The phenomena studied were the daytime convective boundary layer structure and physical processes. This study used airborne measurement of vertical and horizontal wind gusts, humidity, potential temperature, mean horizontal wind speed, and horizontal linear trends of temperature, humidity, radiation. Fluxes of sensible heat, moisture, and momentum were estimated from fast response wind gust, temperature, and humidity measurements; these fluxes were evaluated from data which had been high pass filtered with a third order algorithm with a break point set at 0.012 Hz (5 km wavelength). Several radiation parameters were also measured (e.g., global short and longwave, upwelling, and downwelling). Altitude of the aircraft was measured by radar and pressure; radar was more accurate but was only valid below about 930 m. Geographical position was measured by an inertial navigation system. All level legs of a flight mission were flown at a constant pressure altitude, thus the altitude of the aircraft over the surface varied.
In general, the data set is of excellent overall quality with very little loss of data. Vertical winds were sampled at an effective rate of 5 samples per second instead of the customary 10 samples per second; this had negligible effect on the fluxes but could compromise estimates of turbulence dissipation. From extensive analysis the FIFE Boundary Layer Group recommends using the detrended data rather than the filtered data.
Aircraft Flux - Filtered: Univ. Col. (FIFE)
(Filtered Atmospheric Turbulence Data from the NCAR King Air).
The Atmospheric Turbulence Data from the NCAR King Air data set contains fluxes of sensible heat, moisture, and momentum estimated from fast response wind gust, temperature, and humidity measurements. These data have had a high pass filter applied with a cutoff wavelength of 5 kilometers.
The NCAR King Air participation in FIFE-1987 and FIFE-1989 was part of a coordinated atmospheric boundary layer component which included other aircraft (Univ. Wyoming King Air and National Research Council of Canada (NRC) Twin Otter), surface measurements, balloon-borne profiles, and SODAR and lidar remote sensing. The chief objective of the boundary layer component was to describe the structure of the atmospheric boundary layer over the FIFE study area, increase knowledge of the physical processes active in the daytime boundary layer, and explore the relationship of surface properties to the time and spatial variation in the structure of the boundary layer.
The phenomena studied were the daytime convective boundary layer structure and physical processes. The study used airborne measurement of vertical and horizontal wind gusts, humidity, potential temperature, mean horizontal wind speed, and horizontal linear trends of temperature, humidity, radiation. Fluxes of sensible heat, moisture, and momentum were estimated from fast response wind gust, temperature, and humidity measurements; these fluxes were evaluated from data which had been high pass filtered with a cutoff wavelength of 5 kilometers (i.e., FILTERED). The following radiation parameters were also measured: Global short and longwave; 2 degree field-of-view, 8-12 micrometer, upwelling (apparent surface temperature at lowest altitude flown) and downwelling longwave; a measure of surface greenness (vegetation). Altitude of the aircraft was measured by radar and pressure; radar was more accurate but was only valid below about 930 m. Geographical position was measured by an inertial navigation system. It is important to mention that all level legs of a flight mission were flown at a constant pressure altitude, thus the altitude of the aircraft over the surface varied.
It should be noted that in the final submission to the FIS, three sets of flux and RMS data were submitted. The first used untreated time histories in the derivation of fluxes using the eddy correlation technique (RAW), the second used linearly detrended data (DETREND), the third used time histories that were high-pass filtered (FILTERED) with a third order algorithm with a break point set at 0.012 Hz (5 km wavelength). It is felt that most scientists working with the flux and correlation coefficient data, would prefer to use the linearly detrended data. Data from the NAE (Canada) Twin Otter and University of Wyoming King Air aircraft were archived with the identical formats.
The NCAR King Air participation in FIFE was during IFC-1 (Early part of growing season, 1987) and IFC-5 (the 1989 return to the Konza to capture a dry down situation and apply lessons learned from the 1987 experience).
In IFC-1 the King Air worked alone. As it was the first aircraft to visit the FIFE study area various mission profiles were explored.
Initially, adequacy of the 15 km run length was investigated by extending runs 15 km to the east of the FIFE study area where the surface was more similar to that over the FIFE study area than to the west. Other missions included various attempts at flux profiles using L-patterns where the aircraft flew along the north and east sides of the FIFE study area. The so-called Golden Day mission, 6 June 1987, was designed to investigate the time variation of the fluxes near the surface and inversion layers by flying repeatedly along the north side of the FIFE study area between 490 m pressure altitude (~30 m above the highest point along the track) and about 50 m below the inversion level (which varied with time). For both IFC's the aircraft was based at Salina, Kansas. In 1987 the ferry to the FIFE study area was used to obtain inbound vertical profiles of wind, temperature, humidity, and aerosol from approximately 3000 m to near the surface. However, one mission explored the regional variation of fluxes by flying about 100 km to the northeast of the FIFE study area.
In general data quality for IFC-1 is very good. Vertical winds were sampled at an effective rate of 5 samples per second instead of the customary 10 samples per second; this had negligible effect on the fluxes but could compromise estimates of turbulence dissipation. A full data quality report is given in the Known Problems with the Data Section.
In IFC-5 the NCAR King Air worked in consort with the NRC Twin Otter. During this IFC emphasis was on time and space variation of atmospheric budgets, spatial variation of fluxes over the FIFE study area, and regional variation. The NCAR King Air concentrated on time variation of convective boundary layer budgets and their regional variation. The budget missions were "time centered" so that the average time-of-day was constant for all pairs of legs flown at the same level. Regional variation was explored by always flying the ferry flight from Salina to the FIFE study area at 500 m pressure altitude. Time variation was explored by coordinated missions with the Twin Otter having the same objective and flight plan; these occurred on 27 July, and 2,4,6,7 August 1989.
These missions are presently under analysis and will be reported in journal articles within the next two years.
In general data quality for IFC-5 was very good. A full data quality report is given in the Known Problems with the Data Section.
Dr. Robert Louis Grossman
Aircraft Investigation of Boundary Layer Structure and Turbulence during FIFE.
Robert L. Grossman
University of Colorado
Mr. Vince Glover (FIFE-1987)
National Center for Atmospheric Research
Mr. Alan Schanot (FIFE-1989)
National Center for Atmospheric Research
The National Center for Atmospheric Research (NCAR) King Air participation in FIFE was endorsed by the Research Aviation Panel convened regularly by NCAR and through a special arrangement with the Atmospheric Sciences Division of the National Science Foundation (Dr. Jay Fein). NCAR King Air missions were under the scientific direction of Dr. Robert L. Grossman who was supported by NASA Grant NAG5-904. Pilots were Gil Summers - Jerry Tejcek (1987) and Jerry Tejcek - Henry Boynton (1989). NCAR Operations Managers were Vince Glover (1987) and Alan Schanot (1989).
The measuring devices on the NCAR King Air for FIFE are basically in situ devices. The aircraft is effectively an Eulerian measurement platform since each measurement along a level run and the level run itself take a short time compared to the interval over which substantial time variation in the atmosphere occurs. However, a complete mission, made up of several level legs, often covers a time period during which diurnal and mesoscale time variations can occur. Analysis of this data must take this into account. FIFE planning was successful in avoiding situations where deep convection could have caused mesoscale time and space variations. Grossman (1992b) concluded that the horizontal gradient estimates for runs less than 60 km have very low confidence; thus most of those data are suspect for theoretical reasons. Vertical flux sampling errors are discussed in Grossman 1992.
The theory of measurements for each of the nearly 100 instruments on the NCAR King Air is well beyond the scope of this presentation. For guidance contact Mr. Alan Schanot (see the Contact Information Section).
National Center for Atmospheric Research
Research Aviation Facility
Aircraft Research Instrumentation
King Air N312D - Project #7-217
P.I's: Hall (NASA) & Grossman (University of CO)
NOTE: Inter-comparison flights using CINDE King Air configuration did not have downward video or downward PRT-5 on board. In addition, radiation variables were not sampled and PMS probes were not turned on.
National Center for Atmospheric Research
Research Aviation Facility
Aircraft Research Instrumentation
King Air N312D - Project #8-220
FIFE - 1989, IFC-5
P.I.: R.L.Grossman, Univ. Colorado
User Supplied Equipment:
National Center for Atmospheric Research Beechcraft Super King Air B200T.
The NCAR King Air is maintained by the Research Aviation Facility (RAF) for airborne measurements in support of atmospheric research.
The variables measured are included in the equipment list in the Sensor/Instrument Description Section.
For guidance on particular instruments contact the Research Aviation Facility, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307.
With one exception (described below), all instruments were calibrated according to NCAR RAF procedures. These usually involve calibration to tertiary NIST standards. For details on NCAR RAF calibration procedures contact either Vince Glover (IFC-1) or Alan Schanot (IFC-5) at NCAR Research Aviation Facility, P.O. Box 3000, Boulder, CO 80307; phone (303)497-1030.
The exception, noted above, is the calibration of the Lyman Alpha humidity device. The data herein used the Friehe-Grossman Method which is described in Friehe et al., 1986. Additional information is contained in Appendix C of Grossman et al., 1992. This method depends upon the accurate calibration and operation of the thermoelectric dewpoint instrument on the aircraft. The method requires that each level leg be independently calibrated. The calibration coefficients are checked for altitude (pressure and/or temperature) dependence. In the case of both IFC-1 and -5 the calibration coefficients were averaged for all legs in the well-mixed layer for a given mission since little altitude dependence was noted for the slope of the calibration curve (which is important to the estimation of the moisture flux). This introduced a 2 to 6% error in the moisture flux which should be added to the errors discussed in Grossman et al. 1992b.
Not available at the revision.
Not available at the revision.
NCAR RAF calibrates their instrumentation twice for a given project. Once before the aircraft leaves for the field experiment and when the aircraft returns. The Lyman-alpha calibration, as described above, was performed for each level leg in a given mission.
During IFC-1, one comparison mission was flown with the NAE (Canada) Twin Otter on 12 July 87. Several comparison missions were flown with the NAE Twin Otter during IFC-5. The results of these comparison missions showed that in general each aircraft was within a few percent of the other even though instrumentation and calibrations were different for the two. Details of the inter-comparison missions can be found in MacPherson et al., 1992.
Airborne in situ measurement of the primary variables which go into the computation of the 34 variables used in the estimation of mean values and higher moment statistics is complex. Each aviation facility (and experimenter, in some cases) has his/her own methodology. To check these methods and instruments an analysis of several wing-to-wing comparisons between the NCAR King Air and NAE Twin Otter made during FIFE appears in MacPherson et al., 1992.
The pattern of the aircraft flight track is an important consideration. These patterns are described in the Discussion Section. The first line of each data block has the start time/start point and end time/end point of the level leg to which the data refer; THE TIME SEQUENCE OF THESE LEVEL LEGS SHOULD BE REVIEWED BEFORE SERIOUS ANALYSIS OF THE DATA BEGIN.
Visual observations for each of the missions were recorded in two ways. First by notes taken by Dr. Grossman and secondly by videotapes recorded by two side-looking and one downward looking video cameras. A copy of the handwritten notes and videos can be obtained from the FIFE Information System. A flight-by-flight discussion of these observations is beyond the scope of the project and is actually part of the interpretation of the data by an individual investigator. Dr. Grossman, with a few days notice, would be able to discuss his observations with an interested investigator.
The FIFE study area with areal extent of 15 km by 15 km, is located south of the Tuttle Reservoir and Kansas River, and about 10 km from Manhattan, Kansas, USA. The northwest corner of the area has UTM coordinates of 4,334,000 Northing and 705,000 Easting in UTM zone 14.
The spatial resolution of the data is related to the sampling interval and the length of the individual level legs or pattern of level legs. The sampling interval determines the shortest length scale resolvable by the instruments and the length of the level leg determines the largest length scale resolvable. The data presented here only refers to the length of the level leg. For reference, the mean parameters presented (such as pressure, temperature, mixing ratio, etc.) were collected at one sample per-second (equivalent to about 80 meters horizontal distance through the atmosphere) and the turbulence parameters (such as vertical wind speed, Lyman alpha moisture, fluxes, etc.) were collected at 50 samples per second and block averaged to an effective sampling interval of 20 samples per second (equivalent to about 4 meters horizontal distance through the atmosphere). All data were digitally over sampled and high pass filtered to reduce aliasing and electrical interference from the many sources of EM radiation on the aircraft.
Most NCAR King Air Missions were over the FIFE study area, therefore refer to length scales of the order 15 by 15 kilometers.
The temporal resolution of the NCAR King Air aircraft data in FIFE was episodic with three to five hours of measurement per mission. There were never more than two missions in a day. Thus the aircraft data is not presented as a continuous data stream sampled at given intervals throughout the day. Flight operations were constrained by daylight hours.
FIFE-87 NCAR KING AIR FLIGHT SUMMARY 1987 Date,Run No. FIFE Flight Type Remarks ------------ ---------------- -------------------------------- 5/26 CMP-2 (F2) Flux profiles over north end of Konza site. Orientation. 5/30 CMP-2 (F1) Flux profiles over north end of Konza site. Good mission. 6/1,#1 CMP-1 (F1a) Flux profiles over north end of Konza site. Concentrate on lower subcloud layer. 6/1,#2 CMP-1 (F1a) Flux profiles over north end of Konza site. Concentrate on lower subcloud layer. 6/3 IMP-E Regional Survey to NE of Konza site. Two levels in subcloud layer. 6/4,#1 CMP-1 (F1a) Flux profiles over north end of Konza site. Concentrate on lower subcloud layer. 6/4,#2 CMP-1 (F1a) Flux profiles over north end of Konza site. Concentrate on lower subcloud layer. Further work on flux statistics with 6 legs each at lowest and highest levels. 6/6,#1 CMP-1 (F1a) Time series of fluxes at two levels: near surface and top of mixed layer. Possible entrainment statistics. 6/6,#2 CMP-1 (F1a) Time series of fluxes at two levels: near surface and top of mixed layer. Possible entrainment statistics. FIFE MISSION SUMMARY FOR NCAR KING AIR 1989 (IFC-5) Date/Time Flight Plan Weather Remarks (Flight No.)* --------- ------------ ------------------ ------------------ 26 JULY PLUS Pattern: Began with sctd Ci Regional run. Gust 1007:23 to 4 levels then brkn Scu and probe calibration 1235:42 CDT (RF01) sctd CuCg. Hazy maneuvers. 27 JULY T Pattern: Sctd variable brkn Regional run. Gust 1235:15 to 4 levels Cu; variable cloud probe calibration 1618:37 CDT base. maneuvers. Lidar (RF02) run. Twin Otter mission in morning. 28 JULY Stack: 3 Sctd Cu. Regional run. 1026:32 to levels plus 1235:42 CDT Grid using Twin Otter (RF03) 02 AUGUST L Pattern: 2 Brkn Cu, sctd CuCg Regional run. 1210:54 to levels. sctd As and Ci. Lidar run. Twin 1513:37 CDT Cloud cover dimin- Otter mission in (RF04) ished with time. morning. 04 AUGUST Stack plus Clear, hazy, no FIFE Golden Day. 1149:13 to L pattern: change with time! Regional Run. Gust 1647:56 CDT 4 levels probe calibration maneuvers. Twin (RF05) Otter grid pattern in morning and late afternoon. 06 AUGUST Stack plus Clear becoming Intercompare with 1247:13 to L pattern: sctd Cu. Winds Twin Otter. Lots 1649:32 CDT 4 levels from NNE-NE. of bugs on Very hazy. windshield. Gust (RF06) probe calibration maneuvers. Twin Otter L pattern in morning and late afternoon. 07 AUGUST T pattern: Sctd Cu, sctd Regional run. Much 1254:20 to 4 levels Ci. Very clear fewer bugs this 1254:20 to 4 levels Ci. Very clear day. Twin Otter 1543:19 CDT visibility. mission in morning (RF07) Winds from N. and late Cold front afternoon. passage. 08 AUGUST L pattern: Clear becoming Regional run. 0921:17 to 2 levels brkn Cu. Light Lidar run. Time 1334:44 CDT winds. variation (RF08) objective. 11 AUGUST Telescoping, Brkn Cu, very Regional run 1124:54 to 3-level stacks hazy. Later sctd intercompare with 1705:21 CDT from Konza to Cu, brkn Ac, sctd Twin Otter nr. Emporia, Ci. outbound from Ks. Salina. Scale dependence (RF09) objective. 12 AUGUST Regional scale Sctd Cu, brkn Ac, Regional run 1051:11 to T pattern plus sctd Ci. Cu became intercompare with 1457:18 CDT Grid (Twin brkn with time Twin Otter. Scale Otter) dependence objective. Lake (RF10) calibration of infrared surface temperature.
* These flight numbers have been deduced by the information system staff using the information provided by A. Schanot that appears in the Known Problems with the Data Section of this document. These flight numbers provide the link between the information given here and the information given for IFC-5 in the Known Problems with the Data Section.
On those missions devoted to time variation, intervals ranged between hours and ten's of minutes, depending upon the flight pattern flown.
The SQL definition for this table is found in file AF_FILTR.TDF located on FIFE CD-ROM Volume 1.
Parameter/Variable Description Range Units Source
OBS_DATE The date the observation was made on, in the format (DD-MMM-YY).
START_TIME The starting time for the [GMT] observation run in GMT, in the format (HHMM). The seconds for this time is stored in START_SECONDS.
START_SECONDS The seconds component of the [GMT] START_TIME (format SS).
DURATION The duration of the flight in the format (MMSS).
AIRCRAFT_ID The ID name for the aircraft which made the observation run.
START_LAT The starting latitude for the observation run.
START_LON The starting longitude for the observation run.
START_NORTHING The starting northing position of [meters] the aircraft in UTM coordinates.
START_EASTING The starting easting position of [meters] the aircraft in UTM coordinates.
END_LAT The ending latitude for the observation run.
END_LON The ending longitude for the observation run.
END_NORTHING The ending northing position of [meters] the aircraft in UTM coordinates.
END_EASTING The ending easting position of [meters] the aircraft in UTM coordinates.
HEADING The heading of the aircraft. [degrees from North]
HEIGHT_ABOVE_MEAN_SEA_LVL The altitude of the aircraft [meters] above mean sea level, as determined by air pressure.
HEIGHT_ABOVE_GRND_LVL The altitude of the aircraft [meters] above the ground, as determined by radar.
AIR_TEMP_MEAN The mean air temperature. [degrees Celsius]
POTNTL_TEMP_MEAN The potential mean air temperature. [degrees Kelvin]
MIX_RATIO_MEAN The mixing ratio taken from a [grams] dew-point hygrometer. [kg^-1]
NS_WIND_VELOC_MEAN The mean north/south wind [meters] component (V), with north being [sec^-1] positive.
EW_WIND_VELOC_MEAN The mean east/west wind component [meters] (U), with east being positive. [sec^-1]
PRESS_MEAN The mean air pressure. [millibars]
SURF_TEMP_MEAN The mean surface temperature. [degrees Celsius]
DOWNWELL_RADTN_MEAN The mean downwelling radiation [Watts] count. [meter^-2]
UPWELL_RADTN_MEAN The mean upwelling radiation count. [Watts] [meter^-2]
VEG_INDEX_MEAN The mean vegetation (greenness) index.
AIR_TEMP_RMS The root mean square of the [degrees temperature recorded in column Celsius] AIR_TEMP_MEAN.
POTNTL_TEMP_RMS The root mean square of the [degrees potential temperature recorded in Kelvin] the column POTNTL_TEMP_MEAN.
MIX_RATIO_RMS The root mean square of the [grams] mixing ratio recorded in the [kg^-1] column MIX_RATIO_MEAN, taken from a dew-point hygrometer.
NS_WIND_VELOC_RMS The root mean square of the [meters] north/south wind component [sec^-1] recorded in column NS_WIND_VELOC_MEAN.
EW_WIND_VELOC_RMS The root mean square of the [meters] east/west wind component recorded [sec^-1] in column EW_WIND_VELOC_MEAN.
PRESS_RMS The root mean square of the [millibars] pressure recorded in column PRESS_MEAN.
SURF_TEMP_RMS The root mean square of the [degrees surface temperature recorded in Celsius] column SURF_TEMP_MEAN.
DOWNWELL_RADTN_RMS The root mean square of the [Watts] downwelling radiation count [meter^-2] recorded in column DOWNWELL_RADTN_MEAN.
UPWELL_RADTN_RMS The root mean square of the [Watts] upwelling radiation recorded in [meter^-2] column UPWELL_RADTN_MEAN.
VEG_INDEX_RMS The root mean square of the vegetation (greenness) index recorded in column VEG_INDEX_MEAN.
AIR_TEMP_LINEAR The linear trend of the [degrees temperature recorded in the column Celsius] AIR_TEMP_MEAN. [meter^-1]
POTNTL_TEMP_LINEAR The linear trend of the potential [degrees temperature recorded in column Kelvin] POTNTL_TEMP_MEAN. [meter^-1]
MIX_RATIO_LINEAR The linear trend of the mixing [grams] ratio recorded in column [kg^-1] MIX_RATIO_MEAN (derived from dew [meter^-1] point).
NS_WIND_VELOC_LINEAR The linear trend of the [meters] north/south wind component [sec^-1] recorded in column [meter^-1] NS_WIND_VELOC_MEAN.
EW_WIND_VELOC_LINEAR The linear trend of the east/west [meters] wind component recorded in column [sec^-1] EW_WIND_VELOC_MEAN. [meter^-1]
PRESS_LINEAR The linear trend of the pressure [millibars] recorded in column PRESS_MEAN. [meter^-1]
SURF_TEMP_LINEAR The linear trend of the surface [degrees temperature recorded in column Celsius] SURF_TEMP_MEAN. [meter^-1]
DOWNWELL_RADTN_LINEAR The linear trend of the [Watts] downwelling radiation count [meter^-2] recorded in DOWNWELL_RADTN_MEAN. [meter^-1]
UPWELL_RADTN_LINEAR The linear trend of the upwelling [Watts] radiation count recorded in column [meter^-2] UPWELL_RADTN_MEAN. [meter^-1]
VEG_INDEX_LINEAR The linear trend of the vegetation (greenness) index recorded in column VEG_INDEX_MEAN.
MOIST_AIR_DENSITY_X_CP The moist air density times [Watts][sec] specific heat capacity (CP). [degrees Kelvin^-1
LATENT_HEAT_OF_VAPOR The latent heat of vaporization [Watts] at 20 degrees Celsius. [sec] [gram^-1]
MOIST_AIR_DENSITY The moist air density. [kg] [meter ^-3]
VERT_GUST_VELOC_RMS_FLTR The root mean square of the [meters] vertical wind gust velocity. [sec^-1]
NS_GUST_VELOC_RMS_FLTR The root mean square of the [meters] north/south wind (U) gust velocity. [sec^-1]
EW_GUST_VELOC_RMS_FLTR The root mean square of the [meters] east/west wind (V) gust velocity. [sec^-1]
ALONG_WIND_VELOC_RMS_FLTR The root mean square of the [meters] along-wind component of the wind [sec^-1] gust velocity.
ACROSS_WIND_VELOC_RMS_FLTR The root mean square of the [meters] across-wind component of the wind [sec^-1] gust velocity.
POTNTL_TEMP_RMS_FLTR The root mean square of the [degrees potential temperature. Kelvin]
WATER_MIX_RATIO_RMS_FLTR The root mean square of the water [grams] mixing ratio (lyman alpha). [kg^-1]
CO2_MIX_RATIO_RMS_FLTR The root mean square of the [milligrams] carbon dioxide content. [kg^-1]
VERT_GUST_VELOC_SKEW_FLTR The skewness of the vertical gust wind velocity.
NS_GUST_VELOC_SKEW_FLTR The skewness of the north/south wind gust velocity.
EW_GUST_VELOC_SKEW_FLTR The skewness of the east/west wind gust velocity.
ALONG_WIND_VELOC_SKEW_FLTR The skewness of the along-wind component of the wind gust velocity.
ACROSS_WIND_VELOC_SKEW_FLTR The skewness of the across-wind component of the wind gust velocity.
POTNTL_TEMP_SKEW_FLTR The skewness of the potential temperature.
WATER_MIX_RATIO_SKEW_FLTR The skewness of the water mixing ratio.
CO2_MIX_RATIO_SKEW_FLTR The skewness of the carbon dioxide content.
NS_MOMNTM_FLUX_FLTR The north/south momentum flux, [Newtons] calculated from W*V (wind [meter^-2] components).
EW_MOMNTM_FLUX_FLTR The east/west momentum flux, [Newtons] calculated from W*U (wind [meter^-2] components).
ALONG_MOMNTM_FLUX_FLTR The along-wind momentum flux, [Newtons] calculated from W*along-wind gust [meter^-2] (wind components).
ACROSS_MOMNTM_FLUX_FLTR The across-wind momentum flux, [Newtons] calculated from W*across-wind gust [meter^-2]
SENSIBLE_HEAT_FLUX_FLTR The sensible heat flux. [Watts] [meter^-2]
LATENT_HEAT_FLUX_FLTR The latent heat flux. [Watts] [meter^-2]
CO2_FLUX_FLTR The carbon dioxide flux. [kg] [hectare^-1] [hour^-1]
NS_MOMNTM_CC_FLTR The correlation coefficient for vertical wind velocity and north/south wind gusts.
EW_MOMNTM_CC_FLTR The correlation coefficient for vertical wind velocity and east/west wind gusts.
ALONG_MOMNTM_CC_FLTR The correlation coefficient for vertical wind velocity and along-wind component of the wind gust velocity.
ACROSS_MOMNTM_CC_FLTR The correlation coefficient for vertical wind velocity and across-wind component of the wind gust velocity.
SENSIBLE_HEAT_CC_FLTR The correlation coefficient for the sensible heat flux.
LATENT_HEAT_CC_FLTR The correlation coefficient for the latent heat flux.
CO2_FLUX_CC_FLTR The correlation coefficient for the CO2 flux.
MIX_RATIO_CC_FLTR The correlation coefficient for the mixing ratio * potential temperature.
COMMENTS Any comments pertaining to this record.
FIFE_DATA_CRTFCN_CODE The FIFE Certification Code for * the data, in the following format: CPI (Certified by PI), CPI-??? (CPI - questionable data).
LAST_REVISION_DATE data, in the format (DD-mmm-YY).
* Valid levels
The primary certification codes are: EXM Example or Test data (not for release) PRE Preliminary (unchecked, use at your own risk) CPI Checked by Principal Investigator (reviewed for quality) CGR Checked by a group and reconciled (data comparisons and cross checks)
The certification code modifiers are: PRE-NFP Preliminary - Not for publication, at the request of investigator. CPI-MRG PAMS data that is "merged" from two separate receiving stations to eliminate transmission errors. CPI-??? Investigator thinks data item may be questionable.
OBS_DATE START_TIME START_SECONDS DURATION AIRCRAFT_ID START_LAT --------- ---------- ------------- ---------- ----------- ---------- 12-AUG-89 1642 12 526 NCAR 3904.9 12-AUG-89 1650 37 654 NCAR 3904.9 12-AUG-89 1659 44 645 NCAR 3904.9 12-AUG-89 1710 3 614 NCAR 3904.9 START_LON START_NORTHING START_EASTING END_LAT END_LON END_NORTHING ---------- -------------- ------------- ---------- ---------- ------------ -9633.7 3905.3 -9615.3 -9614 3904.9 -9637.1 -9638.3 3905.1 -9615.5 -9616.1 3904.9 -9636.9 END_EASTING HEADING HEIGHT_ABOVE_MEAN_SEA_LVL HEIGHT_ABOVE_GRND_LVL ----------- ---------- ------------------------- --------------------- 91 1158 100 266 863 560 92 520 192 265 420 89 AIR_TEMP_MEAN POTNTL_TEMP_MEAN MIX_RATIO_MEAN NS_WIND_VELOC_MEAN ------------- ---------------- -------------- ------------------ 16.13 299.9 8.76 5.25 19.22 300.02 8.77 5.82 22.82 300.15 8.9 4.83 24.07 300.38 8.88 5.73 EW_WIND_VELOC_MEAN PRESS_MEAN SURF_TEMP_MEAN DOWNWELL_RADTN_MEAN ------------------ ---------- -------------- ------------------- -.38 881.5 33 800 -.17 913.7 34 769 -.32 952.3 35.1 735 -.15 963.8 36 886 UPWELL_RADTN_MEAN VEG_INDEX_MEAN AIR_TEMP_RMS POTNTL_TEMP_RMS ----------------- -------------- ------------ --------------- 146 99.999 .11 .1 99.999 .15 .15 .27 99.999 .18 .18 .18 99.999 .29 .29 .24 MIX_RATIO_RMS NS_WIND_VELOC_RMS EW_WIND_VELOC_RMS PRESS_RMS ------------- ----------------- ----------------- ---------- .37 .91 .91 .4 .85 .92 .4 2.7 1.07 1.19 .3 3.8 1.12 1.39 .2 3.9 SURF_TEMP_RMS DOWNWELL_RADTN_RMS UPWELL_RADTN_RMS VEG_INDEX_RMS ------------- ------------------ ---------------- ------------- 2.8 121 14 99.999 111 15 99.999 0 136 24 99.999 0 93 16 99.999 0 AIR_TEMP_LINEAR POTNTL_TEMP_LINEAR MIX_RATIO_LINEAR --------------- ------------------ ---------------- -1.680E-07 -.00000011 -.00000894 .000000445 -.00000362 .00000153 -4.410E-07 .00000416 .000000446 .000000913 -.0000067 -.00000148 NS_WIND_VELOC_LINEAR EW_WIND_VELOC_LINEAR PRESS_LINEAR -------------------- -------------------- ------------ -.00000191 -1.570E-07 .00000284 .000000694 .00000105 .0000868 .00000129 -.00000386 -.0000801 -.00000135 -.00000404 .0000717 SURF_TEMP_LINEAR DOWNWELL_RADTN_LINEAR UPWELL_RADTN_LINEAR ---------------- --------------------- ------------------- .000051 .00203 .000575 .00312 .0003 99.999 -.0000986 .000499 99.999 .00501 .000245 99.999 VEG_INDEX_LINEAR MOIST_AIR_DENSITY_X_CP LATENT_HEAT_OF_VAPOR ---------------- ---------------------- -------------------- 99.999 1061.2 2575.3 1.23 2633.2 1.083 1.08 2701 1.115 .99 2719 1.124 MOIST_AIR_DENSITY VERT_GUST_VELOC_RMS_FLTR NS_GUST_VELOC_RMS_FLTR ----------------- ------------------------ ---------------------- 1.056 .94 .75 -.11 .72 .82 -.26 .9 1.03 -.33 1.02 1.18 EW_GUST_VELOC_RMS_FLTR ALONG_WIND_VELOC_RMS_FLTR ---------------------- ------------------------- .79 .75 .72 .82 .9 1.03 1.02 1.17 ACROSS_WIND_VELOC_RMS_FLTR POTNTL_TEMP_RMS_FLTR WATER_MIX_RATIO_RMS_FLTR -------------------------- -------------------- ------------------------ .79 .07 .29 .08 .25 99.999 .12 .16 99.999 .23 .2 99.999 CO2_MIX_RATIO_RMS_FLTR VERT_GUST_VELOC_SKEW_FLTR NS_GUST_VELOC_SKEW_FLTR ---------------------- ------------------------- ----------------------- 99.999 -.117 .107 .633 -.134 -.031 .403 -.039 -.321 .275 -.362 .424 EW_GUST_VELOC_SKEW_FLTR ALONG_WIND_VELOC_SKEW_FLTR ----------------------- -------------------------- .203 .096 -.139 .031 -.005 .34 -.37 -.424 ACROSS_WIND_VELOC_SKEW_FLTR POTNTL_TEMP_SKEW_FLTR --------------------------- --------------------- -.176 .481 .227 -.214 .873 -.086 1.323 .379 WATER_MIX_RATIO_SKEW_FLTR CO2_MIX_RATIO_SKEW_FLTR NS_MOMNTM_FLUX_FLTR ------------------------- ----------------------- ------------------- -.452 99.999 -.12 99.999 1088.3 -.06 99.999 1120.3 .09 99.999 1129.1 -.13 EW_MOMNTM_FLUX_FLTR ALONG_MOMNTM_FLUX_FLTR ACROSS_MOMNTM_FLUX_FLTR ------------------- ---------------------- ----------------------- .04 -.13 -.04 -.11 .06 16 -.25 -.08 86 -.31 .13 151 SENSIBLE_HEAT_FLUX_FLTR LATENT_HEAT_FLUX_FLTR CO2_FLUX_FLTR ----------------------- --------------------- ------------- -9 274 99.999 321 99.999 -.13 208 99.999 -.23 233 99.999 -.29 NS_MOMNTM_CC_FLTR EW_MOMNTM_CC_FLTR ALONG_MOMNTM_CC_FLTR ----------------- ----------------- -------------------- -.17 .06 -.17 -.06 -.13 .06 .08 -.24 -.07 -.1 -.28 .1 ACROSS_MOMNTM_CC_FLTR SENSIBLE_HEAT_CC_FLTR LATENT_HEAT_CC_FLTR --------------------- --------------------- ------------------- -.05 -.12 .39 .18 .45 99.999 .6 .46 99.999 .6 .44 99.999 CO2_FLUX_CC_FLTR MIX_RATIO_CC_FLTR COMMENTS ---------------- ----------------- -------------------- 99.999 -.69 -.29 -.69 .52 -.69 .6 -.69 FIFE_DATA_CRTFCN_CODE LAST_REVISION_DATE --------------------- ------------------ CPI 03-SEP-92 CPI 03-SEP-92 CPI 03-SEP-92 CPI 03-SEP-92
The NCAR King Air aircraft data in FIFE was collected via three to five hours of measurement per mission, with a maximum of two missions in a day. The aircraft data is not presented as a continuous data stream sampled at given intervals throughout the day. The sampling interval for data collection determines the shortest length scale resolvable by the instruments and the length of the level leg determines the largest length scale resolvable. For reference, the mean parameters presented (e.g., pressure, temperature were collected at one sample per-second (i.e., approximately 80 meters horizontal distance) and the turbulence parameters (e.g., vertical wind speed) were collected at 50 samples per second and block averaged to an effective sampling interval of 20 samples per second (equivalent to about 4 meters horizontal distance).
A general description of data granularity as it applies to the IMS appears in the EOSDIS Glossary.
The CD-ROM file format consists of numerical and character fields of varying length separated by commas. The character fields are enclosed with a single apostrophe. There are no spaces between the fields. Each file begins with five header records. Header records contain the following information: Record 1 Name of this file, its table name, number of records in this file, path and name of the document that describes the data in this file, and name of principal investigator for these data. Record 2 Path and filename of the previous data set, and path and filename of the next data set. (Path and filenames for files that contain another set of data taken at the same site on the same day.) Record 3 Path and filename of the previous site, and path and filename of the next site. (Path and filenames for files of the same data set taken on the same day for the previous and next sites (sequentially numbered by SITEGRID_ID)). Record 4 Path and filename of the previous date, and path and filename of the next date. (Path and filenames for files of the same data set taken at the same site for the previous and next date.) Record 5 Column names for the data within the file, delimited by commas. Record 6 Data records begin.
Each field represents one of the attributes listed in the chart in the Data Characteristics Section and described in detail in the TDF file. These fields are in the same order as in the chart.
See NCAR/RAF Bulletin No. 9 (by Miller and Friesen) for details of the formulas used to obtain the derived data from the aircraft measurements; this reference is available from the Research Aviation Facility, NCAR, P.O. Box 3000, Boulder, CO 80307. Grossman 1992 discusses the eddy correlation flux calculation method. Mean, standard deviation, skewness estimates were calculated from standard statistical formulae.
The derivation techniques and algorithms were discussed previously. The main ones were the Lyman-alpha calibration technique and eddy correlation flux technique.
Data Processing by NCAR/RAF: The aircraft data analog signals were initially low pass filtered with a cutoff frequency of 20 Hz, the data were then digitally sampled at 50 samples per second, digital filters were used to low pass again at 20 Hz, The data then were block averaged to a 10 sample per second sampling rate and 1 sample per second sampling rate. Some slow response data obtained at 1 sample per second were interpolated to 20 samples per second: pressure was the main parameter for this project.
Data Processing by R.L. Grossman: Data were despiked by removing all data which were 5 standard deviations from the mean. This represented less than 1% of the data. Moisture mixing ratio was used to avoid corrections due to the dependence of density on temperature. For the filtered data a high-pass recursive filter was used. The filter used here is a third-order high-pass digital filter, derived from the maximally-flat approximation (Budak 1974) and converted to time domain using the pole-zero technique of Jacquot 1981. The cutoff frequency of the filter is fixed at .017 Hz (85 m/s average airspeed divided by a wavelength of 5000 m). Fluxes were computed using standard eddy correlation technique based upon Reynolds Averaging Rules.
Discussed in previous sections.
Only the highlights are given here since it is beyond the scope of the project to discuss potential errors for all of the measurements on the aircraft.
Errors are discussed previously.
This is also discussed in the Notes Section.
NCAR/RAF experts carefully went over the data before it was released to the Principal Investigator. Their reports are given in the Known Problems with the Data Section. The Principal Investigator also looked very carefully at the data for the missions flown on 1 June, 4 June, and 6 June 1987 before publishing results. Level legs for all missions were subjected to criteria based on aircraft altitude changes, position over the FIFE study area, and roll altitude of the aircraft. However, missions other than 1, 4, and 6 June 1987 may contain subtle errors because the principal investigator did not subject these data to the same scrutiny as those used in published results.
Based upon four peer reviewed articles based upon the 1987 data only, the principal investigator feels that the data are of high quality. Limitations have been discussed in the publications as well as in this documentation. The judgment is based also on the analysis of wing-to-wing inter-comparison of measurements as well as computational techniques reported in MacPherson 1992.
A complete list can be found in NCAR/RAF Bulletin No.14, a selection from that list is presented below:
Parameter/Variable Accuracy Resolution ------------------ -------- ----------- Mag. heading 0.05 deg .00275 deg Static press. 1 kPa .06 kPa Geom. Alt. 1.5m, 0-152m 7% , 152-930m 0.1 m Lat/lon < 1 nm per hr .0014 deg Ground veloc. < 1 knot/hr drift .04 m/s Vert. veloc. 0.1 m/s .012 m/s Pitch/roll .05 deg .00275 deg Platform hdg. .05 deg .00275 deg Airspeed .07 kPa .006 kPa Total press. 1.0 kPa .034 kPa Air temp. 0.5 C .006 C Dewpoint temp. 0.5 C, > 0 C .006 C, 1.0 C, < 0 C Note: these have to be converted to mixing ratio using temperature and pressure errors. Vapor density n/a .0006 g/m**3 wind components 1.0 m/s .012 m/s Global Infra-red Radiation not given .40 W/m^2 Global Solar Radiation not given .12 W/m^2
FIS staff applied a general Quality Assessment (QA) procedure to the data to identify inconsistencies and problems for potential users. As a general procedure, the FIS QA consisted of examining the maximum, minimum, average, and standard deviation for each numerical field in the data table. An attempt was made to find an explanation for unexpected high or low values, values outside of the normal physical range for a variable, or standard deviations that appeared inconsistent with the mean. In some cases, histograms were examined to determine whether outliers were consistent with the shape of the data distribution.
The discrepancies, which were identified, are reported as problems in the Known Problems with the Data Section.
The data verification performed by the ORNL DAAC deals with the quality of the data format, media, and readability. The ORNL DAAC does not make an assessment of the quality of the data itself except during the course of performing other QA procedures as described below.
The FIFE data were transferred to the ORNL DAAC via CD-ROM. These CD-ROMs are distributed by the ORNL DAAC unmodified as a set or in individual volumes, as requested. In addition, the DAAC has incorporated each of the 98 FIFE tabular datasets from the CD-ROMs into its online data holdings. Incorporation of these data involved the following steps:
Each distinct type of data (i.e. "data set" on the CD-ROM), is accompanied by a documentation file (i.e., .doc file) and a data format/structure definition file (i.e., .tdf file). The data format files on the CD-ROM are Oracle SQL commands (e.g., "create table") that can be used to set up a relational database table structure. This file provides column/variable names, character/numeric type, length, and format, and labels/comments. These SQL commands were converted to SAS code and were used to create SAS data sets and subsequently to input data files directly from the CD-ROM into a SAS dataset. During this process, file names and directory paths were captured and metadata was extracted to the extent possible electronically. No files were found to be corrupted or unreadable during the conversion process.
Additional Quality Assurance procedures were performed as follows:
As errors are discovered in the online tabular data by investigators, users, or DAAC staff, corrections are made in cooperation with the principal investigators. These corrections are then distributed to users. CD-ROM data are corrected when re-mastering occurs for replenishment of CD-ROM stock.
IFC-1 Data Quality Report from V. Glover
The National Center for Atmospheric Research Aviation Facility has given permission to copy their data quality report for the NCAR King Air participation in FIFE IFC-1. This report was authored by Mr. Vince Glover of NCAR and appears in its entirety. Additional comments by the principal investigators (Grossman, Mahrt, and Hall) are enclosed by parentheses within the text.
"This summary has been written to outline areas of concern in the data set and is not intended to point out every bit of questionable data. It is hoped that this information will facilitate the use of the data as the research concentrates on specific times and flights. In general, the data set is of excellent overall quality with very little loss of data."
"The following report is organized into two sections. The first lists general limitations and systematic biases in the RAF measurements. The second section lists isolated problems on a flight-by-flight basis."
Data Quality Report
King Air N312D-Project # 0-220
This summary has been written to outline basic instrumentation problems affecting the data set and is not intended to point out every bit of questionable data. It is hoped that this information will facilitate use of these data as the research concentrates on specific flights and times.
The following report is organized into two sections. The first section lists reoccurring problems, general limitations, and systematic biases in the RAF measurements. The second section lists isolated problems occurring on a flight-by-flight basis. Some User supplied equipment was included in the instrument package. No attempt has been made to evaluate the performance of any of that equipment.
Special sets of in-flight calibration maneuvers were conducted on flights TF01, RF02 and RF06 to aid in the performance analysis of the wind gust measurements. All of the information, including the summary of INS performance, indicated that the wind measurement system was performing within standard RAF specifications. The time intervals for each set of maneuvers have been documented in both the flight-by-flight data quality review and on the individual Research Flight Forms prepared for each flight.
Lyman-alpha hygrometers are susceptible to in-flight drift in the instrument's bias voltage. Due to this problem, RAF uses a special data processing technique to remove the bias drift by referencing the long term humidity values to one of the more stable thermoelectric dew point sensors. This technique can result in occasional large spikes in the derived Lyman-alpha data (RHOLA, RHOLA1) which often effect the auto-scaling features in the microfilm plotting routines and wash out the plots. Short (2--3 sec) intervals around these spikes should be considered as bad data, but the remaining data points, as recorded on magnetic tape, will be valid. The performance of the data processing technique can be monitored through an examination of the difference plots (DRFHBL, DFRHB1) included in the GENPRO data set. Information on this technique, and literature references on high rate humidity measurements are available in RAF Bulletin # 22. Peak to peak response characteristics for each Lyman-alpha system were determined using the NCAR gas calibration device which is patterned after the "Stull" Lyman-alpha calibration system discussed in the literature. Although both systems generally performed well, sensor SN-1 (VLA, RHOLA) exhibited less drift and should be used as the primary high rate data source.
The microwave refractometer is a high rate humidity sensor currently under development at RAF and was included in this program on an experimental basis. System response is stable with pre and post project calibrations being conducted in the RAF calibration lab. In its current state of development, however, certain flight specific adjustments are necessary to convert the direct measurement of refractivity to an absolute humidity. These adjustments are applied systematically and resulted in only minimal impact on the humidity measurements. The instrument performed well throughout the experiment so the data have been included in the GENPRO data output. Performance during specific intervals can be monitored through an examination of the difference plot (DFRHBR) included in the data set.
The dual wavelength vegetation sensor is a new device and its performance characteristics are not well known. It appeared to perform well with signal fluctuations (VM660, VM730) mirroring some of the other radiometric measurements.
As with all radiometric sensors, these units are hard mounted and none of the data have been corrected for changes in the aircraft's flight attitude.
FIS staff general QA found the following anomalies in Dr. Grossman's aircraft flux data:
The main problems were with the Lyman-alpha Hygrometer Calibration, choosing the level leg timings for fluxes, method of flux calculation, and surface temperature estimation. All of these have been discussed or referenced in the sections above. Here is a capsule discussion.
Not available at this revision.
The Filtered Atmospheric Turbulence Data from the NCAR King Air Data Set can be used to help describe the structure of the atmospheric boundary layer over the FIFE study area, increase knowledge of the physical processes active in the daytime boundary layer, and explore the relationship of surface properties to the time and spatial variation in the structure of the boundary layer.
The FIFE field campaigns were held in 1987 and 1989 and there are no plans for new data collection. Field work continues near the FIFE site at the Long-Term Ecological Research (LTER) Network Konza research site (i.e., LTER continues to monitor the site). The FIFE investigators are continuing to analyze and model the data from the field campaigns to produce new data products.
Software to access the dataset is available on the all volumes of the FIFE CD-ROM set. For a detailed description of the available software see the Software Description Document.
ORNL DAAC User Services
Oak Ridge National Laboratory
Telephone: (865) 241-3952
FAX: (865) 574-4665
ORNL Distributed Active Archive Center
Oak Ridge National Laboratory
Telephone: (865) 241-3952
FAX: (865) 574-4665
Users may place requests by telephone, electronic mail, or FAX. Data is also available via the World Wide Web at http://daac.ornl.gov.
FIFE data are available from the ORNL DAAC. Please contact the ORNL DAAC User Services Office for the most current information about these data.
NCAR/RAF processed tapes containing the original data from which the derived data (statistics, fluxes, etc.) were estimated can be obtained from the FIS or from NCAR/RAF (cost reimbursable). These data are in the public domain. NCAR may also be able to provide these data in digital file format.
Microfilms of the output from the original data tapes discussed in the Tape Products Section are available from the ORNL DAAC.
Video camera (side-looking, downward looking) tapes from the aircraft are available from the ORNL DAAC.
Filtered Atmospheric Turbulence Data from the NCAR King Air are available on FIFE CD-ROM Volume 1. The CD-ROM filename is as follows:
\DATA\AIR_FLUX\AF_FILTR\NCAR\Yyyyy\ydddMULT.NRF for the FILTERED data.
Where yyyy are the four digits of the century and year (e.g., Y1987). Note: capital letters indicate fixed values that appear on the CD-ROM exactly as shown here, lower case indicates characters (values) that change for each path and file.
The format used for the filenames is: ydddMULT.sfx, where y is the last digit of the year (e.g., 7 = 1987, and 9 = 1989), ddd is the day of the year (e.g., 061 = sixty-first day in the year). The filename extension (.sfx), identifies the data set content for the file (see the Data Characteristics Section) and is equal to .WYF for the filtered data.
Miller and Friesen NCAR/RAF Bulletin No. 9.
Anonymous NCAR/RAF Bulletin No. 14.
Betts, A.K., R.L. Desjardins, J.I. MacPherson, and R.D. Kelly. 1990. Boundary layer heat and moisture budgets from FIFE. Boundary Layer Meteorology. 50:109-137.
Betts, A.K., R.L. Desjardins, J.I. MacPherson. 1992. Budget analysis of the boundary layer grid flights during FIFE 1987. J. Geophys. Research. 97(D17):18,523-18.531.
Grossman, Robert L. 1992a. Sampling errors in the vertical fluxes of potential temperature and moisture measured by aircraft during FIFE. J. Geophys. Res. 97(D17):18,439-18,443.
Grossman, Robert L. 1991. Temporal variation of heat and moisture flux within the atmospheric boundary layer over a grassland. Ch. 16 in Land Surface Evaporation Fluxes: Their measurement and parameterization. (Schmugge and Andre, eds.). 755pp. Springer-Verlag, New York.
Grossman, Robert L. 1992b. Convective Boundary Layer Budgets of Moisture and Sensible Heat Over an unstressed prairie. J. Geophys. Res. 97(D17):18,425-18,438.
Kelly, R. 1992. Atmospheric boundary layer studies in FIFE: Challenges and advances. J. Geophys. Res. 97(D17):18,373-18,376.
Kelly, R.D. E.A. Smith, and J.I. MacPherson. 1987. A comparison of surface sensible and latent heat fluxes from aircraft and surface measurements in FIFE 1987. J. Geophys. Res. 97(D17):18,445-18,453.
MacPherson, J.I., R.L. Grossman, and R.D. Kelly. 1992. Inter-comparison results for FIFE flux aircraft. J. Geophys. Res. 97(D17):18,499-18,514.
Sellers, P.J., F.G. Hall, G.Asrar, D.E. Strebel, and R.E. Murphy. An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). J. Geophys. Res. 97(D17):18,345-18,371.
Contact the EOS Distributed Active Archive Center (DAAC) at Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee ( see the Data Center Identification Section). Documentation about using the archive and/or online access to the data at the ORNL DAAC is not available at this revision.
A general glossary for the DAAC is located at Glossary.
A general list of acronyms for the DAAC is available at Acronyms.
April 29, 1994 (citation revised on October 14, 2002).
This document has been reviewed by the FIFE Information Scientist to eliminate technical and editorial inaccuracies . Previous versions of this document have been reviewed by the Principal Investigator, the person who transmitted the data to FIS, a FIS staff member, or a FIFE scientist generally familiar with the data. It is believed that the document accurately describes the data as collected and as archived on the FIFE CD-ROM series.
February 29, 1996.
Grossman, R. L. 1994. Aircraft Flux - Filtered: Univ. Col. (FIFE). Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/8. Also published in D. E. Strebel, D. R. Landis, K. F. Huemmrich, and B. W. Meeson (eds.), Collected Data of the First ISLSCP Field Experiment, Vol. 1: Surface Observations and Non-Image Data Sets. CD-ROM. National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland, U.S.A. (available from http://www.daac.ornl.gov).


Sonic-Shield’s Three Keys to Understanding Acoustics
Understanding acoustics, specifically indoor acoustics, is important for your interaction with a soundproofing company. Three important acoustical concepts must be understood: decibels (dB), Sound Transmission Class (STC) and Noise Reduction Coefficient (NRC):
- Sound pressure levels are typically measured and expressed in terms of decibels (dB), and are often weighted to simulate the sensitivity of the human ear (A-weighting, or dBA). For purposes of comparison, typical noise levels inside a quiet home at night in a rural or suburban environment are from 30-40 dBA, and a quiet home during the day will reach levels of 40-50 dBA. Outdoor noise during the day in an urban environment will be about 60-70 dBA and over 80 dBA near busy roads or transportation hubs. Below is a chart that outlines the United States Occupational Regulations for Noise Plus Acoustics which outlines permissible decibel levels which will enhance your understanding of acoustics.
- The STC of a wall describes how well the wall reduces (or attenuates) airborne sound. STC is generally related to the amount of noise that the wall can reduce from one side to the other. For example, a typical wall in a house or office has an STC of 33, meaning that it can attenuate 33 dB from one side of the wall to the other. Since normal human speech reaches levels of 65-70 dBA, one could hear garbled speech through a typical wall. Loud music (at levels of over 80 dBA) could be heard clearly from one side of the wall to the other.
- The NRC, on the other hand, is an indication of the amount of sound energy absorbed upon striking a particular surface. An NRC of 0 indicates perfect “bounce back” or reflection (such as a smooth concrete surface) and an NRC of 1 indicates perfect absorption. We have all experienced such rooms (or restaurants) with acoustically hard and reflective surfaces (NRC=0), where unwanted noise is amplified, making it difficult to hear speech or music. This often makes us speak louder or increase the volume of audio equipment, further increasing the overall sound levels within the room.
Sonic-Shield offers a full range of soundproofing products and develops customized soundproofing solutions to solve even the most demanding noise problems. Our team of acoustic experts can help you pinpoint sources of noise and rapidly develop cost effective solutions to eliminate, block or absorb the noise.
Sonic-Shield believes that while there may be similarities, no two noise pollution problems are exactly alike. Accordingly, our soundproofing solutions reflect our ability to identify, and when necessary, develop and fabricate the appropriate soundproofing materials for your particular needs. We hope that understanding acoustics has made you more comfortable with our process of developing a solution for your problem and will enable you to have a higher comfort level in evaluating our recommendations.


Boshin War Soldier (Version 1)
The Boshin War was a civil war in Japan, fought from 1868 to 1869 between forces of the ruling Tokugawa shogunate and those seeking to return political power to the imperial court. The war found its origins in dissatisfaction among many nobles and young samurai with the shogunate's handling of foreigners following the opening of Japan the prior decade.
Forces loyal to the Shogun and those loyal to the Imperial court modernised their forces through Western support and training. British, French, and American military experts would train units and sell weapons, and uniforms were often styled with Western influence.


|Poison Ivy Sawfly - Arge humeralis|
Order Hymenoptera - Ants, Bees, Wasps and Sawflies
Symphyta - Sawflies, Horntails, and Wood Wasps / Family Argidae
This species is a leaf miner of the poison ivy plant, Rhus radicans.
This species is a leaf miner of the poison ivy plant, Rhus radicans. It is also a convincing mimic of these wasps. Unlike many hymenopterans, sawflies do not have stingers - their ovipositors developed into the eponymous sawlike device
There are 59 species of Argid sawflies in North America. They are most easily recognized as stout-bodied insects with three-segmented antennae; the third segment being very long. Many argid sawflies are black or dark colored, but a few have bright orange or red mimicking the coloration of the Braconid parasitic wasps.
Sawflies get their name from the saw-like nature of their ovipositor. This female is using her saw to slit open blades of grass wherein she lays her eggs. It took me many attempts before I was able to capture this process. It is virtually impossible to tell what is going on while these creatures are laying eggs, it's so quick, and the structures involved are so small.
Hymenoptera (Latin for membrane wing) is a vast assemblage of insects second only to Coleoptera (beetles) in the number of described species. Hymenoptera number some 115,000 species - of which 18,000 live in North America. Hymenopterans inhabit a wide variety of habitats, and show an incredible diversity in size, behavior, structure and color.
Insects & Spiders | Bees & Wasps Index | Bees & Wasps


Night flight from Marrakech back home
Daylight-saving time ends on 28 October, and clocks have to be set back to standard time. LMU chronobiologist Till Roenneberg explains how the annual shift back and forth perturbs our internal circadian clock, and suggests how best to adapt to the changes.
You once compared advancing the clock in the spring to moving from Frankfurt to Marrakech. Can you explain why?
Till Roenneberg: Consider what actually happens when time “springs forward” or “falls back”. Only the clocks’ hands are shifted, not time itself. In reality, we all go to work an hour earlier in ‘summer time’; we just don’t notice it because clocks are advanced and show us the same time as before. But from the last weekend in March on, we live in a time zone west of our own, and the clock change artificially flattens the annual change in day-length. The net effect is equivalent to taking a trip to the southwest. Essentially, we are sent overnight to, say, Marrakech – without a return flight until the end of October. The changes effectively set our annual biological rhythms back by as much as four weeks.
You have studied the impact of the time shifts on human physiology, taking individual variation in sleep patterns, so-called chronotypes, into account. Do the early rising “larks” and the night-loving “owls” differ in their reaction to the shifts?
The clock change – notably, not the time change – favours early chronotypes and is detrimental to the late ones. Everyone’s biological clock adjusts to the increase in day-length and the earlier appearance of the sun in spring. Especially the “owls” benefit, because they find getting out of bed easier than during the winter. One has to know that about 80% of the population need an alarm clock to help them rise in time for work from their slumbers – left to themselves they would sleep longer. The clock change in spring cancels the effect of the natural adjustment of the biological clock to longer days, because it basically shifts late chronotypes back into winter mode. Early chronotypes, on the other hand, are happy because they can go to work earlier.
Young people are prototypical owls, being late sleepers in both senses of the term. Is the transition more of a burden for them than for older adults and younger children, who tend to be on their toes at five in the morning?
That young people between the ages of 16 and 22 are the latest chronotypes over the course of a lifetime is a biological rather than a social phenomenon. In early childhood, the body clock has an early set-point, and then advances progressively throughout puberty and adolescence. From then on, the process is reversed until – in old age – we suffer from so-called “senile insomnia”. Being late chronotypes, young people indeed suffer most from the change in clock time, because it is particularly difficult for their body clock to adapt to the new schedule. The biological clock being out of sync with the social clock leads to what we call “social jetlag”, which can result in insomnia, obesity, depression, and in addictions to caffeine, nicotine and alcohol.
You have been arguing for years that the practice of switching back and forth should be abandoned, and Russia actually took this step last year. Do you know of any negative effects arising from the still greater difference in clock time relative to the countries of Central Europe?
Russia indeed decided last year not to change the clocks anymore. Unfortunately, the government did not put an end to “summer time”. On the contrary, it decided that summer time should be observed throughout the year. Not surprisingly - from a chronobiological standpoint - people protested. I recently heard from a Russian colleague that, due to the strong opposition, the decision is to be repealed. The money spent on implementing this costly and ultimately pointless measure would have been better invested in research.
Do you have any advice for late chronotypes and others, who find the switch particularly challenging, that might help them to adapt to the changes?
First of all, I should emphasise that we now get back the hour that was stolen from us in the spring. This weekend, we go back to our ‘normal’ time and thereby reduce our social jetlag. The autumn change should therefore be easy to adapt to. The switch in the spring is a different matter, with potentially serious effects on health. The frequency of heart attacks increases markedly around this time.
One factor that facilitates adaptation to both transitions is natural light. To ease the shift in March, one should get out as much as possible in the morning and stay as little as possible in the evening. To help yourself shift back in October, simply do the opposite.


Smartest Elephant in Captivity Has "Aha!" Moment
Aha! Smart Elephant Uses Insight to Solve Problems
The Washington Post's science writer Brian Vastag reported this week on how the National Zoo's youngest elephant showed researchers just how thoughtful the animals can be.
Elephants are near the top of the brainiest creatures list, along with chimpanzees and dolphins. Now, scientists have added a new talent to elephants' mental repertoire: The ability to solve a problem using insight...that aha! moment when your internal light bulb switches on and you figure out the solution to a puzzle. Previously, only a limited number of species, including certain primates, crows, and parrots were known to have this ability.
The National zoo's youngest elephant, Kendula, a nine-year-old, discovered recently how to reach food hanging from a branch by spontaneously and creatively moving a cube and standing on the cube to get to the food. This problem-solving activity has proven Kendula to be one of the the smartest elephants if anyone has ever seen in captivity! So cool!
Check Kendula out in action in the video below and read more about Kendula and the study at ScienceMag.com


- 1 Why were Tamils killed in Sri Lanka?
- 2 What is the main problem in Sri Lanka?
- 3 What happened to the Tamils in Sri Lanka?
- 4 When was the Sri Lanka terrorist attack?
- 5 Why were the Tamils of Sri Lanka angry?
- 6 Are Tamils Safe in Sri Lanka?
- 7 Is Sri Lanka poor?
- 8 What religion is in Sri Lanka?
- 9 Is it safe in Sri Lanka?
- 10 Who came to Sri Lanka first Tamils or Sinhalese?
- 11 How many Sri Lankan soldiers died?
- 12 Is LTTE still active?
- 13 What religion are Tamil Tigers?
- 14 What is the reason to adopt Majoritarianism in Sri Lanka?
Why were Tamils killed in Sri Lanka?
The LTTE initially carried out a campaign of violence against the state, particularly targeting policemen and also moderate Tamil politicians who attempted a dialogue with the government. Between 400–3,000 Tamils were estimated to have been killed, and many more fled Sinhalese-majority areas.
What is the main problem in Sri Lanka?
Air pollution and water pollution are challenges for Sri Lanka since both cause negative health impacts. Overfishing and insufficient waste management, especially in rural areas, leads to environmental pollution. Sri Lanka is also vulnerable to climate change impacts such as extreme weather events and sea level rise.
What happened to the Tamils in Sri Lanka?
A United Nations panel found that as many as 40,000 Tamil civilians may have been killed in the final months of the civil war. In January 2020, President Gotabaya Rajapaksa said that the estimated 20,000+ disappeared Sri Lankan Tamils were dead. One-third of Sri Lankan Tamils now live outside Sri Lanka.
When was the Sri Lanka terrorist attack?
However, its major terrorist act was reported during Easter Sunday on 21 April 2019, when suicide bombers of the group attacked three Catholic churches in Colombo, Negombo and Batticaloa and three luxury hotels in Colombo. 259 people were killed, including at least 45 foreigners, and over 500 more were injured.
Why were the Tamils of Sri Lanka angry?
Tamils of Sri Lanka were angry because their demands were repeatedly denied by the Sinhala community. Their demands were: To consider Tamil an official language too.
Are Tamils Safe in Sri Lanka?
The Department of Foreign Affairs and Trade’s (DFAT) latest country report from 2019 says Tamils in Sri Lanka ” face a low risk of official or societal discrimination ” and “a low risk of torture overall” — an assessment starkly at odds with those of the UN, US and EU.
Is Sri Lanka poor?
In terms of World Bank estimates of per capita GDP Sri Lanka is a poor country indeed: twenty-fifth from the bottom of their list of 125 countries. Income distribution is considerably less unequal than in most developing countries.
What religion is in Sri Lanka?
Buddhism is the largest religion of Sri Lanka with 70.2% of the population practicing the religion; then, there are Hindus with 12.6%; Muslims with 9.7% and Christians with 7.4%. The census indicates that most Muslims are Sunni while the Christians are mainly Roman Catholic.
Is it safe in Sri Lanka?
You could encounter violent crime in Sri Lanka, including sexual assault and robbery. Pickpocketing, bag snatching and other petty crime are risks. Be careful of theft in crowds, and in these locations: markets.
Who came to Sri Lanka first Tamils or Sinhalese?
The Sinhalese are allegedly the descendants of the Aryan Prince Vijaya, from India, and his 700 followers; they came to Sri Lanka about 485 B.C.E., chased from their homes for their marauding activities. Tamils fall into two groups: Sri Lankan and Indian.
How many Sri Lankan soldiers died?
The Sri Lankan civil war was very costly, killing an estimated 80,000-100,000 people between 1982 and 2009. The deaths include 27,639 Tamil fighters, more than 21,066 Sri Lankan soldiers, 1000 Sri Lankan police, 1500 Indian soldiers, and tens of thousands of civilians.
Is LTTE still active?
Firstly, the Sri Lankan Government has taken strict military measures to prevent any kind of regrouping of the LTTE. It continues with the Emergency laws and Prevention of Terrorism Act. However, the LTTE sympathizers are still there in foreign countries. They have formed a Trans-National Government of Tamil Eelam.
What religion are Tamil Tigers?
Their religion (most are Hindu ) and Tamil language set them apart from the four-fifths of Sri Lankans who are Sinhalese—members of a largely Buddhist, Sinhala-speaking ethnic group.
What is the reason to adopt Majoritarianism in Sri Lanka?
Majoritarianism in Sri Lanka: The leaders of the Sinhala community sought to secure dominance over government by virtue of their majority. As a result, the democratically elected government adopted a series of Majoritarian measures to establish Sinhala supremacy.


Yellowstone National Park Report
Above- and belowground biomass of Idaho fescue Festuca idahoensis and bluebunch wheatgrass Agropyron spicatum and nematode densities under these plant species were sampled during the growing season inside and outside a 2-year old exclosure on Crystal Bench in Yellowstone National Park. Early in the growing season, grazed plants of both species had lower shoot and root biomass than ungrazed plants. Standing biomass of grazed plants was equal to ungrazed plants at the end of the growing season. Densities/g root biomass of phytophagous and bacterial feeding nematodes were higher under grazed than ungrazed plants of both plant species only early in the growing season. Foliar concentrations of nitrogen in grazed plants were higher than ungrazed plants but there was no difference in root nitrogen between grazed and ungrazed plants. The effects of ungulate grazing on the Northern winter range of Yellowstone National Park has recently received considerable attention (Frank 1990, Coughenour 1991, Singer 1992, Wallace submitted). Early interest in this topic centered around the question to cull or not to cull elk in the Park. However, as the concepts of "maintaining ecological processes" (Houston 1982) and "ecosystem management" (Keiter 1991) have gained acceptance in Park management, understanding the dynamics and interactions of a broader array of herbivores inhabiting the Park have become increasingly important. In this paper, we describe the results of a study which focused on the effects of aboveground herbivory on nematode density and trophic structure. Root-feeding nematodes are major herbivores in other grassland systems and may consume twice as much biomass as aboveground consumers (Ingham and Detling 1984, Stanton 1988). Houston (1982) reported that nothing is known about the effects of nematodes on the native grasses of the northern range especially in combination with aboveground grazers. We hypothesized that if spring grazing is intense, grazed plants would initially show a decline in root growth and phytophagous nematodes. Cessation of root growth is a common response of plants to grazing and may occur within the first 2-24 hours (Hodgkinson and Baas Becking 1977). Evidence to date supports the idea that phytophagous nematode densities are highest under moderate levels of grazing and low under heavily grazed and ungrazed plants (Stanton 1983, Stanton et al. 1984, Seastedt 1985, Seastedt et al. 1988). Because senescing roots, subsequent to grazing, provide increased substrates for decomposers, we also hypothesized that microbial activity and nitrogen mineralization should increase (Stanton et al. 1984). As a result, we expected to detect an increase in microbial feeding nematodes. As root regrowth occurred, we expected phytophagous nematodes to increase. However, we predicted that populations would not reach levels found under ungrazed plants because plants in grazed areas experience higher levels of nitrogen mineralization (Holland and Detling 1990) than ungrazed plants and may produce proportionally fewer numbers of root hairs (nutrient absorption organs) which serve as feeding sites for nematodes. Because of reduced densities of phytophagous nematodes and increased mineralization rates under grazed plants, we expected grazed plants to recoup their losses rapidly. The net result we predicted would be no detectable differences in aboveground or belowground biomass during years of normal rainfall. Thus, our study addressed 3 null hypotheses. First, root and shoot biomass of grazed and ungrazed plants will be similar at the end of the growing season. Second, density of phytophagous and microbial feeding nematodes will not differ between grazed and ungrazed plants. Finally, nitrogen concentration of roots and aboveground foliage will not be higher in grazed than in ungrazed plants. We focused our attention on bluebunch wheatgrass Agropyron spicatum and Idaho fescue Festuca idahoensis because of their importance as winter range forages and because Mueggler (1975) reported that bluebunch wheatgrass was more sensitive and recovered more slowly to heavy clipping than Idaho fescue.
Merrill, Evelyn; Hak, Jon; and Stanton, Nancy
"Responses of Nematodes to Ungulate Herbivory on Bluebunch Wheatgrass and Idaho Fescue in Yellowstone National Park,"
University of Wyoming National Park Service Research Center Annual Report: Vol. 18
, Article 19.
Available at: http://repository.uwyo.edu/uwnpsrc_reports/vol18/iss1/19


Thursday June 14, 2018
If you want to serve on a criminal jury, the most important rule is this: Say as little as possible, with your words, your body language, and your appearance.
But why would you want to sit on a jury in the first place? Because in a criminal trial, if you can read and reason and resist being swayed by emotion, you will make a better juror than most of your fellow members of the community. A jury is the entity that acts as the voice of the community, and serving as a juror allows you to contribute to that voice.
You may also believe that the law under which the defendant is being prosecuted is an illegitimate use of state power. In that case, acting as a juror gives you the opportunity to exercise the power of jury nullification—finding the defendant "not guilty" regardless of whether the state has proven the accusation beyond a reasonable doubt.
This power to nullify an unjust law is as old as the institution of the jury; it's a practice rooted in the principle that a juror can and should reach whatever verdict her conscience leads her to, and that there is nothing the government, or anyone else, can do to stop her beforehand or punish her afterward. Of course, the state prefers to maintain tight control over trials. In most jurisdictions, defense lawyers are prohibited from telling juries about nullification, and judges and prosecutors will, if pressed, lie and tell jurors they may not vote to nullify. But that is all the state can do to try to stop it. Knowing the truth will keep you from being deceived.


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Because most people don't have the luxury of sitting down uninterrupted for hours at a time to learn Outlook, this 10-Minute Guide focuses on the most often used features, covering them in lessons designed to take 10 minutes or less to complete. In addition, this guide teaches the user how to use Outlook without relying on technical jargon. By providing straightforward, easy-to-follow explanations and lists of numbered steps that tell the user which keys to press and which options to select.
1. What's New in Outlook 2002?
Getting Organized with Outlook 2002. New Features in Outlook 2002.
Starting Outlook. Understanding the Outlook Window. Using the Mouse in Outlook. Working Offline. Exiting Outlook.
Types of Outlook E-mail Configurations. Adding Other Types of E-mail Accounts. Deleting E-mail Accounts.
Using the Outlook Bar. Using the Folder List. Changing Views. Creating Custom Views. Using Outlook Today.
Help: What's Available? Using the Ask a Question Box. Using the Office Assistant. Using the Help Window. Getting Help with Screen Elements.
Composing a Message. Formatting Text. Selecting the E-mail Message Format. Checking Spelling. Add a Signature. Sending Mail. Recalling a Message.
Reading Mail. Saving an Attachment. Answering Mail. Printing Mail.
Deleting Mail. Forwarding Mail. Creating Folders. Moving Items to Another Folder.
Attaching a File Attaching Outlook Items.
Saving a Draft. Viewing Sent Items and Changing Defaults. Using the Organize Tool. Creating Advanced Rules with the Wizard.
Working with Message Options. Using Message Flags.
Understanding the Outlook Address Books. Using the Address Book. Importing Address Books and Contact Lists. Exporting Outlook Address Records.
Creating a New Contact. Viewing the Contacts List. Using Distribution Lists. Communicating with a Contact. Viewing a Map of a Contact's Address.
Navigating the Calendar. Creating an Appointment. Scheduling a Recurring Appointment. Planning Events. Inserting an Object.
Scheduling a Meeting. Working Out Meeting Details. Editing Meeting Details and Adding Attendees.
Entering a Task. Creating a Recurring Task. Assigning Tasks to Others. Viewing Tasks. Managing Tasks. Recording Statistics About a Task. Tracking Tasks.
Creating a Journal Entry. Viewing Journal Entries.
Creating Notes. Setting Note Options. Managing Individual Notes. Viewing Notes.
Choosing Page Setup. Previewing Before Printing. Printing Items. Printing Labels and Envelopes. Setting Printer Properties.
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As long as there will people in this world, there will be business, and as long as there is business, there will be accounting. Accounting is involved in virtually everything in our lives whether we know it or not, and its importance can sometimes be overlooked. Everything needs accounting. From a baseball cap you wear, to the food you eat, to the company you work for, or even the music you hear on the radio, accounting is needed and was involved in all of those things steps of production or operation.
An accountant measures, aggregates and reports financial information necessary for the sake of decision making by organizational managers, owners, investors, government agencies and other users. From the previous statement, we can see the indication that shows how accounting is important in careers. Everything needs accounting. Whether you are a doctor, or an ordinary office worker, you would still need to play with numbers and take into account the cost of things that you used or used by someone else. Timely and accurate information on operations is crucial to success in the recent days of rapidly changing business environment, and the accountant is the one responsible for the critical business function that mostly though indirectly affects our career and everyday life.
In the career perspective every occupation must carefully manage their money, their cash inflows and outflows. Jobs such as independent contractors have to analyze how much the job is going to cost them and how much they are going to charge for the job. They must track all their expenses and estimates in order to not under cost and to charge appropriate prices. In simplest form, this is accounting. If not accurately tracked and estimated, that contractor’s business will not profit and eventually go out of business. Same thing goes for big business, if that company or firms does not accurately handle the operations of its cash flows, it will not sustain for long. More and more today employees such as secretaries and receptionists are being taught basic accounting practices to help aid the accountants and to keep proper bookkeeping. This way these employees can manage the lower importance items of a business and leave transactions of higher significance to the accountant to analyze and interpret for decision making purposes.
In one’s life, can we say accounting will promote a better lifestyle? I say, definitely yes. The management and control of personal finance is a subject of immense importance today to people. In the world that we lived in today, there is barely a single person who lives without a single debt in their life. Debts ranging from credits cards, car payments, mortgages, and so on, one can find themselves stuck in economic hardships that in some cases destroy lives and has become wake-up calls to many people. Wouldn’t it be nice to know exactly where the increases and decreases are coming from and going to, respectively, to whatever degree of detail you need? Of course it would be. Many of us spend without much thought on the impact of the action. It is essential on knowing the proportions being spent on the basic necessities and luxuries and how to have that proper balance across our expenditure. Life is not as easy as driving on the road with traffic lights and signs along the way, but we still need financial signals in our life whether we are in a comfortable phase or in the danger phase that requires help. Accounting can help people detect these dangers. If not much, the least it can do is ensure that your debt is under control and manageable.
Handling all the calculations of accounting can be tedious and bothersome but can make all the difference one’s finances. You can strategically plan ahead to save for your retirement, your children’s college fund, or luxury or two later down the road. We can see the importance of this concept by noticing companies today whose operations are strictly financial advising. It can be concluded that in the era that we are living in today, Families, and retirees depend on the credibility of financial reporting for their futures and livelihoods. With financial pressure amounting in many peoples’ lives, accounting is seen as important in so many ways that it is almost impossible to live without it.


Experts say that the world’s data is doubling every two years. This epic increase in Big Data has highlighted the limitations of reliance on traditional forms of data storage and management and focused attention on new methods for addressing the volume and variety and veracity of structured and unstructured data. In these discussion, one of the terms you’ve undoubtedly heard lots of buzz around is the expression “NoSQL.” The market is a formidable one with projected growth forecast to reach $3.4 Billion in 2020, representing a compound annual growth rate (CAGR) of 21% for the period 2015 – 2020.
NoSQL is a technical term that means either “No SQL” or “Not only SQL” and represents a paradigm shift away from sole reliance on relational databases. Relational databases (RDMS) emerged in the 1970s and were based on a set of data tables that could be queried and matched based on languages like SQL. These database architectures were ‘structured’ meaning that the data was organized in a uniform format and varied little over time.
The premise behind NoSQL is The Key-Value Pair (KVP) which is a framework where each record has a primary key and a collection of values (bins) associated with that record. The way Key Values work can be demonstrated in this sample chart below:
Here you see a ‘key’ in the column and a ‘value’ in the right; notice it can be a string, integer, or the like. Most KVP objects allow you to store any object on the right, because it’s just a value. According to this schema there will always be a unique key for a particular object that needs to be returned. Querying the database for that unique key will return the results back from whichever node has the object.
There are any number of applications for NoSQL data storage and processing solutions today, ranging from user profile stores, to ecommerce sites, to mobile applications. The ability to deliver high-volume, high-variety online applications for a fraction of the cost that it took with traditional methods, is also one of the reasons why NoSQL technologies are an appealing solution for smaller organizations with limited budgets.
With this brief overview in mind, we can now begin to explore some of the top NoSQL solutions on the market today. Below we’ve compiled a list that startups and small businesses should seriously look into if they wish to start implementing NoSQL solutions and earning maximum ROI.
Chances are good that you’ve heard of MongoDB. Named after the word “humongous”, MongoDB is an open-source document database that has been around since 2007 and since then has gained a reputation as the world’s most popular NoSQL database. MongoLab is a MongoDB startup that began in 2011 as fully managed cloud database service hosting MongoDB databases and running on cloud providers Amazon, Google, Joyent, Rackspace and Windows Azure. The value of MongoLab is that it has combined the right business (MongoDB) and the right delivery model (PaaS) in a manner that has really appealed to developers in the NoSQL community.
The extensibility and scalability of the MongoDB and MongoLab offerings are especially impressive as measured by their growing footprint in the data storage market. MongoDB Inc., in collaboration with Microsoft and MongoLab, announced last summer a fully-managed MongoDB-as-a-Service Add-On offering on the Microsoft Azure store.
MarkLogic is a Silicon Valley-based private software firm founded in 2001 that builds what it calls “the Only Enterprise NoSQL Database” on the market. Starting with its roots in XML databases, the firm has leveraged over a decade of experience in developing solutions for unstructured data, leading it to embrace the “enterprise NoSQL” label. The experience has paid off and the firm is well-funded with a total of $73.6 Million received in 6 rounds from 5 investors – the most recent being over $25 Million in April 2013.
MarkLogic defines its solution as “a document-centric, transactional, search-centric, structure-aware, schema-agnostic, programmatic, high-performance, clustered, database server,” and has received considerable accolades throughout the industry. In 2013 MarkLogic released a new semantics platform called MarkLogic 7, which provides the capability of storing billions of RDF triples that can queried with SPARQL to provide richer, deeper into your data in ways not possible with NoSQL or relational models.
A recent funding round of $60 million makes Couchbase one of the emerging and newly popular NoSQL databases on the market today, rivaling MongoDB. One of the key areas where Couchbase has innovated is in the area of providing streamlined scalability and performance for interactive applications at the intersection of what it calls “three interrelated megatrends – Big Data, Big Users, and Cloud Computing.” Cloudbase has also pushed data management for mobile offerings by enabling users to easily synchronize data between mobile devices and the cloud.
Couchbase’s open source technology is available in two versions: a Community Edition that comes without recent bug fixes, and the stable Enterprise Edition for commercial use. Couchbase builds are available for Ubuntu, Red Hat, Windows and Mac OS X platforms.
CouchDB’s features are more easily accessible through its built-in administration web interface called Futon, which allows users to manage their databases, view and edit documents, compose and run MapReduce views, and trigger replication between databases.
Amazon’s DynamoDB is a fully proprietary NoSQL database service that leverages Amazon’s immense cloud-computing infrastructure. DynamoDB is the culmination of Amazon’s 15 years of experience in building non-relational databases for its own internal needs, and represents the cloud-based version of this technology designed for external customers.
With DynamoDB all you have to do is create the database table and the service does the rest. As you scale up there is no need for hardware or software provisioning, setup and configuration, software patching, operation of a distributed database cluster, or the need to partition data over multiple instances. DynamoDB is unique in that it works on the principle of “throughput” rather than storage. Based on this model, the Amazon service will ensure that DynamoDB allocates the machine resources to meet your throughput needs along with the guarantee of consistent, low-latency performance.
With the massive growth expected in the Internet of Things market and the enormous Big Data sets this will produce, startups and small businesses would be well advised to start looking seriously at real world NoSQL use cases and taking measures to adopt the latest benefits of this technology. The alternative will mean getting left behind in the market and swallowed up by your competition. So go ahead and get onboard with NoSQL today; you’ll be glad you did!


This is it. You have always wanted to learn to play the drums. Now you have finally bought your first drum set, taken your first drum lessons and know the difference between the snare drum, toms, bass drum and know which cymbals are the hi-hat and which the crash or ride.
Now, of course, any beginner drum lessons, including drum lessons for kids will include not just instruction in drum notation, but also pieces for practising your strokes, fills and drum rolls.
And this is all very nice, but of course, you want to learn how to play the drums so you can groove with the best bands out there, not spend all your time listening to a metronome and counting beats.
Naturally, it’s important for beginners to learn the rudiments of the craft. And that means doing the drudge work. Without a good technique, you won’t get the most out of your drum sets. Rolls need to be practised until they flow smoothly. You need to know stick control, how hitting with the butt or the head of your drum sticks changes the sound, what brushes can do and how to make the best of them. Improving your craft means practising, it means discipline.
That doesn’t mean you can’t use actual songs to practice your technique. Learning to integrate your drumwork into a musical piece is an essential part of being a musician.
So here are some easy drum songs you can play as a beginner, from some of your favourite bands.
Want to learn how to drum like Buddy Rich? Play jazz like Max Roach? Though learning to improvise is part of jazz music culture, you first need to understand the tempos of jazz, the rhythms used and the colour and tone of the music.
Drumming along to jazz numbers will help you in mastering your technique so you can move on to the more complicated and challenging music and improv that will eventually let you land your first gigs.
So here are some simple jazz numbers to play along to.
Practise your jazz drumming with these easy songs. Photo credit: Deseronto Archives on Visual Hunt
You might find your drum teacher giving you Cantaloupe Island to help you with the concept of beat displacement. Usually associated with the backbeat, the snare drums don’t fall exactly on the count, but slightly before or after it. It can be a full eighth note but is generally more around a 16th note. Original drummer Tony Williams also sometimes added buzz fills on these displaced beats.
Salvaged from the oddly-named musical “The Great Magoo”, enough people had their hands in writing and composing this song, originally titled “If You Believed in Me” that it’s hard to tell exactly how it came about. It has been performed by a lot of different artists including Art Blakey. The latter’s interpretation is well worth listening to for any percussion student.
Your drum teacher might have you play this jazz song. It has a lengthy drum solo that will help boost your confidence and give you plenty of experience in learning how to play the drum.
Do you know the different drums that comprise a drum set?
This song has a mellow rhythm that’s perfect for beginners. The verse uses the bass drum, snare and hi-hat; then switches to the cymbals – both ride and hi-hat – for the chorus. The slow drumming makes switching the instrument constellation on your drum kit easy on you. It’s almost as though the Beatles had designed this song for teaching someone how to play the drums.
Beatles songs often have a simple but very atmospheric drum part. Photo credit: alex.bretado on VisualHunt.com
Another Beatles song for learning to play the drums is Ticket to Ride. It’s a bit more upbeat and rhythmic than In My Life, but that’s all the better for improving your timing. It doesn’t have any tricks. It begins with the bass, adding some colour with the toms and snare: bass-snare-bass-bass-snare-tom. You can practise double-handed strokes for a crisper tone.
This song was first played by drumming legend Ginger Baker. It’s a wonderful song for practising your drum patterns and fills. It also teaches you how to use percussion instruments to create atmosphere in a song.
Learn more about Ginger and other famous drummers!
From the 1969 album “Let It Bleed”, this song is wonderful for beginners because it has a very classical rock’n’roll pattern using the bass drum, snare and ride cymbal. The start of the song is fairly stark, but has some drum fills later on. You can start by leaving them out, slowly adding them as you grow familiar with it.
Whether you want to learn how to play funk or heavy metal, you will need to know the typical drum beats, how to use that drum pedal and play drum rolls and fills.
When you learn how to play the drums, you might want to try the percussion part of Michael Jackson’s “Billie Jean”. “Billie Jean” uses a common drumbeat often called the “money beat” that is very easy to learn.
The snare drum will land on the counts of two and four – commonly referred to as a backbeat. The bass drum comes down on one and three.
When you learn how to play this piece, learn the drum part first until you have the switch between snare and bass down pat.
Then you can add work with the pedal for the hi-hat. It is played as a one-eighth note – when counting, tap the cymbal pedal twice for each count. This will improve your hand-foot coordination.
Also discover the parts of modern drum kits…
For a lighter tune with a catchy rhythm, try “Teenage Dream” by Katie Perry. It’s a mid-tempo song with a simple, unassuming drum part that will teach you rudimental drumming techniques
A characteristic part of rock band AC/DC’s drummer Phil Rudd’s style is the absolute absence of fills. This makes his songs a good way to learn how to play the drum. “You Shook Me All Night Long” does have a few particularities that will challenge you to bring your drumming up to another level. It’s mostly in the rhythm: the kick drums fall on the count of one and the OFFBEAT of three – that little hitch while you say “…three AND four”.
First played by legendary drummer Dave Grohl, “Heart-Shaped Box” has a very simple drum melody, but is perfect for learning the little technical points, frills and rim clicks that liven up a drum solo. It’s also great for becoming well-rounded and working on your dynamics. Dave Grohl’s switch between the quiet verses to the heavier choruses is a wonderful example of the kind of control that makes drumming an art form.
Nirvana’s “Heart-Shaped Box” is good beginner piece for the drums. Photo credit: Oldtasty on Visual hunt
This song is well known for its drum intro. It’s a rewarding piece for beginner drum practice as it slowly moves up from the floor tom to the snare to increase the volume, setting up the guitar’s entrance. The ride cymbal gives the beat in eighth notes; the bass drum comes in once on the count of one and twice on three, with the floor tom coming in on two and four. Since these also correspond with cymbals, it will help you learn to synchronise cymbals and drums.
An interesting song for the modern drummer – a slow song that still has a lot of tempo to it. Its perfect for experimenting with drum fills; it also has a triplet ride pattern that will challenge you as a beginner percussionist without being too hard to master at that skill level.
Discover also the long history of drumming…
If you are truly passionate about playing the drums, you will want to practice during every spare minute. But you may be finding yourself frustrated by your usual drumming lessons and want something a little different to learn that kick technique or master the ride cymbal.
Put your favourite music-video DVDs or band CDs into the player, check out your playlist or simply switch on YouTube and watch and listen to the drummer. Don’t try to follow the melody, simply tune in to the drumming voice or look exclusively at the drummer in a video. Listen to it a few times like that, then start drumming in time with the band’s lead drummer. If you’re unsure of the way it goes, the drum line for most pop and rock songs are available as sheet music through an online music shop. Learning how to read music is an essential skill in classic drum lessons.
Learning how to use your drum kit can be approached from several different angles. Photo credit: nikolaymarushchak on VisualHunt.com
If you don’t have an ear for music that lets you keep up with the most famous drummers of all time, you can instead take online drum lessons. This YouTube channel walks you through how to play a great number of modern songs.
Or you can download a drum and bass track of the songs you want to master – basically, only the drum grooves from the song, without the guitar player or bass guitar or vocals.
Another option is to download a backing track that will have all the parts EXCEPT the drum, so you can play along with a virtual band without another drum set confusing you.
Obviously, if regular drum lessons are not cutting it, you might not want to deal with yet another drum course with a teacher taking care of several different students at once. The best is to go for a private tutor. Though it can be more expensive, it might be a good idea to have the teacher come to your home and give a private lesson. He or she can work more intensely, as well as to motivate you to stick with drums with interesting pieces to practice on.
Superprof has a wide selection of drum tutors near you to help you improve your drumming. Why not check out our drum lessons Edinburgh, or London?
Now learn more about the drums and drumming…