Satellite soil moisture products and their application to drought monitoring

Satellite soil moisture data products have been generated since more than a decade ago. However, none of these satellite soil moisture data products has been used operationally in numerical weather prediction models because of their accuracy or reliability issues. A climatologically consistent and qualitatively reliable global soil moisture product for NCEP Global Forecast System (GFS) has been generated from NOAA-NESDIS Soil Moisture Product System (SMOPS) recently. SMOPS scales the soil moisture data products from Soil Moisture Ocean Salinity (SMOS) satellite of European Space Agency, Advanced Scatterometer (ASCAT) on EUMETSAT's Metop-A and Metop-B satellites, AMSR2 of JAXA's GCOM-W1, GPM/GMI and SMAP of NASA to the climatology of the Noah land surface model of GFS, and merges them to a blended global soil moisture data product. Meanwhile, an Ensemble Kalman filter (EnKF) data assimilation algorithm is being implemented for numerical weather prediction models such as GFS and NUWRF to assimilate the satellite soil moisture data products via NASA's Land Information System (LIS). After a quick introduction of the production and validation of the soil moisture data products from SMOPS and their impact on GFS numerical weather prediction, this presentation will focus on using the satellite soil moisture data products in global and US drought monitoring. Based on the performance evaluated via the triple-colocation error model, anomalies of various soil moisture data products are merged to generate a blended drought index (BDI). Comparing with US Drought monitor and other drought indices, BDI is shown to perform fairly well and could be used as a reference information source for global and US drought monitoring