Assessing the Impacts of Assimilating SMAP Soil Moisture Retrievals on WRF-Chem Simulations of Dust Storm Events

Dust storms are hazardous to transportation safety and human health. Dust emissions are controlled by soil composition, vegetation fraction, and other faster-varying factors like wind speed and soil moisture (SM) content. Greater soil moisture content increases the cohesive forces of soil particles, making it more difficult to loft dust particles. Improving the SM representation in numerical weather prediction fully coupled with atmospheric chemistry models like WRF-Chem is therefore hypothesized to be an important factor to improving modeling of atmospheric dust aerosol loading.

To test this hypothesis, we selected eight dust storm events over the western U.S. For each case we ran two seven-day WRF-Chem simulations: in one simulation we assimilated SM content retrievals from the Soil Moisture Active Passive (SMAP) satellite into WRF-Chem via direct insertion (“Insert SMAP”), while the other simulation had no SM assimilation (“No SMAP”). The SM representation in WRF-Chem was generally improved in many locations in the western U.S. by assimilating SMAP retrievals. Additionally, the Insert SMAP simulations consistently had higher values of aerosol optical depth (AOD) due to increased atmospheric dust loading; in cases where WRF-Chem under-predicted dust loading, the AOD simulations were improved in these regions, but in the cases where WRF-Chem already over-predicted dust loading, the additional dust in Insert SMAP produced slightly worse simulations of AOD. Overall, adjusting the soil moisture representation in WRF-Chem using SMAP data had a relatively small impact on AOD during these dust storms, which points to other larger sources of model error, such as the erodibility input dataset or dust emission parameterization in WRF-Chem, that improved SM alone cannot resolve.

To explore whether assimilating SMAP SM retrievals adds more value if dust emission errors are reduced, we also tested scaling down the dust emissions across the entire domain by various factors in a series of one-year WRF-Chem simulations over the conterminous U.S. We found that while the magnitude of the impacts of assimilating SMAP data were reduced with reduced emissions (as expected), skill scores showed more consistent improvements to WRF-Chem AOD simulations when assimilating SMAP data with more accurate dust emissions.