: (1) the multi-variate sensitivity is approximated by a univariate sensitivity, and (2) sampling error in off-diagonal elements are obviated. It is unknown, however, how much information is lost by ignoring the off-diagonal elements in the full covariance. When forecasts depend on many details of the previous analysis, it is reasonable to expect that the diagonal approximation is too severe. The purpose of this presentation is to clarify the effects of the diagonal approximation, and investigate the need for localization when off-diagonal elements are considered. Motivated by examples arising from sensitivities estimated within a cycling mesoscale ensemble data assimilation system, for easier interpretation we turn to the two-scale model first presented by Lorenz in 2005. We show that for most problems, an efficient matrix inversion is possible by finding a minimum-norm solution, and employing appropriate matrix factorization. Comparing the full inversion with off-diagonal elements, the fine-scale sensitivity estimates can be substantially different from those arising when the diagonal approximation is used. Localization on the sensitivity can be handled by an off-line empirical or Bayesian estimation technique. Because the sensitivity estimated from the full inversion is subject to sampling error, it is sensitive to the localization. The results show that compared to typical practices, more complete ensemble sensitivity formulations may be needed to draw robust inferences in general.
Hacker, Joshua; Lei, Lili
An altitude chamber rescue ensemble
NASA Technical Reports Server (NTRS)
Altitude chamber tests accomplished with the astronaut crews in the spacecraft at a simulated altitude of above 200,000 ft requires that a rescue team be provided in the event of an accident in the spacecraft. The rescue crew is stationed in an airlock maintained at an altitude of 18,000 ft. A protective ensemble provides the rescue crew with life support capabilities, communications, and protection in the event of an emergency. In the event of an emergency, repressurization of the chamber is initiated; as the chamber descends, the airlock ascends and the two meet at 25,000 ft. This phase of the emergency repressurization takes less than 30 sec.
Lloyd, R. P.
Extended Gibbs ensembles with flow
A recently proposed [Ph. Chomaz, F. Gulminelli, and O. Juillet, Ann. Phys. (Paris) 320, 135 (2005)] statistical treatment of finite unbound systems in the presence of collective motions is applied to a classical Lennard-Jones system, numerically simulated through molecular dynamics. In the ideal gas limit, the flow dynamics can be exactly recast into effective time-dependent Lagrange parameters acting on a standard Gibbs ensemble with an extra total energy conservation constraint. Using this same ansatz for the low-density freeze-out configurations of an interacting expanding system, we show that the presence of flow can have a sizable effect on the microstate distribution.
Ison, M. J. [Departamento de Fisica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428 (Argentina); LPC Caen, ENSICAEN, Universite de Caen, CNRS/IN2P3, Caen (France); Gulminelli, F. [LPC Caen, ENSICAEN, Universite de Caen, CNRS/IN2P3, Caen (France); Dorso, C. O. [Departamento de Fisica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428 (Argentina)
Ensemble averaging of acoustic data
NASA Technical Reports Server (NTRS)
A computer program called Ensemble Averaging of Acoustic Data is documented. The program samples analog data, analyzes the data, and displays them in the time and frequency domains. Hard copies of the displays are the program's output. The documentation includes a description of the program and detailed user instructions for the program. This software was developed for use on the Ames 40- by 80-Foot Wind Tunnel's Dynamic Analysis System consisting of a PDP-11/45 computer, two RK05 disk drives, a tektronix 611 keyboard/display terminal, and FPE-4 Fourier Processing Element, and an analog-to-digital converter.
Stefanski, P. K.
A Localized Ensemble Kalman Smoother
NASA Technical Reports Server (NTRS)
Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother.
Butala, Mark D.
Extended Gibbs ensembles with flow.
A recently proposed [Ph. Chomaz, F. Gulminelli, and O. Juillet, Ann. Phys. (Paris) 320, 135 (2005)] statistical treatment of finite unbound systems in the presence of collective motions is applied to a classical Lennard-Jones system, numerically simulated through molecular dynamics. In the ideal gas limit, the flow dynamics can be exactly recast into effective time-dependent Lagrange parameters acting on a standard Gibbs ensemble with an extra total energy conservation constraint. Using this same ansatz for the low-density freeze-out configurations of an interacting expanding system, we show that the presence of flow can have a sizable effect on the microstate distribution. PMID:18233636
Ison, M J; Gulminelli, F; Dorso, C O
A downscaling method for the assessment of local climate change
NASA Astrophysics Data System (ADS)
The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by downscaling GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a 'Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The statistical parameters characterizing daily storm occurrence, storm intensity and duration needed to apply the PRP scheme are considered among STARDEX collection of extreme indices.
Bruno, E.; Portoghese, I.; Vurro, M.
Discrete orthogonal polynomial ensembles and the Plancherel measure
We consider discrete orthogonal polynomial ensembles which are discrete analogues of the orthogonal polynomial ensembles in random matrix theory. These ensembles occur in certain problems in combinatorial probability and can be thought of as probability measures on partitions. The Meixner ensemble is related to a two-dimensional directed growth model, and the Charlier ensemble is related to the lengths of weakly
Kurt Johansson
Convergence of the ensemble Kalman filter in the limit for large ensembles to the Kalman filter is proved. In each step of the filter, convergence of the ensemble sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the ensemble members, and Lp bounds on the ensemble then give Lp convergence.
Mandel, Jan; Cobb, Loren; Beezley, Jonathan D.
The utility of MODIS products for downscaling AMSR-E soil moisture estimates
NASA Astrophysics Data System (ADS)
Recently, the use of microwave observations has been highlighted as a complementary tool for evaluating land surface properties. Microwave observations are less affected by clouds, water vapor and aerosol and also contain valuable soil moisture information. However, a critical limitation in these observations is the coarse spatial resolution attributed to the complex retrieval process. As a result, the best hope for providing high- resolution microwave-related products is to integrate visible, NIR/Infrared and microwave observations. In this context, we explore a downscaling approach for combining surface temperature and vegetation indices from higher spatial resolution MODIS (1km) and soil moisture from lower spatial resolution AMSR-E (25km) to obtain soil moisture at the MODIS scale (1km). The linkage is based on l integrating high resolution surface temperature and vegetation indices, through the triangle/trapezoid model, to provide an indication of relative variations in surface wetness conditions. These relative variations provide weighting parameters for downscaling of the large footprint (25km) of soil moisture to the MODIS scale (1km). Initial evaluation of the downscaled soil moisture product is undertaken at a series of Soil Moisture EXperiment (SMEX) sites conducted annually from 2002 to 2004 (Iowa, Georgia and Arizona). This concentrated validation yields insight into how well the proposed downscaling method integrates multi-scale data and the usefulness of MODIS observations in compensating for the coarse resolution of microwave observations.
Kim, J.; Hogue, T.
Statistical downscaling of GCM simulations to streamflow using relevance vector machine
General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to
Subimal Ghosh; P. P. Mujumdar
Exploring two methods for statistical downscaling of Central European phenological time series
In this study we set out to investigate the possibility of linking phenological phases throughout the vegetation cycle, as a local-scale biological phenomenon, directly with large-scale atmospheric variables via two different empirical downscaling techniques. In recent years a number of methods have been developed to transfer atmospheric information at coarse General Circulation Model's grid resolutions to local scales and individual
C. Matulla; H. Scheifinger; A. Menzel; E. Koch
A case study of statistical downscaling in Australia using weather classification by recursive partitioning
Because of their coarse resolution General Circulation Models (GCMs) lack the ability to predict reliably surface variables that may be needed for climate effects studies on a regional scale. Stochastic downscaling methods, based on the relationship between large-scale circulation types and regional surface variables, offer one approach to bridge this scale mismatch. The method explored in this paper uses recursive
Reiner Schnur; Dennis P Lettenmaier
1. NTRODUCTION Analyses performed for the US National Assessment require accurate projections of climate at scales below those resolved by global General Circulation Models (GCMs). Two techniques have been developed that counter this deficiency: semi- empirical (statistical) downscaling (SDS) of GCM outputs, and regional climate models (RCMs) nested within a GCM. To date, few studies have compared SDS and RCM
W. J. Gutowski; R. Wilby; L. E. Hay; C. J. Anderson; R. W. Arritt; M. P. Clark; G. H. Leavesley; Z. Pan; R. Silva; E. S. Takle
Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs
Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the
Andrew W. Wood; Lai R. Leung; V. Sridhar; D. P. Lettenmaier
On the Choice of the Temporal Aggregation Level for Statistical Downscaling of Precipitation
The merits of daily and monthly downscaling models for precipitation are compared using data from Bern, Switzerland; Deuselbach, Germany; and De Bilt, the Netherlands. For each station, generalized linear models are developed to describe rainfall occurrence, the wet-day precipitation amounts, and the monthly precipitation totals. The predictor dataset includes dynamical variables and atmospheric moisture (relative humidity for rainfall occurrence and
T. A. Buishand; M. V. Shabalova; T. Brandsma
An estimate of future climate change for western France using a statistical downscaling technique
A statistical downscaling procedure based on an analogue technique is used to determine projections for future climate change in western France. Three ocean and atmosphere coupled models are used as the starting point of the regionalization technique. Models' climatology and day to day variability are found to reproduce the broad main characteristics seen in the reanalyses. The response of the
B. Timbal; A. Dufour; B. McAvaney
A statistical method to downscale aggregated land use data and scenarios
This paper presents a method to downscale aggregated land use data based on statistical techniques. A purely spatial multinomial logistic regression (MNLR) model is proposed using observed fine resolution land use data. This model provides initial probability maps of land use presence, which are updated using aggregated land use data and an iterative procedure based on Bayes' theorem. The simplicity
N. Dendoncker; P. Bogaert; M. Rounsevell
Generalization of a statistical downscaling model to provide local climate change projections for Australia
Climate change information required for impact studies is of a much finer spatial scale than climate models can directly provide. Statistical downscaling models (SDMs) are commonly used to fill this scale gap. SDMs are based on the view that the regional climate is conditioned by two factors: (1) the large- scale climatic state and (2) local physiographic features. An SDM
B. Timbal; E. Fernandez; Z. Li
A simple statistical-dynamical downscaling scheme based on weather types and conditional resampling
A multivariate statistical downscaling methodology is implemented to generate local precipitation and temperature series at different sites based on the results from a variable resolution general circulation model. It starts from regional climate properties to establish discriminating weather types for the chosen local variable, precipitation in this case. Intratype variations of the relevant forcing parameters are then taken into account
J. Boé; L. Terray; F. Habets; E. Martin
Downscaling of global climate models for flood frequency analysis: where are we now?
The issues of downscaling the results from global climate models (GCMs) to a scale relevant for hydrological impact studies are examined. GCM outputs, typically at a spatial resolution of around 3° latitude and 4° longitude, are currently not considered reliable at time scales shorter than 1 month. Continuous rainfall-runoff modelling for flood regime assessment requires input at the daily or
Christel Prudhomme; Nick Reynard; Sue Crooks
Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture downscaling
NASA Astrophysics Data System (ADS)
Downscaling methods have been proposed to estimate catchment-scale soil moisture patterns from coarser resolution patterns. These methods usually infer the fine-scale variability in soil moisture using variations in ancillary variables like topographic attributes that have relationships to soil moisture. Previously, such relationships have been observed in catchments using soil moisture observations taken on uniform grids at hundreds of locations on multiple dates, but collecting data in this manner limits the applicability of this approach. The objective of this paper is to evaluate the effectiveness of two strategic sampling techniques for characterizing the relationships between topographic attributes and soil moisture for the purpose of constraining downscaling methods. The strategic sampling methods are conditioned Latin hypercube sampling (cLHS) and stratified random sampling (SRS). Each sampling method is used to select a limited number of locations or dates for soil moisture monitoring at three catchments with detailed soil moisture datasets. These samples are then used to calibrate two available downscaling methods, and the effectiveness of the sampling methods is evaluated by the ability of the downscaling methods to reproduce the known soil moisture patterns. cLHS outperforms random sampling in almost every case considered. SRS usually performs better than cLHS when very few locations are sampled, but it can perform worse than random sampling for intermediate and large numbers of locations.
Werbylo, Kevin L.; Niemann, Jeffrey D.
Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations
NASA Astrophysics Data System (ADS)
Dynamical downscaling has been recognized as a useful tool not only for the climate community, but also for associated application communities such as the environmental and hydrological societies. Although climate projection data are available in lower-resolution general circulation models (GCMs), higher-resolution climate projections using regional climate models (RCMs) have been obtained over various regions of the globe. Various model outputs from RCMs with a high resolution of even as high as a few km have become available with heavy weight on applications. However, from a scientific point of view in numerical atmospheric modeling, it is not clear how to objectively judge the degree of added value in the RCM output against the corresponding GCM results. A key factor responsible for skepticism is based on the fundamental limitations in the nesting approach between GCMs and RCMs. In this article, we review the current status of the dynamical downscaling for climate prediction, focusing on basic assumptions that are scrutinized from a numerical weather prediction (NWP) point of view. Uncertainties in downscaling due to the inconsistencies in the physics packages between GCMs and RCMs were revealed. Recommendations on how to tackle the ultimate goal of dynamical downscaling were also described.
Hong, Song-You; Kanamitsu, Masao
Development of a European Windstorm Event Set using a Combined Dynamical and Statistical Downscaling Approach
NASA Astrophysics Data System (ADS)
Winter storms cause very high insurance losses in Europe. In order to allow for a valuable risk assessment, both storm frequency on a large scale and storm and gust intensity on a small scale are precondition for construction of loss estimation tools. The presented common effort between research and insurance consists of a large scale identification of intense storms for both historical (reanalysis) data and present day climate simulations in order to extend the statistical basis of extreme events to a number of 10000 storms. For historical storms, dynamical downscaling is performed with the regional climate model COSMO-CLM. Since dynamical downscaling is not feasible for 10000 events, a statistical downscaling tool is derived from large scale storm tracks, historical storms in the period 1960-2010, defined from potential loss estimation based on NCEP reanalyses, re-simulated in a two-step nesting approach using COSMO-CLM 4.8 in 0.165° and 0.0625° resolution with ERA-forcing and from observations. A method of a combined probabilistic downscaling and MOS technique is proposed for the enhancement of gust speed estimations. The methodical procedure is presented along with results and a quality check for both spatial and temporal correctness, considering errors in terms of RMSE and the form of gust distributions in order to provide gust estimations which are unbiased in comparison to the observations.
Born, K.; Drinka, R.; Georgiadis, A.; Haas, R.; Ludwig, P.; Karremann, M. K.; Podlaha, A.; Ulbrich, S.; Pinto, J. G.
Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States
High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.
Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.
Downscaling IPCC control run and future scenario with focus on the Barents Sea
NASA Astrophysics Data System (ADS)
Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric downscaling of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional downscaling of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional downscalings of the present-day climate as well as projected climate change. Thus, we study separately how downscaling changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that downscaling reduces the differences in the Barents Sea between the original global models. Furthermore, the downscaled results are closer to observations. On the other hand, the downscaled results from the scenario simulations are significantly different: while the heat transport into the Barents Sea and the salinity distribution change modestly from control to scenario with ROMS-G, in ROMS-N the heat transport is much larger in the scenario simulation, and the water masses become much less saline. The lack of robustness in the results from the scenario simulations leads us to conclude that the results for the regional oceanic response to changes in the radiative forcing depend on the choice of AOGCM and is not settled. Consequently, the effect of climate change on the marine ecosystem of the Barents Sea is anything but certain.
Sandø, Anne Britt; Melsom, Arne; Budgell, William Paul
Addressing impacts of different statistical downscaling methods on large scale hydrologic simulations
NASA Astrophysics Data System (ADS)
Many hydrologic assessments, such as evaluations of climate change impacts on water resources, require downscaled climate model outputs to force hydrologic simulations at a spatial resolution finer than the climate models' native scale. Statistical downscaling is an attractive alternative to dynamical downscaling methods for continental scale hydrologic applications because of its lower computational cost. The goal of this study is to illustrate and compare how the errors in precipitation and temperature produced by different statistical downscaling methods propagate into hydrologic simulations. Multi-decadal hydrologic simulations were performed with three process-based hydrologic models (CLM, VIC, and PRMS) forced by multiple climate datasets over the contiguous United States. The forcing datasets include climate data derived from gauge observations (M02) as well as climate data downscaled from the NCEP-NCAR reanalysis using 4 statistical downscaling methods for a domain with 12-km grid spacing: two forms of Bias Corrected Spatially Disaggregated methods (BCSD-monthly and BCSD-daily), Bias Corrected Constructed Analogue (BCCA), and Asynchronous Regression (AR). Our results show that both BCCA and BCSD-daily underestimate extreme precipitation events while AR produces these correctly at the scale at which the simulations were run but does not scale them up appropriately to larger basin scales like HUC-4 and HUC-2. These artifacts lead to a poor representation of flooding events when hydrologic models are forced by these methods over a range of spatial scales. We also illustrate that errors in precipitation depths dominate impacts on runoff depth estimations, and that errors in wet day frequency have a larger effect on shortwave radiation estimations than do the errors in temperatures; this error subsequently affects the partitioning of precipitation into evaporation and runoff as we show over mountainous areas of the upper Colorado River. Finally we show the inter-model differences across our simulations are generally lower than than inter-forcing data differences. We conclude with preliminary guidance on sound methodological choices for future climate impact studies using these methods. Comparison of annual precipitation between statistically downscaled data and observation (M02) and illustration of how these differences propagate into hydrologic simulations with two models. Figure shows the simulations over the western United States.
Mizukami, N.; Clark, M. P.; Gutmann, E. D.; Mendoza, P. A.; Brekke, L. D.; Arnold, J.; Raff, D. A.
Statistical Downscaling of Large-Scale Wind Signatures Using a Two-Step Approach
NASA Astrophysics Data System (ADS)
Downscaling global scale climate data is an important issue in order to obtain high-resolution data desired for most applications in meteorology and hydrology and to gain a better understanding of local climate variability. Statistical downscaling transforms data from large to local scale by relating punctual climate observations, climate model outputs and high-resolution surface data. In this study, a statistical downscaling approach is used in combination with dynamical downscaling in order to produce gust characteristics of wind storms on a small-scale grid over Europe. The idea is to relate large-scale data, regional climate model (RCM) data and observations by transfer functions, which are calibrated using physically consistent features of the RCM model simulations. In comparison to purely dynamical downscaling by a regional model, such a statistical downscaling approach has several advantages. The computing time is much shorter and, therefore, such an approach can be easily applied on very large numbers of windstorm cases provided e.g. by long-term GCM model simulations, like millennium runs. The first step of the approach constructs a relation between observations and COSMO-CLM signatures with the aim of calibrating the modelled signatures to the observations in terms of model output statistics. For this purpose, parameters of the theoretical Weibull distribution, estimated from the observations at each test site, are interpolated to a 7km RCM grid with Gaussian weights and are compared to Weibull parameters from the COSMO-CLM modelled gust distributions. This allows for an evaluation and correction of gust signatures by quantile mapping. The second step links the RCM wind signatures and large-scale data by a multiple linear regression (MLR) model. One model per grid point is trained using the COSMO-CLM simulated and MOS-corrected gusts for selected wind storm events as predictands, and the according NCEP reanalysis wind speeds of the surrounding NCEP grid points as predictors. For validation purposes, the model is again applied on NCEP reanalysis data. The statistical model is able to reproduce well the observed regional scale wind signatures. Afterwards, the statistical model is applied to ECHAM5 climate simulation data to generate large numbers of downscaled wind gust signatures at high spatial resolution. For further analyses, statistical values as mean, minimum and maximum wind gust speeds are compared at every grid point.
Haas, R.; Born, K.; Georgiadis, A.; Karremann, M. K.; Pinto, J. G.
Downscaling IPCC control run and future scenario with focus on the Barents Sea
NASA Astrophysics Data System (ADS)
Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric downscaling of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional downscaling of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional downscalings of the present-day climate as well as projected climate change. Thus, we study separately how downscaling changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that downscaling reduces the differences in the Barents Sea between the original global models. Furthermore, the downscaled results are closer to observations. On the other hand, the downscaled results from the scenario simulations are significantly different: while the heat transport into the Barents Sea and the salinity distribution change modestly from control to scenario with ROMS-G, in ROMS-N the heat transport is much larger in the scenario simulation, and the water masses become much less saline. The lack of robustness in the results from the scenario simulations leads us to conclude that the results for the regional oceanic response to changes in the radiative forcing depend on the choice of AOGCM and is not settled. Consequently, the effect of climate change on the marine ecosystem of the Barents Sea is anything but certain.
Sandø, Anne Britt; Melsom, Arne; Budgell, William Paul
Downscaling Soil Moisture Product from SMOS for Monitoring Agricultural Droughts in South America
NASA Astrophysics Data System (ADS)
Availability of reliable near-surface soil moisture (SM) estimates at fine spatial resolutions of 1 km and at temporal resolutions of a few days is critical for accurate quantification of drought impacts on crop yields and recommending meaningful management and adaptation strategies. The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future NASA-Soil Moisture Active Passive (SMAP) missions provide unprecedented, global SM product every 2-3 days at spatial resolutions of ~50 km. In addition, the SMAP will provide a SM product at 10 km . Downscaling the above SM products to 1km is essential for any meaningful drought-related application in agricultural terrains. Optimal downscaling should retain information from higher-order moments and leverage information from auxiliary remote sensing products that are available at fine resolutions. In this study, a novel downscaling methodology based upon information theory was implemented to obtain distributed SM at 1 km every 3 days, using the SM product from SMOS. Observations of land surface temperature (LST), leaf area index (LAI) and land cover (LC) at 1 km from MODIS, and precipitation at 25 km from TRMM, were used as auxiliary information to facilitate the downscaling process. The use of information-theory in downscaling provides a hierarchical decomposition of image data that is optimal in terms of the transfer of information across scales and is therefore a better alternative to methods that use second-order statistics only. The downscaling methodology was implemented over the agricultural regions in the lower La Plata Basin (L-LPB) in South America. The L-LPB region is of great economic value in South America, where agricultural cover makes up about 25% of the continent's land area and is vulnerable to high losses in crop yields due to agricultural drought . Both remote sensing and in situ observations (precipitation, temperature, and soil moisture) obtained during the drought period of 2007-2008 were used to train the downscaling methodology. Observations obtained during the growing season of 2010, during which ESA-SMOS observations were available, was used to demonstrate the feasibility of the methodology for monitoring agricultural droughts.
Nagarajan, K.; Fu, C.; Judge, J.; Fraisse, C.
Memory for Multiple Visual Ensembles in Infancy
ERIC Educational Resources Information Center
The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of ensembles that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of ensemble…
Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa
Introducing Recorder Ensembles in General Music Classes.
ERIC Educational Resources Information Center
Focuses on the use of recorder ensembles in general music classes, discussing topics such as strategies for procuring soprano, alto, and bass recorders and ensemble activities for grades 3-8. Provides a bibliography of resources for recorder playing and information on transposition and arranging music. (CMK)
Kersten, Fred
Analysis Scheme in the Ensemble Kalman Filter
This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the ensemble Kalman filter. It is shown that the observations must be treated as random variables at the analysis steps. That is, one should add random perturbations with the correct statistics to the observations and generate an ensemble of observations that then is
Gerrit Burgers; Peter Jan van Leeuwen; Geir Evensen
Memory for Multiple Visual Ensembles in Infancy
The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of ensembles that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of ensemble representations by asking whether infants
Jennifer M. Zosh; Justin Halberda; Lisa Feigenson
Ensemble: cooperative proximity-based authentication
Ensemble is a system that uses a collection of trusted personal devices to provide proximity-based authentication in pervasive environments. Users are able to securely pair their personal devices with previously unknown devices by simply placing them close to each other (e.g., users can pair their phones by just bringing them into proximity). Ensemble leverages a user's growing collection of trusted
Andre Kalamandeen; Adin Scannell; Eyal de Lara; Anmol Sheth; Anthony LaMarca
Creating Diverse Ensemble Classifiers to Reduce Supervision.
National Technical Information Service (NTIS)
Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its ge...
P. N. Melville
Downscaling of precipitation for climate change scenarios: A support vector machine approach
NASA Astrophysics Data System (ADS)
SummaryThe Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.
Tripathi, Shivam; Srinivas, V. V.; Nanjundiah, Ravi S.
Multiscale spatial recorrelation of RCM precipitation downscaling to correct predictions over large areas and small
NASA Astrophysics Data System (ADS)
In downscaling Regional Circulation Model (RCM) rainfall estimates, it is common to concentrate on the local statistics. To ignore the spatial dependence at large scales may lead to problems in hydrological applications, particularly in estimating/modelling extreme runoff from large areas, due to the wrongly modelled clustering behaviour of storms. In Bárdossy and Pegram [2011], downscaling of RCM rainfall marginal distributions, dependent on Circulation Patterns (CPs), was successfully achieved over 172 blocks of the Rhine basin at 25 km scale. Uneasy about the spatial statistics of the downscaled RCM rainfall, we calculated the spatial cross correlation coefficients (cccs) of daily rainfalls of the same set. We found that the cccs of the RCM precipitations were significantly lower than those of the observations, especially for large areas aggregated from the elemental block estimates. CP based downscaling led to a slight increase of the cccs but their values remained below those of the observed cccs. We therefore decided to perform a recorrelation treatment to correct the cccs of the RCM estimates back to the observed set, before undertaking the final quantile-quantile (Q-Q) transform. In this presentation we use a matrix method of recorrelation which was successful in that it recaptured the observed cccs almost exactly. In addition, it was demonstrated that the method coped with problems presented by the high proportion of dry days, when applied to five moderately large and climatologically different South African regions (10 000 to 14 000 sq km) in addition to the large German Rhine basin (108 000 sq km). After recorrelation, the appropriate Q-Q transforms are used to recover the appropriate distributions, and it is demonstrated that the spatial coherence of precipitation over large areas is recovered well enough to closely match that of the gauge-based observations. Bárdossy, A., and G. Pegram (2011), Downscaling precipitation using regional climate models and circulation patterns toward hydrology, Water Resources Research, 47(W04505), doi:10.1029/2010WR009,689.
Pegram, G. G. S.; Bárdossy, A.
The concept of exchangeability in ensemble forecasting
NASA Astrophysics Data System (ADS)
A set of random variables is exchangeable if its joint distribution function is invariant under permutation of the arguments. The concept of exchangeability is discussed, with a view towards potential application in evaluating ensemble forecasts. It is argued that the paradigm of ensembles being an independent draw from an underlying distribution function is probably too narrow; allowing ensemble members to be merely exchangeable might be a more versatile model. The question is discussed whether established methods of ensemble evaluation need alteration under this model, with reliability being given particular attention. It turns out that the standard methodology of rank histograms can still be applied. As a first application of the exchangeability concept, it is shown that the method of minimum spanning trees to evaluate the reliability of high dimensional ensembles is mathematically sound.
Bröcker, J.; Kantz, H.
Feature selection inspired classifier ensemble reduction.
Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets. PMID:24108493
Diao, Ren; Chao, Fei; Peng, Taoxin; Snooke, Neal; Shen, Qiang
Statistical downscaling of winter monthly mean North Atlantic sea-level pressure to sea level variations in the Baltic Sea.
National Technical Information Service (NTIS)
The term ''downscaling'' describes a procedure in which information about a process with a certain characteristic scale is derived from other processes with larger scales. The present paper identifies a relationship between the main components of the Nort...
H. Heyen E. Zorita H. Storch
High-resolution climate simulations for Central Europe: An assessment of dynamical and statistical downscaling techniques
NASA Astrophysics Data System (ADS)
To bridge the resolution gap between the outputs of global climate models (GCMs) and finer-scale data needed for studies of the climate change impacts, two approaches are widely used: dynamical downscaling, based on application of regional climate models (RCMs) embedded into the domain of the GCM simulation, and statistical downscaling (SDS), using empirical transfer functions between the large-scale data generated by the GCM and local measurements. In our contribution, we compare the performance of different variants of both techniques for the region of Central Europe. The dynamical downscaling is represented by the outputs of two regional models run in the 10 km horizontal grid, ALADIN-CLIMATE/CZ (co-developed by the Czech Hydrometeorological Institute and Meteo-France) and RegCM3 (developed by the Abdus Salam Centre for Theoretical Physics). The applied statistical methods were based on multiple linear regression, as well as on several of its nonlinear alternatives, including techniques employing artificial neural networks. Validation of the downscaling outputs was carried out using measured data, gathered from weather stations in the Czech Republic, Slovakia, Austria and Hungary for the end of the 20th century; series of daily values of maximum and minimum temperature, precipitation and relative humidity were analyzed. None of the regional models or statistical downscaling techniques could be identified as the universally best one. For instance, while most statistical methods misrepresented the shape of the statistical distribution of the target variables (especially in the more challenging cases such as estimation of daily precipitation), RCM-generated data often suffered from severe biases. It is also shown that further enhancement of the simulated fields of climate variables can be achieved through a combination of dynamical downscaling and statistical postprocessing. This can not only be used to reduce biases and other systematic flaws in the generated time series, but also to further localize the RCM outputs beyond the resolution of their original grid. The resulting data then provide a suitable input for subsequent studies of the local climate and its change in the target region.
Miksovsky, J.; Huth, R.; Halenka, T.; Belda, M.; Farda, A.; Skalak, P.; Stepanek, P.
Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods
Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1\\/8- degree (approximately 140km2 per grid cell) resolution over the western U.S. We use NCEP\\/NCAR Reanalysis data from 1950-1999 as a surrogate General Circulation Model (GCM). The two
E. P. Maurer; H. G. Hidalgo
A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in the Australian region
. ?The study seeks to describe one method of deriving information about local daily temperature extremes from larger scale atmospheric\\u000a flow patterns using statistical tools. This is considered to be one step towards downscaling coarsely gridded climate data\\u000a from global climate models (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch\\u000a between GCMs and climate
S. Schubert; A. Henderson-Sellers
A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape
NASA Astrophysics Data System (ADS)
Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (?1000 m) thermal images with high revisiting capability (?1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMSDS) and (v) Pace regression downscaling (PRDS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMSDS and PRDS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMSDS and PRDS models, respectively. The LMSDS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.
Mukherjee, Sandip; Joshi, P. K.; Garg, R. D.
Ensemble Modeling of CME Propagation
NASA Astrophysics Data System (ADS)
The current progression toward solar maximum provides a unique opportunity to use multi-perspective spacecraft observations together with numerical models to better understand the evolution and propagation of coronal mass ejections (CMEs). Of interest to both the scientific and forecasting communities are the Earth-directed "halo" CMEs, since they typically produce the most geoeffective events. However, determining the actual initial geometries of halo CMEs is a challenge due to the plane-of-sky projection effects. Thus the recent 15 February 2011 halo CME event has been selected for this study. During this event the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft were fortuitously located ˜ 90° away from the Sun-Earth line such that the CME was viewed as a limb event from these two spacecraft, thereby providing a more reliable constraint on the initial CME geometry. These multi-perspective observations were utilized to provide a simple geometrical description that assumes a cone shape for a CME to calculate its angular width and central position. The event was simulated using the coupled Wang-Sheeley-Arge (WSA)-Enlil 3D numerical solar corona-solar wind model. Daily updated global photospheric magnetic field maps were used to drive the background solar wind. To improve our modeling techniques, the sensitivity of the modeled CME arrival times to the initial input CME geometry was assessed by creating an ensemble of numerical simulations based on multiple sets of cone parameters for this event. It was found that the accuracy of the modeled arrival times not only depends on the initial input CME geometry, but also on the accuracy of the modeled solar wind background, which is driven by the input maps of the photospheric field. To improve the modeling of the background solar wind, the recently developed data-assimilated magnetic field synoptic maps produced by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model were used. The ADAPT maps provide a more instantaneous snapshot of the global photospheric field distribution than that provided by traditional daily updated synoptic maps. Using ADAPT to drive the background solar wind, an ensemble set of eight different CME arrival times was generated, where the spread in the predictions was ˜ 13 hours and was nearly centered on the observed CME shock arrival time.
Lee, C. O.; Arge, C. N.; Odstr?il, D.; Millward, G.; Pizzo, V.; Quinn, J. M.; Henney, C. J.
Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters
This paper considers implications of different forms of the ensemble transformation in the ensemble square root filters (ESRFs) for the performance of ESRF-based data assimilation systems. It highlights the importance of using mean-preserving solutions for the ensemble transform matrix (ETM). The paper shows that an arbitrary mean-preserving ETM can be represented as a product of the symmetric solution and an
Pavel Sakov; Peter R. Oke
Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America
NASA Astrophysics Data System (ADS)
This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric-ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.
Johnson, Bradford; Kumar, Vinay; Krishnamurti, T. N.
Statistical downscaling of GCM scenarios - what can we learn from using different models as forcing sources?
NASA Astrophysics Data System (ADS)
As the saying goes "if you have one clock you know what time it is, but if you have several you cannot be all too sure ...". Rather often the basic information for an investigation and perhaps an ensuing decision process is derived from one run of one large scale circulation model. The study presented here applies the output of different global models, and, if applicable, different model runs, to a statistical downscaling model. All other things being equal it enables to obtain insight of the statistical model's variability and its sensitivity concerning different forcing sources. The results presented include an analysis of the accuracy with which the downscaled information reproduces the current climate conditions as well as insight into the bandwidth of future climate projections according to the different forcing models.
Spekat, A.; Kreienkamp, F.; Enke, W.
Statistical Ensembles for Economic Networks
NASA Astrophysics Data System (ADS)
Economic networks share with other social networks the fundamental property of sparsity. It is well known that the maximum entropy techniques usually employed to estimate or simulate weighted networks produce unrealistic dense topologies. At the same time, strengths should not be neglected, since they are related to core economic variables like supply and demand. To overcome this limitation, the exponential Bosonic model has been previously extended in order to obtain ensembles where the average degree and strength sequences are simultaneously fixed (conditional geometric model). In this paper a new exponential model, which is the network equivalent of Boltzmann ideal systems, is introduced and then extended to the case of joint degree-strength constraints (conditional Poisson model). Finally, the fitness of these alternative models is tested against a number of networks. While the conditional geometric model generally provides a better goodness-of-fit in terms of log-likelihoods, the conditional Poisson model could nevertheless be preferred whenever it provides a higher similarity with original data. If we are interested instead only in topological properties, the simple Bernoulli model appears to be preferable to the correlated topologies of the two more complex models.
Bargigli, Leonardo
Statistical mechanics in the extended Gaussian ensemble
NASA Astrophysics Data System (ADS)
The extended Gaussian ensemble (EGE) is introduced as a generalization of the canonical ensemble. This ensemble is a further extension of the Gaussian ensemble introduced by Hetherington [J. Low Temp. Phys. 66, 145 (1987)]. The statistical mechanical formalism is derived both from the analysis of the system attached to a finite reservoir and from the maximum statistical entropy principle. The probability of each microstate depends on two parameters ? and ? which allow one to fix, independently, the mean energy of the system and the energy fluctuations, respectively. We establish the Legendre transform structure for the generalized thermodynamic potential and propose a stability criterion. We also compare the EGE probability distribution with the q-exponential distribution. As an example, an application to a system with few independent spins is presented.
Johal, Ramandeep S.; Planes, Antoni; Vives, Eduard
Multicycle ensemble forecasting of sea surface temperature
NASA Astrophysics Data System (ADS)
novel extension to time-lagged ensemble forecasting called multicycle ensemble forecasting improves the independent sampling of forecast model errors. Multicycle is defined such that each forecast cycle is independent of the previous forecast cycle. For an M cycle system the background field for each cycle is from a model hindcast M cycles earlier. The model errors have a factor M longer period to grow compared with a sequential system; however, the increased independence in the forecast model errors provide weighted ensemble averages with greater skill and reliability over the 0 lag forecast and a good spread-error relationship. This cost-efficient technique is relevant to global ocean forecasting where an ensemble method is computationally prohibitive.
Brassington, Gary B.
Statistical mechanics in the extended Gaussian ensemble.
The extended Gaussian ensemble (EGE) is introduced as a generalization of the canonical ensemble. This ensemble is a further extension of the Gaussian ensemble introduced by Hetherington [J. Low Temp. Phys. 66, 145 (1987)]. The statistical mechanical formalism is derived both from the analysis of the system attached to a finite reservoir and from the maximum statistical entropy principle. The probability of each microstate depends on two parameters beta and gamma which allow one to fix, independently, the mean energy of the system and the energy fluctuations, respectively. We establish the Legendre transform structure for the generalized thermodynamic potential and propose a stability criterion. We also compare the EGE probability distribution with the q-exponential distribution. As an example, an application to a system with few independent spins is presented. PMID:14682852
Johal, Ramandeep S; Planes, Antoni; Vives, Eduard
Quantum Dynamics with AN Ensemble of Hamiltonians
NASA Astrophysics Data System (ADS)
We review recent progress in the nonequilibrium dynamics of thermally isolated many-body quantum systems, evolving with an ensemble of Hamiltonians as opposed to deterministic evolution with a single time-dependent Hamiltonian. Such questions arise in (i) quantum dynamics of disordered systems, where different realizations of disorder give rise to an ensemble of real-time quantum evolutions, (ii) quantum evolution with noisy Hamiltonians (temporal disorder), which leads to stochastic Schrödinger equations, and, (iii) in the broader context of quantum optimal control, where one needs to analyze an ensemble of permissible protocols in order to find one that optimizes a given figure of merit. The theme of ensemble quantum evolution appears in several emerging new directions in noneqilibrium quantum dynamics of thermally isolated many-body systems, which include many-body localization, noise-driven systems, and shortcuts to adiabaticity.
Rahmani, Armin
Research and operational applications in multi-center ensemble forecasting
The North American Ensemble Forecast System (NAEFS) was built up in 2004 by the Meteorological Service of Canada (MSC), the National Meteorological Service of Mexico (NMSM), and the US National Weather Service (NWS) as an operational multi-center ensemble forecast system. Currently it combines the 20-member MSC and NWS ensembles to form a joint ensemble of 40 members twice a day.
Y. Zhu; Z. Toth
Numerical Investigation with a Physically Based Regional Interpolator for Off-Line Downscaling of GCMs: FIZR
A novel approach for regional climate modeling based on an off-line downscaling of GCM simulations is described and illustrated with a one-month simulation example. The model is physically based and it requires outputs from a previous GCM integration. The methodology is based upon the premise that much of `small-scale' variability (i.e., for spatial scales below current GCM resolution) is often
Stéphane Goyette; J. P. René Laprise
Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency
A deterministic approach for downscaling ?40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km).
Olivier Merlin; Jeffrey P. Walker; Abdelghani Chehbouni; Yann Kerr
Statistical downscaling and future scenario generation of temperatures for Pakistan Region
NASA Astrophysics Data System (ADS)
Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical downscaling) has been adopted. Statistical DownScaling Model (SDSM) employed for downscaling of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The downscaled data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.
Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas
Exploring two methods for statistical downscaling of Central European phenological time series.
In this study we set out to investigate the possibility of linking phenological phases throughout the vegetation cycle, as a local-scale biological phenomenon, directly with large-scale atmospheric variables via two different empirical downscaling techniques. In recent years a number of methods have been developed to transfer atmospheric information at coarse General Circulation Model's grid resolutions to local scales and individual points. Here multiple linear regression (MLR) and canonical correlation analysis (CCA) have been selected as downscaling methods. Different validation experiments (e.g. temporal cross-validation, split-sample tests) are used to test the performance of both approaches and compare them for time series of 17 phenological phases and air temperatures from Central Europe as microscale variables. A number of atmospheric variables over the North Atlantic and Europe are utilized as macroscale predictors. The period considered is 1951-1998. Temporal cross-validation reveals that the CCA model generally performs better than MLR, which explains 20%-50% of the phenological variances, whereas the CCA model shows a range from 40% to over 60% throughout most of the vegetation cycle. To show the validity of employing phenological observations for downscaling purposes both methods (MLR and CCA) are also applied to gridded local air temperature time series over Central Europe. In this case there is no obvious superiority of the CCA model over the MLR model. Both models show explained variances from 40% to over 70% in the temporal cross-validation experiment. The results of this study indicate that time series of phenological occurrence dates are very compatible with the needs of empirical downscaling originally developed of local-scale atmospheric variables. PMID:12955613
Matulla, C; Scheifinger, H; Menzel, A; Koch, E
Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada
Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data\\u000a representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs)\\u000a have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land\\/sea discontinuities\\u000a or heterogeneous land cover. The performance of the
Y. B. Dibike; P. Gachon; A. St-Hilaire; T. B. M. J. Ouarda; Van T.-V. Nguyen
Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling
This study investigates the potential of predicting local precipitation over northern Taiwan using statistical downscaling of large-scale circulation variables from global climate models (GCMs). Historical hindcast data of 500 hPa geopotential height (Z500) and sea level pressure (SLP) from six different GCMs, with the target season of being that of June, July, and August (JJA), are used as predictors for
Jung-Lien Chu; Hongwen Kang; Chi-Yung Tam; Chung-Kyu Park; Cheng-Ta Chen
Statistical downscaling of near-surface wind over complex terrain in southern France
Summary Accurate and rapid determination of near-surface wind fields in a complex area (orography, inhomogeneous surface properties)\\u000a is a challenge for applications like the evaluation of wind energy production, the prediction of pollution transport and hazardous\\u000a conditions for aeronautics and ship navigation, or the estimation of damage to farm plantations, among others. This paper\\u000a presents a statistical downscaling approach based on
T. Salameh; P. Drobinski; M. Vrac; P. Naveau
Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling
We compare regional climate change scenarios (temperature and precipitation) over eastern Nebraska produced by a semiempirical statistical downscaling (SDS) technique and regional climate model (RegCM2) experiments, both using large scale information from the same coarse resolution general circulation model (GCM) control and 2×CO2 simulations. The SDS method is based on the circulation pattern classification technique in combination with stochastic generation
L. O. Mearns; I. Bogardi; F. Giorgi; I. Matyasovszky; M. Palecki
Constructing Site-Specific Climate Change Scenarios on a Monthly Scale Using Statistical Downscaling
Summary Monthly mean temperature and monthly precipitation totals in two small catchments in the Czech Republic are estimated from\\u000a large-scale 500?hPa height and 1000\\/500 hPa thickness fields using statistical downscaling. The method used is multiple linear\\u000a regression. Whereas precipitation can be determined from large-scale fields with some confidence in only a few months of the\\u000a year, temperature can be determined
R. Huth; J. Kyselý
Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs
Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the ?-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.
Wood, Andrew W.; Leung, Lai R.; Sridhar, V.; Lettenmaier, D. P.
A Procedure for Statistical Downscaling of Precipitation with an Objective Method for Predictor Selection
NASA Astrophysics Data System (ADS)
Downscaling General Circulation Models' (GCM) outputs to a finer grid cell size is an important step in climate change impact and adaptation studies in particular for hydrologic applications. Many investigations have been focused on presenting techniques to downscale GCM data utilizing statistical approaches. Nevertheless there is currently the need to present techniques on predictor selection and also to compare different downscaling models' capabilities. Hence in this study an algorithm has been developed to select GCM predictors in a subseasonal to seasonal time scale. Independent component analysis was used to find the statistically independent signals of CGCM3 variables in the 4*7 grid cells covering the Willamette river basin in Oregon, USA. Using the multi-linear regression cross validation (MLR-CV) the GCM predictors were selected for each period. The selected predictors were then applied to train the ANFIS (Adaptive Network-based Fuzzy Inference System) and the SVM (Support Vector Machine) models, and their performances were assessed on the test data. To design more robust networks that are less dependent on training data set, the cross validation was performed. . Predictors with the best performance for each season in the test set (using both ANFIS and SVM models) were selected for that specific season. The comparison of ANFIS and SVM models using statistical measures showed that ANFIS presents better results suitable for climate impact studies. Also application of ICA allowed reducing the size of many dependent GCM variables in 28 grid cells considerably resulting in higher accuracy in downscaling and more effectiveness in the procedure.
Najafi, M.; Moradkhani, H.; Wherry, S.
Hydroclimatological response to dynamically downscaled climate change simulations for Korean basins
We investigated the hydrological response to climate change simulations for three basins in South Korea. To provide fine-scale\\u000a climate information to the PRMS hydrological model, an ECHO-G B2 simulation was dynamically downscaled using the RegCM3 double-nested\\u000a system implementing two different convection schemes, namely, the Grell and the MIT-Emanuel (EMU) schemes. The daily minimum\\u000a and maximum temperatures and precipitation from the
Eun-Soon Im; Il-Won Jung; Heejun Chang; Deg-Hyo Bae; Won-Tae Kwon
A Summary of ACPI Climate Downscaling Studies for the Western United States
NASA Astrophysics Data System (ADS)
Downscaling is an important component of the end-to-end prediction system developed for the Department of Energy Accelerated Climate Prediction Initiative (ACPI) Pilot Project to assess the impacts of climate change on water resources of the western U.S. Two Regional Climate Models (RCMs), one based on the Penn State/NCAR Mesoscale Model MM5, and the NCEP Regional Spectral Model (RSM), have been used to simulate the regional climate conditions using large-scale conditions based on global analyses (15-20 years) for model evaluation and global climate models (20 years) for climate change impact assessment. The simulations produced by different regional models and driven by different global analyses are being inter-compared and evaluated using observations. Both RCMs realistically simulated the heavy precipitation along the coastal mountains of California and the Pacific Northwest during the cold season but did not simulate nearly enough precipitation in the southwestern U.S. as observed during the summer monsoon. Furthermore the simulations generated by different RCMs and different global analyses are sufficiently different to deserve further investigations. These models are being used to downscale the control and future climate conditions simulated by the global Parallel Climate Model (PCM). The control simulation is taken from 20 years of a PCM run that was initialized in 1995 with constant CO2 concentration. For future climate conditions, downscaling is being performed using the PCM simulated conditions for 2040-2060 with CO2 concentration following the Business-As-Usual Scenario. Three PCM runs, initialized slightly differently, are used to capture climate variability and downscaling is being performed for all three PCM simulations. Surface energy and water budgets observed and simulated by the RCMs are being analyzed for the Columbia River and Sacramento River basins to evaluate model performance and elucidate climate sensitivity to greenhouse warming in the western U.S.
Leung, L.; Bian, X.; Qian, Y.; Roads, J.; Han, J.; Mason, S.
SVM binary classifier ensembles for image classification
We study how the SVM-based binary classifiers can be effectively combined to tackle the multi-class image classification problem. We study several ensemble schemes, including OPC (one per class), PWC (pairwise coupling), and ECOC (error-correction output coding), that aim to achieve good error correction capability through redundancy. To enhance these ensemble schemes' accuracy, we propose methods that on the one hand
King-Shy Goh; Edward Y. Chang; Kwang-Ting Cheng
Wind speed forecasting via ensemble Kalman Filter
Wind speed prediction is crucial for electricity system security and planning. In this paper, ensemble Kalman Filter (EnKF) method is employed to predict 10 minutes averaged wind speed. We use Auto-Regressive and Moving Average (ARMA) model as the state function of EnKF, perturb initial wind data to generate ensembles and forecast wind speed data via EnKF. The comparison with in-situ
Zhang Wei; Wang Weimin
Duality in random matrix ensembles for all ?
Gaussian and Chiral ?-Ensembles, which generalise well-known orthogonal (?=1), unitary (?=2), and symplectic (?=4) ensembles of random Hermitian matrices, are considered. Averages are shown to satisfy duality relations like {?,N,n}?{4\\/?,n,N} for all ?>0, where N and n respectively denote the number of eigenvalues and products of characteristic polynomials. At the edge of the spectrum, matrix integrals of the Airy (Kontsevich)
Patrick Desrosiers
Evaluation of an Ensemble Dispersion Calculation.
NASA Astrophysics Data System (ADS)
A Lagrangian transport and dispersion model was modified to generate multiple simulations from a single meteorological dataset. Each member of the simulation was computed by assuming a ±1-gridpoint shift in the horizontal direction and a ±250-m shift in the vertical direction of the particle position, with respect to the meteorological data. The configuration resulted in 27 ensemble members. Each member was assumed to have an equal probability. The model was tested by creating an ensemble of daily average air concentrations for 3 months at 75 measurement locations over the eastern half of the United States during the Across North America Tracer Experiment (ANATEX). Two generic graphical displays were developed to summarize the ensemble prediction and the resulting concentration probabilities for a specific event: a probability-exceed plot and a concentration-probability plot. Although a cumulative distribution of the ensemble probabilities compared favorably with the measurement data, the resulting distribution was not uniform. This result was attributed to release height sensitivity. The trajectory ensemble approach accounts for about 41%-47% of the variance in the measurement data. This residual uncertainty is caused by other model and data errors that are not included in the ensemble design.
Draxler, Roland R.
Statistical Downscaling of General Circulation Model Output for the Northern Great Plains: A Comparative Analysis of Downscaling Methods for Temperature and Precipitation
NASA Astrophysics Data System (ADS)
General Circulation Models have come to be the foremost dynamical tools to understanding and predicting the complex changes associated with climate change, yet the grid spacing of these models are far too course for use in local and regional impact studies. Recent research has highlighted the possibility to downscale these course resolution data through regional climate models (RCMs) or through statistical means. Given the current changes in climate due to natural and human made forcings and the importance of the Northern Great Plains (NGP) to the global agricultural food supply, it is important to gain insight into the small scale effects these changes will have on this important region. Here is presented an analysis of three statistical downscaling methods for translating the course scale information to a more usable scale planners and the public can use for more effective decision making. Regression and weather typing methods are applied to ten GCM outputs and compared for their effectiveness and ability to accurately generate fine scale daily and monthly temperature and precipitation data for the NGP. The implications are explored by utilizing the most robust method to extrapolate the outcomes of the 2.6, 4.5 and 8.5 RCP experiments for future climate.
Coburn, J.
A high-speed mixed-signal down-scaling circuit for DAB tuners
NASA Astrophysics Data System (ADS)
A high-speed mixed-signal down-scaling circuit with low power consumption and low phase noise for use in digital audio broadcasting tuners has been realized and characterized. Some new circuit techniques are adopted to improve its performance. A dual-modulus prescaler (DMP) with low phase noise is realized with a kind of improved source-coupled logic (SCL) D-flip-flop (DFF) in the synchronous divider and a kind of improved complementary metal oxide semiconductor master-slave (CMOS MS)-DFF in the asynchronous divider. A new more accurate wire-load model is used to realize the pulse-swallow counter (PS counter). Fabricated in a 0.18-?m CMOS process, the total chip size is 0.6 × 0.2 mm2. The DMP in the proposed down-scaling circuit exhibits a low phase noise of -118.2 dBc/Hz at 10 kHz off the carrier frequency. At a supply voltage of 1.8 V, the power consumption of the down-scaling circuit's core part is only 2.7 mW.
Lu, Tang; Zhigong, Wang; Jiahui, Xuan; Yang, Yang; Jian, Xu; Yong, Xu
A coupled stochastic space-time intermittent random cascade model for rainfall downscaling
NASA Astrophysics Data System (ADS)
Analysis of Next Generation Weather Radar rainfall data indicates that for the central United States, rainfall exhibits a composite behavior with respect to its spatial and temporal scaling characteristics. Our data analysis shows that rainfall fluctuations at spatial scales smaller than a reference scale exhibit self-similarity and that at scales larger than the reference scale, rainfall fluctuations are scale dependent. Accordingly, we present a new methodology for downscaling large-scale rainfall consistent with this composite character of rainfall variability. The new downscaling model is a composite of a stochastic space-time submodel that preserves the spatial and temporal dependency characteristics at scales larger than the reference scale and an intermittent random cascade submodel that preserves the statistical self-similarity and spatial intermittency at scales smaller than the reference scale. The new model is applied to downscale summer daily rainfall for the central United States from a scale of 256 km to a scale of 2 km. We show that the new model reproduces quite well the intermittency and self-similarity features and the interscale and across-scale correlation structures of observed rainfall with a relatively low computational burden.
Kang, Boosik; RamíRez, Jorge A.
Variational rainfall fusion and downscaling via L_1 regularization in the wavelet domain
NASA Astrophysics Data System (ADS)
In data fusion and/or downscaling (DFD) of geophysical signals an improved high-resolution estimate of a state variable of interest is sought from a set of coarse-scale and noisy observations. Following recent developments in regularization of inverse problems in transform domains, this work proposes a new variational approach for rainfall data fusion and/or downscaling. From a statistical standpoint, the proposed framework can be interpreted as a maximum a posteriori (MAP) estimator, which explicitly accounts for the non-Gaussian and sparse pdf of the state variable in the transform domain. This MAP estimator yields an improved estimate of rainfall and properly regularizes the intrinsically ill-posed DFD problem in a noisy environment. Efficient solution methods for large scale problems are discussed and the results of synthetic and real case studies for simultaneous fusion and downscaling of the ground-based NEXRAD and TRMM-PR are presented, denoting superior performance of the proposed framework compared to the classical least squares methods.op panel: TRMM-PR snapshot 06/28/1996 at 18:13:00 UTC(TX), middle panel: NEXRAD coincidental snapshot and lower panel: fused product using variational methods with L_1 regularization in the wavelet domain.
Foufoula, E.; Ebtehaj, M.
Downscaling of GCM parameter outputs to RCM spatial scale using an artificial neural network
NASA Astrophysics Data System (ADS)
An artificial neural network (ANN) approach was used to downscale temperature and rainfall fields from the ECHAM-5 global climate model to the spatial scale of the RegCM3 regional climate model over Europe. Inputs to the ANN include the GCM temperature/rainfall field, the GCM and RCM orography fields, and the distance between GCM and RCM gridpoints. The ANN was trained with 20 years of RegCM3 and ECHAM-5 data, then ANN downscaled estimates were assessed against RegCM3 outputs for several different time periods within a 120 year model run. A comparison was also performed of the ANN method against a simple lapse-rate downscaling method for the ECHAM-5 temperature field. It was found that the ANN was able to accurately reproduce RegCM3 parameter fields for a validation time period near to the training time period, but not for time periods far from the training time period. For validation periods near to the training time-period, the ANN approach outperformed the lapse-rate method. Work is ongoing into a 'timeslice' ANN training method, using data from three distinct 10 year slices within a RegCM3 model run for training, and results from this will be presented.
Chadwick, Robin; Coppola, Erika; Giorgi, Filippo
Design of a downscaling method to estimate continuous data from discrete pollen monitoring in Tunisia.
The study of microorganisms and biological particulate matter that transport passively through air is very important for an understanding of the real quality of air. Such monitoring is essential in several specific areas, such as public health, allergy studies, agronomy, indoor and outdoor conservation, and climate-change impact studies. Choosing the suitable monitoring method is an important step in aerobiological studies, so as to obtain reliable airborne data. In this study, we compare olive pollen data from two of the main air traps used in aerobiology, the Hirst and Cour air samplers, at three Tunisian sampling points, for 2009 to 2011. Moreover, a downscaling method to perform daily Cour air sampler data estimates is designed. While Hirst air samplers can offer daily, and even bi-hourly data, Cour air samplers provide data for longer discrete sampling periods, which limits their usefulness for daily monitoring. Higher quantities of olive pollen capture were generally detected for the Hirst air sampler, and a downscaling method that is developed in this study is used to model these differences. The effectiveness of this downscaling method is demonstrated, which allows the potential use of Cour air sampler data series. These results improve the information that new Cour data and, importantly, historical Cour databases can provide for the understanding of phenological dates, airborne pollination curves, and allergenicity levels of air. PMID:24824947
Orlandi, Fabio; Oteros, Jose; Aguilera, Fátima; Ben Dhiab, Ali; Msallem, Monji; Fornaciari, Marco
Statistical downscaling of extreme rainfall events in Romania using artificial neural networks
NASA Astrophysics Data System (ADS)
The main purpose of statistical downscaling methods is to model the relationship between large-scale atmospheric circulation and climatic variables on a regional and subregional scale. Downscaling is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the effects of climate change on smaller areas. In this study we present the first results of a statistical downscaling model, using a neural network-based approach by means of multi-layer perceptron networks. As predictands, various indices associated to temperature and precipitation extremes in Romania are used over the entire country (for temperature extremes) and on selected homogenous areas (for precipitation extremes). Several large-scale predictors (sea-level pressure, temperature at 850 / 700 hPa, specific humidity at 850 / 700 hPa) are tested, in order to select the optimum statistical model for each predictand. Predictands are considered separately or in various combinations. This work has been realised within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian Executive Agency for Higher Education Research, Development and Innovation Funding (UEFISCDI).
Birsan, Marius-Victor; Busuioc, Aristita; Dumitrescu, Alexandru
Assessment of Future Storm Losses in Germany: Probabilistic Extension of the Statistical-Dynamical Downscaling Approach
NASA Astrophysics Data System (ADS)
Future loss estimation is an important planning tool for insurance companies. In particular, good estimates of ranges of uncertainty are necessary for the assessment of climate change impacts and its implications. In this study, the probabilistic aspect of loss estimation is considered by prediction of loss distributions instead of best estimates for average values. For this purpose, downscaling of global climate model data is combined with regional modelling and a probabilistic loss function, which describes the relation between wind speeds and losses. The statistical-dynamical downscaling (SDD) approach is applied to reanalysis data and ECHAM5 climate scenarios for 1960-2100. The SDD consists of a cluster classification of storm relevant weather episodes, referred to as weather types (WT), dynamical downscaling for WT episodes and a recombination of wind speed distributions on the regional scale using frequencies of WT occurrences. Changes in wind distributions for different time periods are divided into external changes due to variability of WT frequencies and internal changes due to wind speed distributions within WT classes. The losses are estimated using generalized loss functions, which fit wind speeds locally to observed loss frequencies via quantile regression. The results corroborate earlier findings, which describe an enhancement of loss potentials for Germany associated with winter storms under future climate conditions. In addition, uncertainty ranges in terms of quantile functions allow for a discussion of loss potential changes with respect to the relative sizes of events.
Born, K.; Karremann, M. K.; Ludwig, P.; Pinto, J.
Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland
NASA Astrophysics Data System (ADS)
Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely "Shadegan" in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987-2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.
Etemadi, H.; Samadi, S.; Sharifikia, M.
Potential for downscaling soil moisture maps derived from spaceborne imaging radar data
NASA Astrophysics Data System (ADS)
The presence of nonlinear relationships between surface soil moisture and various hydrologic processes suggests that grid-scale water and energy fluxes cannot be accurately modeled without subgrid-scale soil moisture information. For land surface and energy balance models run over continental- to global-scale domains, accurate fine-scale soil moisture observations are nearly impossible to obtain on a consistent basis and will likely remain so through the next generation of soil moisture remote sensors. In the absence of such data sets, an alternative approach is to generalize the statistical behavior of soil moisture fields across the relevant range of spatial scales. Downscaling procedures offer the possibility that the fine-scale statistical properties of soil moisture fields can be inferred from coarse-scale data. Such an approach was used for a 29×200 km transect of 25 m active radar data acquired over Oklahoma by NASA's spaceborne imaging radar imaging (SIR-C) mission on April 12, 1994. Using a soil dielectric inversion model, the radar data were processed to provide estimates of surface soil dielectric values, which can be equated to volumetric soil moisture content. The soil moisture field along each strip was analyzed for evidence of spatial scaling for scales ranging from 100 to 6400 m. Results suggest that a spatial scaling assumption may not always be an appropriate basis for a downscaling approach. Prospects for the development of a more robust downscaling procedure for soil moisture are discussed.
Crow, Wade T.; Wood, Eric F.; Dubayah, Ralph
Applying downscaled global climate model data to a hydrodynamic surface-water and groundwater model
Precipitation data from Global Climate Models have been downscaled to smaller regions. Adapting this downscaled precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The downscaled rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.
Swain, Eric; Stefanova, Lydia; Smith, Thomas
On variational downscaling, fusion, and assimilation of hydrometeorological states: A unified framework via regularization
NASA Astrophysics Data System (ADS)
Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment has been a subject of growing research in the past decades. Here we introduce a unified variational framework that ties together the problems of downscaling, data fusion, and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing a proper regularization, expressed as a constraint consistent with the degree of smoothness and/or probabilistic structure of the underlying state. We review relevant smoothing norm regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on land surface hydrometeorological applications. Our results demonstrate that proper regularization of downscaling, data fusion, and data assimilation problems can lead to more accurate and stable recovery of the underlying non-Gaussian state of interest with improved performance in capturing isolated and jump singularities. In particular, we show that the Huber regularization in the derivative space offers advantages, compared to the classic solution and the Tikhonov regularization, for spatial downscaling and fusion of non-Gaussian multisensor precipitation data. Furthermore, we explore the use of Huber regularization in a variational data assimilation experiment while the initial state of interest exhibits jump discontinuities and non-Gaussian probabilistic structure. To this end, we focus on the heat equation motivated by its fundamental application in the study of land surface heat and mass fluxes.
Ebtehaj, A. M.; Foufoula-Georgiou, E.
Ensemble postprocessing for probabilistic quantitative precipitation forecasts
NASA Astrophysics Data System (ADS)
Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop ensemble prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an ensemble setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an ensemble system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. Ensemble systems like COSMO-DE-EPS are often limited with respect to ensemble size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient ensemble spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated ensemble system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical postprocessing can compensate deficiencies in calibration of biased and underdispersive ensemble forecasts and should be considered as an integral part of an ensemble prediction system. The relative gain in predictive skill is evaluated for logistic regression which provides well calibrated forecast for the probability of precipitation and threshold exceedance. Quantile regression is used to obtain skillfull probabilistic forecasts of extreme precipitation events. The selection of predictive covariates is done by penalized regression based on the least absolute shrinkage and selection operator (LASSO).
Bentzien, S.; Friederichs, P.
Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts
NASA Technical Reports Server (NTRS)
This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.
Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.
The Future of Land-Use in the United States: Downscaling SRES Emission Scenarios
NASA Astrophysics Data System (ADS)
Scenarios have emerged as useful tools to explore uncertain futures in ecological systems. We describe research being initiated by the U.S. Geological Survey (USGS) to develop a comprehensive portfolio of land-use and land-cover (LULC) scenarios for the United States. The USGS has identified LULC scenarios as a major area of future research and the USGS LandCarbon research project has adopted a scenario-based approach for forecasting changes in LULC that may result in changes to ecosystem carbon flux and greenhouse gas (GHG) emissions. Scenarios generally require knowledge of history and current conditions, and specific understanding about how drivers of change have acted to influence the historical and current condition. We describe a methodology to downscale Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) by leveraging three primary sources of information: 1) comprehensive land-use histories developed through remote sensing and survey data, 2) analytical model outputs from global integrated assessment models (IAMs), and 3) expert knowledge. SRES scenarios are organized along two primary dimensions described by economic versus environmental preferences and global versus regional influences. The SRES process identified the primary drivers of GHG emissions and provided narrative storylines describing their unique characteristics within each scenario family. We describe an iterative process to downscale SRES storylines to the national and regional scale for the United States. Based on downscaled narratives and guided by expert opinion, IAM outputs are used to extend land-use histories into the future. A wide range of qualitative and quantitative products will be developed throughout the downscaling process. Qualitative narratives will be developed at national and regional scales and case studies to downscale to local scales will be explored. Quantitative data includes scenarios of LULC (rates of change, composition, and conversions) at national and regional scales. Quantitative LULC scenarios are useful inputs for LULC models, which can be used to spatially allocate LULC demand on the landscape, resulting in high-resolution annual maps of LULC consistent with IPCC SRES global scenarios.
Sleeter, B. M.; Sohl, T. L.
Cluster analysis of explicitly and downscaled simulated North Atlantic tropical cyclone tracks
NASA Astrophysics Data System (ADS)
The response of tropical cyclone (TC) activity to climate change is a question of major interest. In order to address this crucial issue, several types of models have been developed in the past, such as Global Climate Models (GCMs). However, the horizontal resolution of those models usually leads to some difficulties in resolving the inner core of TCs and then to properly simulate TC activity. In order to avoid this problem, an alternative tool has been developed by Emanuel (2005). This downscaling technique uses tracks that are initiated by randomly seeding large areas of the tropics with weak vortices. Then the survival of the tracks is based on large-scale environmental conditions produced by GCMs in our case. Here we compare the statistics of TC tracks simulated explicitly in four GCMs to the results of the downscaling technique driven by the four same GCMs in the present and future climates over the North Atlantic basin. Simulated tracks are objectively separated into four groups using a cluster technique (Kossin et al. 2010). The four clusters form zonal and meridional separations of tracks as shown in Figure 1. The meridional separation largely captures the separation between hybrid or baroclinic storms (clusters 1 and 2) and deep tropical systems (clusters 3 and 4), while the zonal separation segregates Gulf of Mexico and Cape Verde storms. Except for the seasonality, the downscaled simulations better capture the general characteristics of the clusters (mean duration of the tracks, intensity...) compared with the explicit simulations, which present strong biases. In the second part of this study, we use three different scenarios to examine the possible future changes of the clusters from the downscaled simulations. We explored the role of a warming of the SST, an increase in carbon dioxide and a combination of both ones. The results show that the response to each scenario is highly varying depending on the simulation examined. References - Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. Journal of Climate, 23, 3057-3076, DOI: 10.1175/2010JCLI3497.1. - Emanuel, K., 2005: Climate and Tropical Cyclone activity: A new downscaling approach. Journal of Climate, 19, 4797-4802.
Daloz, A.; Camargo, S. J.; Kossin, J. P.; Emanuel, K.
Long-lead multi-model ensemble prediction of a drought index sensitive to global warming
NASA Astrophysics Data System (ADS)
Given the changing climate, advance information on hydrological extremes such as droughts will help in planning for disaster mitigation and facilitate better decision making for water availability management. A deficit of precipitation for long-term time scales beyond 6 months has impacts on the hydrological sectors such as ground water, streamflow, and reservoir storage, whereas there are difficulties in predicting such long-lead precipitation anomalies especially over in-land extratropical areas, even using state-of-the-art multiple coupled model ensembles. The potential of prediction of long-lead hydrological variations based on climatic water balance with multi-coupled model statistics has been investigated. Multi-scalar hydrological index based on both of precipitation and temperature, i.e. newly proposed standardized precipitation evapotranspiration index (SPEI), is used not only to appropriately define the hydrological extremes but also to consider hydrological balance between precipitation and evapotranspiration. Further, since it includes the role of temperature, it becomes sensitive to any linear trend, such as the global warming, and can properly respond its consequent extremes, unlike standardized precipitation index (SPI). To predict long-lead district level multi-model ensemble (MME)-based hydrological extremes, six-month downscaled MME (DMME) prediction system is developed for 60 stations in South Korea. DMME, in conjunction with variance inflation, can give predictions of hydrological extremes with reasonable skills in terms of SPI and SPEI. The results could potentially improve hydrological extreme predictions using meteorological forecasts for policymaker and stakeholders in water management sector for better climate adaption.
Sohn, S.; Ahn, J.; Tam, C. F.
Weather extremes in very large, high-resolution ensembles: the weatherathome experiment
NASA Astrophysics Data System (ADS)
Resolution and ensemble size are often seen as alternatives in climate modelling. Models with sufficient resolution to simulate many classes of extreme weather cannot normally be run often enough to assess the statistics of rare events, still less how these statistics may be changing. As a result, assessments of the impact of external forcing on regional climate extremes must be based either on statistical downscaling from relatively coarse-resolution models, or statistical extrapolation from 10-year to 100-year events. Under the weatherathome experiment, part of the climateprediction.net initiative, we have compiled the Met Office Regional Climate Model HadRM3P to run on personal computer volunteered by the general public at 25 and 50km resolution, embedded within the HadAM3P global atmosphere model. With a global network of about 50,000 volunteers, this allows us to run time-slice ensembles of essentially unlimited size, exploring the statistics of extreme weather under a range of scenarios for surface forcing and atmospheric composition, allowing for uncertainty in both boundary conditions and model parameters. Current experiments, developed with the support of Microsoft Research, focus on three regions, the Western USA, Europe and Southern Africa. We initially simulate the period 1959-2010 to establish which variables are realistically simulated by the model and on what scales. Our next experiments are focussing on the Event Attribution problem, exploring how the probability of various types of extreme weather would have been different over the recent past in a world unaffected by human influence, following the design of Pall et al (2011), but extended to a longer period and higher spatial resolution. We will present the first results of the unique, global, participatory experiment and discuss the implications for the attribution of recent weather events to anthropogenic influence on climate.
Allen, M. R.; Rosier, S.; Massey, N.; Rye, C.; Bowery, A.; Miller, J.; Otto, F.; Jones, R.; Wilson, S.; Mote, P.; Stone, D. A.; Yamazaki, Y. H.; Carrington, D.
[Evaluating the performance of the UCLA method for spatially downscaling soil moisture products using three Ts/VI indices].
Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially downscale the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially downscaling soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial downscaling. In this paper, we compared the performance of the UCLA method for spatially downscaling the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The downscaled results using the three Ts/VI indices were all reasonable. However, the downscaled results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for downscaling soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research. PMID:24830256
Ling, Zi-Wei; He, Long-Bin; Zeng, Hui
Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions
Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in time-slice mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO{sub 2} GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.
Busuioc, A. [National Inst. of Meteorology and Hydrology, Bucharest (Romania); Storch, H. von; Schnur, R. [GKSS Research Center, Geesthacht (Germany). Inst. of Hydrophysics
Statistical downscaling of GCM-simulated precipitation using Model Output Statistics
NASA Astrophysics Data System (ADS)
Increased concentrations of greenhouse gases are associated not only with rising global temperatures, but are also expected to lead to considerable changes in global precipitation patterns. Estimates for precipitation changes on a local scale are difficult to obtain because General Circulation Models (GCMs) are unable to resolve the small-scale processes that are important to precipitation formation and distribution. Consequently, statistical downscaling is often used to establish relationships between atmospheric processes occurring at different spatial scales. Such statistical links are typically derived from real-world observations and then applied to the output of future GCM simulations to estimate local precipitation changes. This so-called 'perfect-prog' method requires GCM-simulated predictors to be fed directly into the statistical model, therefore assuming the GCM to be a 'perfect' representation of large-scale reality. An alternative approach is to derive statistical models that correct the simulated precipitation. It is known as 'Model Output Statistics' (MOS) and is used routinely in numerical weather prediction. In applying MOS to GCMs, the fundamental requirement is a simulation of some historical period in which the large-scale circulation captures observed temporal variability. As GCM simulations do not typically assimilate observations, the MOS approach has not yet been used for estimating long-term precipitation changes directly from GCM simulations. We have conducted a simulation for the period 1958-2001 using the ECHAM5 GCM in which key circulation and temperature variables are nudged towards equivalent fields from the ECMWF Reanalysis (ERA-40). Such a forcing allows for simulated precipitation, which crucially is not nudged and is calculated independently from prognostic fields, to represent the day-to-day variability in observations. Correlation maps showing the relationship between simulated and observed (GPCC gridded dataset) monthly precipitation reveal an estimate for the spatial skill of ECHAM5 precipitation given a realistic circulation. Here we use a number of MOS downscaling methods to reconstruct regional monthly precipitation (1958-2001) directly using the precipitation field from the nudged ECHAM5 simulation as a predictor. The first MOS method, a simple local scaling of simulated precipitation, shows ECHAM5 precipitation to have excellent potential as a downscaling predictor variable. Correlations with GPCC observations are as high as 0.9 in parts of the Northern Hemisphere and Australasia. A second, non-local, downscaling approach uses Maximum Covariance Analysis (MCA) to estimate point-scale precipitation from simulated precipitation within a surrounding spatial grid. Reconstruction of European winter (summer) precipitation shows a mean correlation with observations of 0.72 (0.51). All methods using ECHAM5 precipitation as a predictor variable show greater performance than conventional perfect-prog approaches. Reconstruction of European winter precipitation using MCA downscaling models with geopotential height and specific humidity at 1000hPa as predictors show a mean correlation with observations of 0.54 and 0.44 respectively. Summer precipitation is generally more difficult to reconstruct with the strongest predictors being geopotential height at 850hPa and relative humidity at 1000hPa (mean correlations of 0.34 and 0.32 respectively). It is anticipated that further downscaling methods, including Canonical Correlation Analysis (CCA), using ECHAM5 precipitation as a predictor will be implemented and compared with perfect-prog methods. There is also scope for downscaling precipitation on a daily time scale with particular focus on extreme values. The most successful methods will then be applied to a future ECHAM5 simulation used in the IPCC Fourth Assessment Report (AR4) with the overall goal of identifying key areas where estimates of precipitation changes are considered most reliable.
Eden, Jonathan; Widmann, Martin
A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes
The ensemble transform Kalman filter (ETKF) ensemble forecast scheme is introduced and compared with both a simple and a masked breeding scheme. Instead of directly multiplying each forecast perturbation with a constant or regional rescaling factor as in the simple form of breeding and the masked breeding schemes, the ETKF transforms forecast perturbations into analysis perturbations by multiplying by a
Xuguang Wang; Craig H. Bishop
Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP
The initial perturbations used for the operational global ensemble prediction system of the National Centers for Envi- ronmental Prediction are generated through the breeding method with a regional rescaling mechanism. Limitations of the system include the use of a climatologically fixed estimate of the analysis error variance and the lack of an orthog- onalization in the breeding procedure. The Ensemble
A method to treat climate changes of year-to-year variations in the pseudo-global-warming method as a dynamical downscaling
NASA Astrophysics Data System (ADS)
The pseudo-global-warming (PGW) method is a time-slice dynamical downscaling one developed by Kimura and Kitoh (2007) to obtain regional climate change information with finer resolutions. In the present climate experiment, regional climate model (RCM) experiment is carried out with objective analysis data (ANAL) as the lateral boundary conditions. On the other hand, in the future climate experiment, the lateral boundary conditions are the sum of the ANAL and the difference between the present and future climates by an atmosphere-ocean general circulation model (AOGCM). The advantage of this method is that the influences of biases of the AOGCMs are reduced. In addition, the number of downscaling experiments could be reduced in multi-model problems, namely, a RCM experiment result with the boundary conditions created by a multi-model ensemble mean of AOGCMs seems to be similar to the average of the results of RCMs with their AOGCMs. However, PGW method involves some problems. One of them is that climate changes in year-to-year variations are ignored. To overcome this problems, a new method is introduced. In the new method, a mean climatological difference of a AOGCM is added to ANAL in future climate experiment, which is the same as PGW method. Next, the year-to-year variation term of ANAL, AOGCM in the present climate (GCM-P), and that in the future climate (GCM-F) are normalized in each level and element of ANAL to X'a, X'p, and X'f, respectively. The eigenvector of X'a (Va) is extracted by Principal Component Analysis (PCA). Only Va is trusted among the variations of ANAL, GCM-P, and GCP-F. Thus, the coefficients (Ta, Tp, and Tf) of the Multiple Regression Analysis (MRA) with Va are examined, and a coefficient (Tw) for Va are newly estimated as the variation term of boundary conditions of RCM in the future climate (X'w). To create Tw, three-step calculations are included in the estimation. First, a matrix operator, that the covariance of coefficients matrix of GCM-P is changed into that of GCM-P, is calculated using the lower triangle matrix made by the Cholesky decomposition. The operation is carried out for Ta, and Tw1 and its covariance Cw1 are estimated. Second, the reliability of Tw1 and Cw1 are investigated. The Cw1 is modified using a weight matrix W into Cw2. The details are omitted, but the unreliable variation mode is mostly replaced by that of the ANAL. Tw2 are calculated from Cw2. Third, The total amplitude of the variations is adjusted. The total amplitude of Cw2 (Pw), that is a trace of orthogonalized Cw2, is somewhat small, because all variations of Tw could not be expressed by Va. To amplify Pw, Cw is defined as Qw/Pw*Cw2. Here, Qw=Qf/Qp*Qa. Qa (Qp, Qf) is a trace of covariance of coefficient, which is estimated by a PCA and a MRA with ANAL (GCM-P, GCM-F). The final coefficient Tw is calculated by Cw. Consequently, a suitable year-to-year variation pattern is created for the boundary conditions of PGW experiment in the future climate. Here, differences of the boundary conditions between the present and future climate are constant in the normal PGW method. However, these vary with year in the new method. In the multi-model downscaling problem, the statistical operations such as the average are carried out for Cw.
Wakazuki, Y.; Hara, M.; Kimura, F.; Regional Climate Modeling Research Team
Ensemble habitat mapping of invasive plant species.
Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. PMID:20136746
Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate
Coupling spin ensembles via superconducting flux qubits
NASA Astrophysics Data System (ADS)
We study a hybrid quantum system consisting of spin ensembles and superconducting flux qubits, where each spin ensemble is realized using the nitrogen-vacancy centers in a diamond crystal and the nearest-neighbor spin ensembles are effectively coupled via a flux qubit. We show that the coupling strengths between flux qubits and spin ensembles can reach the strong and even ultrastrong coupling regimes by either engineering the hybrid structure in advance or tuning the excitation frequencies of spin ensembles via external magnetic fields. When extending the hybrid structure to an array with equal coupling strengths, we find that in the strong-coupling regime, the hybrid array is reduced to a tight-binding model of a one-dimensional bosonic lattice. In the ultrastrong-coupling regime, it exhibits quasiparticle excitations separated from the ground state by an energy gap. Moreover, these quasiparticle excitations and the ground state are stable under a certain condition that is tunable via the external magnetic field. This may provide an experimentally accessible method to probe the instability of the system.
Qiu, Yueyin; Xiong, Wei; Tian, Lin; You, J. Q.
Rotation forest: A new classifier ensemble method.
We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest ensemble on a random selection of 33 benchmark data sets from the UCI repository and compared it with Bagging, AdaBoost, and Random Forest. The results were favorable to Rotation Forest and prompted an investigation into diversity-accuracy landscape of the ensemble models. Diversity-error diagrams revealed that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest, and more diverse than these in Bagging, sometimes more accurate as well. PMID:16986543
Rodríguez, Juan J; Kuncheva, Ludmila I; Alonso, Carlos J
The Hydrologic Ensemble Prediction Experiment (HEPEX)
NASA Astrophysics Data System (ADS)
The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological ensemble prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.
Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.
Skill of Ensemble Seasonal Probability Forecasts
NASA Astrophysics Data System (ADS)
In operational forecasting, the computational complexity of large simulation models is, ideally, justified by enhanced performance over simpler models. We will consider probability forecasts and contrast the skill of ENSEMBLES-based seasonal probability forecasts of interest to the finance sector (specifically temperature forecasts for Nino 3.4 and the Atlantic Main Development Region (MDR)). The ENSEMBLES model simulations will be contrasted against forecasts from statistical models based on the observations (climatological distributions) and empirical dynamics based on the observations but conditioned on the current state (dynamical climatology). For some start dates, individual ENSEMBLES models yield significant skill even at a lead-time of 14 months. The nature of this skill is discussed, and chances of application are noted. Questions surrounding the interpretation of probability forecasts based on these multi-model ensemble simulations are then considered; the distributions considered are formed by kernel dressing the ensemble and blending with the climatology. The sources of apparent (RMS) skill in distributions based on multi-model simulations is discussed, and it is demonstrated that the inclusion of "zero-skill" models in the long range can improve Root-Mean-Square-Error scores, casting some doubt on the common justification for the claim that all models should be included in forming an operational probability forecast. It is argued that the rational response varies with lead time.
Smith, Leonard A.; Binter, Roman; Du, Hailiang; Niehoerster, Falk
Multiscale Macromolecular Simulation: Role of Evolving Ensembles
Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP ensembles of atomic configurations. Such ensembles are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom ensembles at every Langevin timestep is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in ensembles of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these ensembles, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers.
Singharoy, A.; Joshi, H.; Ortoleva, P.J.
Ensemble habitat mapping of invasive plant species
Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.
Stohlgren, T. J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J. T.; Jarnevich, C. S.; Benson, N.
An Ensemble Prognostic Model for Colorectal Cancer
Colorectal cancer can be grouped into Dukes A, B, C, and D stages based on its developments. Generally speaking, more advanced patients have poorer prognosis. To integrate progression stage prediction systems with recurrence prediction systems, we proposed an ensemble prognostic model for colorectal cancer. In this model, each patient was assigned a most possible stage and a most possible recurrence status. If a patient was predicted to be recurrence patient in advanced stage, he would be classified into high risk group. The ensemble model considered both progression stages and recurrence status. High risk patients and low risk patients predicted by the ensemble model had a significant different disease free survival (log-rank test p-value, 0.0016) and disease specific survival (log-rank test p-value, 0.0041). The ensemble model can better distinguish the high risk and low risk patients than the stage prediction model and the recurrence prediction model alone. This method could be applied to the studies of other diseases and it could significantly improve the prediction performance by ensembling heterogeneous information.
Zhang, Jian; Zhang, Ning; Huang, Guo-Hua; Liu, Lei; Cai, Yu-Dong
Is the past a guide to the future? Evaluating the assumption of climate stationarity in statistical downscaling
NASA Astrophysics Data System (ADS)
Climate change requires a global perspective to understand the past and explore the future. The impacts of climate change, however, are experienced mainly at the local to regional level. Downscaling techniques are commonly used to bridge the gap between the spatial scales at which climate is modeled vs. the scales at which impact assessments require climate projections. One of the most common assumptions made by downscaling is that of stationarity: that current-day relationships between climate variables, relationships that cannot be directly represented by fundamental physical equations but rather must be parameterized or statistically modeled, hold true under very different future conditions. As future observations are not yet available, the validity of this assumption is difficult to test. Here, by treating 25km output from the GFDL-HiRAM-C360 model as "observations" for both past and future periods, we quantify the ability of three different statistical downscaling methods (seasonal delta, monthly quantile mapping, and daily asynchronous quantile regression) to reproduce high-resolution projections of current and future mean and extreme temperature and precipitation over North America from coarser-resolution (~200km) versions of the fields. We evaluate the performance of the statistical downscaling models using impact-relevant threshold and intensity metrics specifically tied to agriculture, ecosystems, human health, infrastructure, energy demand, and water availability to demonstrate how the validity of the stationarity assumption can vary by downscaling approach, geographic location, season, and the quantile(s) of the distribution reflected by a given metric.
Hayhoe, K.; Dixon, K. W.; Stoner, A. K.; Lanzante, J.; Radhakrishnan, A.
Quantum discord of ensemble of quantum states
NASA Astrophysics Data System (ADS)
We highlight an information-theoretic meaning of quantum discord as the gap between the accessible information and the Holevo bound in the framework of ensemble of quantum states. This complementary relationship implies that a large amount of preexisting arguments about the evaluation of quantum discord can be directly applied to the accessible information and vice versa. For an ensemble of two pure qubit states, we show that one can avoid the optimization problem with the help of the Koashi-Winter relation. Further, for the general case (two mixed qubit states), we recover the main results presented by Fuchs and Caves (Phys Rev Lett 73:3047, 1994), but totally from the perspective of quantum discord. Following this line of thought, we also investigate the geometric discord as an indicator of quantumness of ensembles in detail. Finally, we give an example to elucidate the difference between quantum discord and geometric discord with respect to optimal measurement strategies.
Yao, Yao; Huang, Jing-Zheng; Zou, Xu-Bo; Han, Zheng-Fu
Optimized gold nanoshell ensembles for biomedical applications
NASA Astrophysics Data System (ADS)
We theoretically study the properties of the optimal size distribution in the ensemble of hollow gold nanoshells (HGNs) that exhibits the best performance at in vivo biomedical applications. For the first time, to the best of our knowledge, we analyze the dependence of the optimal geometric means of the nanoshells' thicknesses and core radii on the excitation wavelength and the type of human tissue, while assuming lognormal fit to the size distribution in a real HGN ensemble. Regardless of the tissue type, short-wavelength, near-infrared lasers are found to be the most effective in both absorption- and scattering-based applications. We derive approximate analytical expressions enabling one to readily estimate the parameters of optimal distribution for which an HGN ensemble exhibits the maximum efficiency of absorption or scattering inside a human tissue irradiated by a near-infrared laser.
Sikdar, Debabrata; Rukhlenko, Ivan D.; Cheng, Wenlong; Premaratne, Malin
Cavity cooling of an ensemble spin system.
We describe how sideband cooling techniques may be applied to large spin ensembles in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of ensemble size. Our calculations indicate that the coupled angular momentum subspaces of a spin ensemble containing roughly 10(11) electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices. PMID:24580576
Wood, Christopher J; Borneman, Troy W; Cory, David G
Ensemble modeling of the ambient solar wind
NASA Astrophysics Data System (ADS)
Ensemble modeling is a method of prediction based on the use of a representative sample of possible future states. Global models of the solar corona and inner heliosphere are now maturing to the point of becoming predictive tools, thus, it is both meaningful and necessary to quantitatively assess their uncertainty and limitations. In this study, we apply simple ensemble modeling techniques in a first step towards these goals. We focus on one relatively quiescent time period, Carrington rotation 2062, which occurred during the late declining phase of solar cycle 23 and assess the sensitivity of the model results to variations in boundary conditions, models, and free parameter values. We present variance maps, "whisker" plots, and Taylor diagrams to estimate the accuracy of the solutions, which demonstrate that the ensemble mean solution outperforms any of the individual realizations. Our results provide a baseline against which future model improvements can be compared.
Riley, Pete; Linker, Jon A.; Miki?, Zoran
The future climate characteristics of the Carpathian Basin based on a regional climate model mini-ensemble
NASA Astrophysics Data System (ADS)
Four regional climate models (RCMs) were adapted in Hungary for the dynamical downscaling of the global climate projections over the Carpathian Basin: (i) the ALADIN-Climate model developed by Météo France on the basis of the ALADIN short-range modelling system; (ii) the PRECIS model available from the UK Met Office Hadley Centre; (iii) the RegCM model originally developed at the US National Center for Atmospheric Research, is maintained at the International Centre for Theoretical Physics in Trieste; and (iv) the REMO model developed by the Max Planck Institute for Meteorology in Hamburg. The RCMs are different in terms of dynamical model formulation, physical parameterisations; moreover, in the completed simulations they use different spatial resolutions, integration domains and lateral boundary conditions for the scenario experiments. Therefore, the results of the four RCMs can be considered as a small ensemble providing information about various kinds of uncertainties in the future projections over the target area, i.e., Hungary. After the validation of the temperature and precipitation patterns against measurements, mean changes and some extreme characteristics of these patterns (including their statistical significance) have been assessed focusing on the periods of 2021-2050 and 2071-2100 relative to the 1961-1990 model reference period. The ensemble evaluation indicates that the temperature-related changes of the different RCMs are in good agreement over the Carpathian Basin and these tendencies manifest in the general warming conditions. The precipitation changes cannot be identified so clearly: seasonally large differences can be recognised among the projections and between the two periods. An overview is given about the results of the mini-ensemble and special emphasis is put on estimating the uncertainties in the simulations for Hungary.
Krüzselyi, I.; Bartholy, J.; Horányi, A.; Pieczka, I.; Pongrácz, R.; Szabó, P.; Szépszó, G.; Torma, Cs.
Accounting for Skewness in Ensemble Data Assimilation
NASA Astrophysics Data System (ADS)
I will discuss a new framework for understanding how a non-normal probability density function (pdf) may affect a state estimate and how one might usefully exploit the non-normal properties of the pdf when constructing a state estimate. A Bayesian framework is constructed that leads naturally to an expansion of the expected forecast error in a polynomial series consisting of powers of the innovation vector. This polynomial expansion in the innovation reveals a new view of the geometric nature of the state estimation problem. Among other things a direct relationship is shown between the degree to which the state estimate varies with the innovation and the moments of the distribution. A practical data assimilation algorithm will also be presented that explicitly accounts for skewness in the prior distribution. The algorithm operates as a global-solve using a conjugate-gradient technique and Schur/Hadamard (element-wise) localization, and as a general rule is only a factor of four more expensive than the traditional ensemble Kalman filter. The central feature of this technique is the squaring of the innovation and the ensemble perturbations so as to create an extended state-space that accounts for the second, third and fourth moments of the prior distribution. This new technique is illustrated in a simple scalar system as well as in a Boussinesq model of O(10000) variables configured to simulate nonlinearly evolving Kelvin-Helmholtz waves in shear flow. It is shown that ensemble sizes of at least 100 members is needed to adequately resolve the third and fourth moments required for the algorithm. For ensembles of this size it is shown that this new technique is superior to a state-of-the-art Ensemble Kalman Filter in situations with significant skewness, otherwise the new algorithm reduces to the performance of the Ensemble Kalman Filter.
Hodyss, D.
Distinguishability measures between ensembles of quantum states
NASA Astrophysics Data System (ADS)
A quantum ensemble {(px,?x)} is a set of quantum states each occurring randomly with a given probability. Quantum ensembles are necessary to describe situations with incomplete a priori information, such as the output of a stochastic quantum channel (generalized measurement), and play a central role in quantum communication. In this paper, we propose measures of distance and fidelity between two quantum ensembles. We consider two approaches: the first one is based on the ability to mimic one ensemble given the other one as a resource and is closely related to the Monge-Kantorovich optimal transportation problem, while the second one uses the idea of extended-Hilbert-space (EHS) representations which introduce auxiliary pointer (or flag) states. Both types of measures enjoy a number of desirable properties. The Kantorovich measures, albeit monotonic under deterministic quantum operations, are not monotonic under generalized measurements. In contrast, the EHS measures are. This property can be regarded as a generalization of the monotonicity under deterministic maps of the trace distance and the fidelity between states. The EHS measures are equivalent to convex optimization problems and are bounded by the Kantorovich measures which are equivalent to linear programs. We present operational interpretations for both types of measures. We also show that the EHS fidelity between ensembles provides an interpretation of the fidelity between mixed states as the fidelity between all pure-state ensembles whose averages are equal to the mixed states being compared. We finally use the measures to define distance and fidelity for stochastic quantum channels and positive operator-valued measures. These quantities may be useful in the context of tomography of stochastic quantum channels and quantum detectors.
Oreshkov, Ognyan; Calsamiglia, John
Ensemble Eclipse: A Process for Prefab Development Environment for the Ensemble Project
NASA Technical Reports Server (NTRS)
This software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, Ensemble. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the Ensemble Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on Ensemble software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (Ensemble-specific) version of the software includes Ensemble-specific plug-ins as well as settings for the Ensemble project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for Ensemble development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software
Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa
Electrostatic Evaluation of the Propellant Handlers Ensemble
NASA Technical Reports Server (NTRS)
The Self-Contained Atmospheric Protective