Surface Turbulent Fluxes, 1x1 deg Daily Grid, Set1 V2c

nasa-test-0.demo.socrata.com | Last Updated 19 Jul 2015

These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-2c (GSSTF2c) Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie (UMBC/GEST, NASA/GSFC), converted to HDF-EOS5 format. The stewardship of this HDF-EOS5 dataset is part of the MEaSUREs project, http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects/surface-turbulent-fluxes-esdr http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects GSSTF version 2b (Shie et al. 2010, Shie et al. 2009) generally agreed better with available ship measurements obtained from several field experiments in 1999 than GSSTF2 (Chou et al. 2003) did in all three flux components, i.e., latent heat flux [LHF], sensible heat flux [SHF], and wind stress [WST] (Shie 2010a,b). GSSTF2b was also found favorable, particularly for LHF and SHF, in an intercomparison study that accessed eleven products of ocean surface turbulent fluxes, in which GSSTF2 and GSSTF2b were also included (Brunke et al. 2011). However, a temporal trend appeared in the globally averaged LHF of GSSTF2b, particularly post year 2000. Shie (2010a,b) attributed the LHF trend to the trends originally found in the globally averaged SSM/I Tb's, i.e., Tb(19v), Tb(19h), Tb(22v) and Tb(37v), which were used to retrieve the GSSTF2b bottom-layer (the lowest atmospheric 500 meter layer) precipitable water [WB], then the surface specific humidity [Qa], and subsequently LHF. The SSM/I Tb's trends were recently found mainly due to the variations/trends of Earth incidence angle (EIA) in the SSM/I satellites (Hilburn and Shie 2011a,b). They have further developed an algorithm properly resolving the EIA problem and successfully reproducing the corrected Tb's by genuinely removing the "artifactitious" trends. An upgraded production of GSSTF2c (Shie et al. 2011) using the corrected Tb's has been completed very recently. GSSTF2c shows a significant improvement in the resultant WB, and subsequently the retrieved LHF - the temporal trends of WB and LHF are greatly reduced after the proper adjustments/treatments in the SSM/I Tb's (Shie and Hilburn 2011). In closing, we believe that the insightful "Rice Cooker Theory" by Shie (2010a,b), i.e., "To produce a good and trustworthy 'output product' (delicious 'cooked rice') depends not only on a well-functioned 'model/algorithm' ('rice cooker'), but also on a genuine and reliable 'input data' ('raw rice') with good quality" should help us better comprehend the impact of the improved Tb on the subsequently retrieved LHF of GSSTF2c. This is the Daily (24-hour) product; data are projected to equidistant Grid that covers the globe at 1x1 degree cell size, resulting in data arrays of 360x180 size. A finer resolution, 0.25 deg, of this product has been released as Version 3. The GSSTF, Version 2c, daily fluxes have first been produced for each individual available SSM/I satellite tapes (e.g., F08, F10, F11, F13, F14 and F15). Then, the Combined daily fluxes are produced by averaging (equally weighted) over available flux data/files from various satellites. These Combined daily flux data are considered as the "final" GSSTF, Version 2c, and are stored in this HDF-EOS5 collection. There are only one set of GSSTF, Version 2c, Combined data, "Set1" It contains 9 variables: "E" 'latent heat flux' (W/m**2), "STu" 'zonal wind stress' (N/m**2), "STv" 'meridional wind stress' (N/m**2), "H" 'sensible heat flux' (W/m**2), "Qair" 'surface air (~10-m) specific humidity' (g/kg), "WB" 'lowest 500-m precipitable water' (g/cm**2), "U" '10-m wind speed' (m/s), "DQ" 'sea-air humidity difference' (g/kg) "Tot_Precip_Water" 'total precipitable water' (g/cm**2) The double-quoted labels are the short names of the data fields in the HDF-EOS5 files. The "individual" daily flux data files, produced for each individual satellite, are also available in HDF-EOS5, although from differe...

Tags: earth science, atmosphere, atmospheric pressure, atmospheric temperature, atmospheric water vapor, atmospheric winds, atmospheric radiation, oceans, ocean heat budget, ocean pressure, ocean temperature, ocean winds, ngda, national geospatial data asset