<http://purl.org/dc/terms/description>
"Numerical weather prediction models, which are discretized approximations to the\r\nphysical equations of the atmosphere, are a critical part of weather forecasting. They\r\ncan project an observed state of the atmosphere into the future, but forecasts have\r\nmany error sources. To counter this, several forecasts made from slightly different\r\ninitial states or models can be made over the same domain and time period. This\r\napproach, called ensemble forecasting, can forecast uncertainty, and produce a better\r\naverage forecast.\r\nIn this study, ensemble forecasts are generated and analyzed to understand the\r\nnature of initial condition (IC) and model error over the North Pacific. A poorlypredicted\r\nstorm event (a bust) in Feb. 1999 is a useful case study period. To approximate\r\ndifferent aspects of IC uncertainty, three IC-perturbation methods are used:\r\n(1) ranked perturbations that target coherent structures in the analyses; (2) perturbations\r\nthat simulate differences between operational analyses from major forecast\r\ncenters; and (3) random perturbations. An ensemble of different model configurations\r\napproximates model uncertainty.\r\nEnsembles are verified several ways to separate model and IC uncertainty, and\r\nevaluate ensemble performance. It is found that during the period surrounding the\r\nbust, IC error is greater than model error. But for one critical forecast, model error\r\nis unusually high while error from ICs is unusually small.\r\nComparison with rawinsonde observations shows that differences between operational\r\nanalyses cannot account for analysis error. Ensembles generated with this\r\ninformation show that analysis differences contain some spatial information about\r\nanalysis uncertainty, but the magnitude is too small. To account for total forecast\r\nerror, model uncertainty must be included with IC uncertainty.\r\nComparing different ensembles reveals that a scaled ranked-perturbation ensembles\r\nhas the best characteristics, including perturbation magnitude, uncertainty growth\r\nvs. error growth, spread-error correlation, and shape of the variance spectrum. Its\r\nproperties are verified by running an independent case study, and only minor differences\r\nare found.\r\nContributions to the field of weather prediction include a new ranked-perturbation\r\nmethod that results in improved short-range ensemble-mean forecasts, an understanding\r\nof the relationship between analysis error and analysis differences, and the confirmation\r\nthat model uncertainty is necessary to account for short-range forecast error."@en
<https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0052776>