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Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional‐to‐global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.

Improving the simulation of convective dust storms in regional‐to‐global models