Impact of direct insertion of SMAP soil moisture retrievals in WRF-Chem for dust storm events in the western U.S.

Dust storms negatively impact human health, and also lead to vehicle crashes due to reduced visibility. Improving our ability to model and predict the location, timing, and intensity of dust storms would therefore benefit society. The amount of dust emissions is controlled by soil composition, vegetation fraction, and other faster-varying factors like wind speed and soil moisture content. Greater soil moisture content increases the cohesive forces of soil particles, which in turn makes it more difficult for a given wind speed to loft dust particles in sufficient quantities to create a dust storm. Thus, improving the soil moisture representation in numerical weather prediction fully coupled with atmospheric chemistry models like WRF-Chem is hypothesized to be an important factor to improving predictions of dust aerosol content in the atmosphere. To test this hypothesis, we identified eight cases of dust storms over the western U.S. from 2015–2021. For each case we ran two seven-day WRF-Chem simulations: in one simulation we directly inserted soil moisture content retrievals from the Soil Moisture Active Passive (SMAP) satellite into WRF-Chem ("Insert SMAP"), while the other simulation had no soil moisture adjustments ("No SMAP"). The soil moisture representation in WRF was generally improved in many locations in the southwestern U.S. by directly inserting 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 lingering, larger sources of model error, such as the erodibility input dataset or dust emission parameterization in WRF-Chem, that improved soil moisture alone cannot resolve.