Impact of SMAP Soil Moisture Assimilation on Numerical Weather Forecasts over the Contiguous United States and and East Africa

The NASA Short-Term Prediction Research and Transition (SPoRT) Center is assimilating passive microwave retrievals (Level 2 Enhanced) from the Soil Moisture Active Passive (SMAP) satellite into the Noah land surface model within the Land Information System (LIS). We have previously demonstrated the ability of SMAP assimilation to correct erroneous spatial patterns in near-surface soil moisture distribution. Here, we examine the impact of the soil moisture field changes on numerical weather forecasts initialized with LIS soil moisture fields.

Surface soil moisture and temperature fields were obtained from Noah land surface model simulations, performed with and without SMAP assimilation for two domains—Contiguous US (CONUS) and East Africa. These are used to initialize 48-h forecasts in parallel Weather Research and Forecasting (WRF) model runs. The CONUS LIS run is forced by North American Land Data Assimilation Phase 2 (NLDAS-2) analyses and covers the warm seasons of 2015 and 2016. The East Africa simulation is forced by the Global Data Assimilation System (GDAS) analyses and Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) precipitation, and focuses on the March-April and October-November rainy season transitions in Kenya and surrounding nations. Qualitative analysis is performed by examining case studies of significant convective events to compare forecast precipitation to radar observations. Preliminary results indicate improvements in the timing and placement of a mesoscale convective system over the U.S. Midwest and convective evolution during a U.S. Southern Plains tornado outbreak. Quantitative results will also be presented for aggregate verification statistics of forecast precipitation and near-surface temperature and dewpoint validated against precipitation analyses and meteorological in situ observations.