A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models

NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31
^st
, 2015 and has been providing global soil moisture (SM) data products since April 2015. One of the primary justifications of the mission was to improve numerical weather predictions. With the SMAP SM data becoming available, it is anxiously expected that SMAP SM data could be demonstrated to significantly improve weather forecasts from numerical weather prediction (NWP) models. In this study, the NOAA Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF) model coupled with NASA Land Information System are used to carry out the demonstration. A hardwired Ensemble Kalman filter is implemented within GFS to assimilate surface SM observations. For assimilating SM data into NUWRF model, NASA Land Information System (LIS) is coupled with the NUWRF model. In this paper preliminary results of SMAP soil moisture impact on GFS and NUWRF forecasts are presented after the assimilation algorithms and system designs are introduced. Plans for more comprehensive assessment of the satellite soil moisture data impact on NWP models will be discussed.