This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.

The work addresses the need for reliable precipitation forecasts in hyper-arid environments through state-of-the-art hydro-meteorological modeling. Accounting for land–atmosphere interactions in the applied model is shown to improve the accuracy of precipitation output. The chain of events controlling the soil moisture–precipitation feedback are diagnosed and verified by in situ observations and satellite data.

Various sensitivity simulations during two airborne campaigns were performed to assess the impact of different initialization methods and model resolutions on NUWRF-modeled weather states, heat fluxes, and the follow-on MEGAN isoprene emission calculations. Proper land initialization is shown to be important to the coupled weather modeling and the follow-on emission modeling, which is also critical to accurately representing other processes in air quality modeling and data assimilation.