Integrating Satellite Soil Moisture Retrievals for Hydrological Prediction Applications

Soil moisture is one of the critical land surface state variables that control the water, energy and even trace gas exchanges between land surface and the atmosphere. Numerical weather and hydrological prediction models require soil moisture data to initialize their forecasts. To meet the operational needs of near real time soil moisture observations, NOAA NESDIS has developed a Soil Moisture Operational Product System (SMOPS) that has become operational since 2013. The current Version 3.1 ingests observations from L-band sensors on NASA’s Soil Moisture Active/Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS), C-band Advanced Microwave Scanning Radiometer (AMSR2) on JAXA’s GCOM-W1 and Advanced Scatterometers (ASCAT-A and ASCAT-B) on EUMETSAT’s MetOp-A and B satellites. Soil moisture retrievals retrieved from near real time Level-1 brightness temperatures from SMAP, SMOS and AMSR2 have latency shorter than those official SMAP, SMOS or AMSR2 soil moisture products. The EUMETSAT official relative soil moisture retrievals from ASCAT-A and B are directly ingested into SMOPS and scaled to volumetric soil moisture before blended into those soil moisture retrievals from AMSR2, SMOS and SMAP. The blended SMOPS soil moisture data product has larger spatial coverage and is available to NCEP numerical weather prediction (NWP) users within 3-6 hours from acquisition time. In this presentation, the SMOPS soil moisture products are evaluated against in situ soil moisture measurements and other independent products such as European’s Climate Change Initiative soil moisture product. Intercomparison of SMOPS data with National Water Model (NWM) simulations indicated satellite retrievals contain independent information and have the potential to improve NWM performance. Preliminary results of assimilating of SMOPS product into NCEP NWP models indicated that SMOPS products are valuable for improving NCEP NWP model forecasts. This presentation will introduce the SMOPS data products and demonstrate their potentials to benefit NWP and NWM.