Revisiting the role of SMAP soil moisture retrievals in WRF-Chem dust emission simulations over the western U.S.

Having good replication of the soil moisture evolution is desirable to properly simulate the dust emissions and atmospheric dust load because soil moisture increases the cohesive forces of soil particles, modulating the wind erosion threshold above which emissions occur. To reduce errors, one can use soil moisture retrievals from space-borne microwave radiometers. Here, we explore the potential of inserting soil moisture retrievals from the Soil Moisture Active Passive (SMAP) satellite into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to improve dust simulations. We focus our analysis on the contiguous U.S. due to the presence of important dust sources and good observational networks. Our analysis extends over the first year of SMAP retrievals (1 April 2015–31 March 2016) to cover the annual soil moisture variability and go beyond extreme events, such as dust storms, in order to provide a statistically robust characterization of the potential added value of the soil moisture retrievals. We focus on the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model from the Air Force Weather Agency (GOCART-AFWA) dust emission parameterization that represents soil moisture modulations of the wind erosion threshold with a parameterization developed by fitting observations. The dust emissions are overestimated by the GOCART-AFWA parameterization and result in an overestimation of the aerosol optical depth (AOD). Sensitivity experiments show that emissions reduced to 25% in the GOCART-AFWA simulations largely reduced the AOD bias over the Southwest and lead to better agreement with the standard WRF-Chem parameterization of dust emissions (GOCART) and with observations. Comparisons of GOCART-AFWA simulations with emissions reduced to 25% with and without SMAP soil moisture insertion show added value of the retrievals, albeit small, over the dust sources. These results highlight the importance of accurate dust emission parameterizations when evaluating the impact of remotely sensed soil moisture data on numerical weather prediction models.