Reprocessing of Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) Data Set for Global Water and Energy Cycle Research
Principal Investigator (PI): Chung-Lin Shie, University of Maryland, Baltimore County
We propose to resume processing of, and to reprocess, the Goddard Satellite-based Surface Turbulent Fluxes (GSSTFYC) data set. This data set has been widely used by scientific communities for global energy and water cycle research and regional and short period data analysis since its official release in 2000/2001. Accurate sea surface fluxes measurements are crucial to understanding the global water and energy cycles. The oceanic evaporation that is a major component of the global oceanic fresh water flux is particularly useful to predicting oceanic circulation and transport. Remote sensing is a valuable tool for global monitoring of these flux measurements. The GSSTF algorithm has been developed and applied to remote sensing research and applications. An old/retired version (version 2) of GSSTF (i.e., GSSTF2) covered the data period starting in July 1987 and ended in December 2000.
Objective: The objective of this project is to continually produce a uniform data set of sea surface turbulent fluxes derived from remote sensing data and analysis that have been and continue to be useful for global energy and water flux research and applications.
Method/Technique: Our approach is first to update the algorithm codes with bug fixes discovered and reported by users and developers of GSSTF2. The data set will then be reprocessed and brought up-to-date using improved input data sets. The input data sets include a recently released National Centers for Environmental Protection (NCEP) sea surface temperature (SST) analysis, a uniform (across satellites) surface wind and microwave brightness temperature (TB) data set from the Special Sensor Microwave Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites produced by the Wentz of Remote Sensing Systems. To gauge the improvement of the data sets over the previous version and provide error/confidence estimates, the surface fluxes will be compared with historical field experimental data and buoy observations. Error estimates of the flux products will be included in the documentation.
Significance of the Project: There have been three subsequently improved datasets: (1) GSSTF2b (global 1°×1°; July 1987-December 2008) using upgraded and improved input data such as the SSM/I V6 brightness temperature (TB) and the NCEP-DOE Reanalysis II SST; (2) GSSTF2c (global 1°×1°; July 1987-December 2008) using the lately corrected TB’s by removing artificial trends due to the temporal variations (decreasing) of Earth incidence angle of individual SSM/I satellites; (3) GSSTF3 (global 0.25°×0.25°; January 1998-December 2008 so far) with a finer spatial resolution and using an updated method for retrieving surface specific humidity (Qa); developed and distributed in October 2010, October 2011, and June 2012, respectively, by this revived GSSTF project. The GSSTF products are useful for diagnosing the global water and energy cycle and hence can contribute to the goals of NASA Energy and Water Cycle Study (NEWS) and World Climate Research Program/Global Energy and Water Experiment (GEWEX). Model climate simulations show an enhanced hydrologic cycle, which must be corroborated with observations. The daily temporal and one-degree spatial resolution of the product can be used to examining climate variability at these scales. Oceanic evaporation contributes to the net fresh water input to the oceans and drives the upper ocean density structure and consequently the circulation of the oceans. On the other hand, the quarter degree spatial resolution of the product can be used to studying the hurricane-ocean interaction of higher frequency scenario. Fully tested, these products can serve as a crucial input for data assimilation of oceanic global climate models (GCMs) for forecasting.
Distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC)
Last Updated: Jun 6, 2019 at 2:12 PM EDT