Presenter(s):
Dr. Jerry Zhan, Research Physical Scientist, NOAA NESDIS Center for Satellite Applications and Research
Description:
Soil moisture is one of the critical land surface variables that impact energy and water vapor fluxes into the atmosphere, and thus affecting NOAA weather, climate and hydrological forecasts. Satellite remote sensing is currently the only approach providing the near real time and spatially distributed soil moisture observations at regional and global scales, which are essential for forecast model verification, calibration and performance improvement. To meet the data needs of NOAA's numerical weather and water prediction models, NESDIS has developed the soil moisture operational product system (SMOPS). In addition to the long time series of global satellite soil moisture dataset reprocessed from SMOPS coarse resolution products recently, NESDIS scientists are generating a high spatial resolution satellite soil moisture data product for applications in the National Water Model that is operational at 1km spatial scale to river flow forecasts of thousands of basins in the United States. The presentation will describe the machine learning algorithms, satellite observations and ancillary data sets used for the 1km soil moisture data products of the National Water Model domain. Preliminary validation results and application examples will be demonstrated.
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