Development of a Data-Driven Machine-Learning Based Aeolian Threshold Friction Velocity with Applications to the NOAA FENGSHA Dust Emission Model in the Rapid Refresh Forecast System with Smoke and Dust (RRFS-SD) and the National Air Quality Forecast Capability (NAQFC)

Aerosols have both direct and indirect effects on meteorology, atmospheric chemistry, human health, and ultimately the global energy budget with dust being a major contributor to the atmospheric aerosol burden. A great effort has been made to characterize the sources and mobilization of dust. However, current models still show large uncertainty as the modeling of mineral dust in the atmosphere is complex. One of the key parameters to model aeolian emissions within weather, climate, and air quality models is the threshold friction velocity (TFV). While in many models the TFV is determined by knowledge of soil size distributions and characteristics often assumed on a global scale, the FENGSHA dust emission model was originally constrained by observations following the works of Dale Gillette (1980, 1982, 1988). In this way, FENGSHA is a data-driven model, constrained by observations of saltation with respect to soil characteristics. In this study, a new way to determine the TFV is developed through a machine learning (ML) technique constrained by observations and high resolution soil characteristics. We first use observed dust points detected by satellites compiled in Hennen et al. (2022), soil parameters from SOILGRIDS 2.0 or BNU, and ERA5 Land reanalysis data to generate a ML model of the TFV based solely on the soil inputs, including composition and physical characteristics. The ML model results compare well with observations from Gillette and provide a robust and high resolution method to accurately describe the saltation velocity in both time and scale. Next, we apply this TFV within the Unified Forecast System via NOAA’s Rapid Refresh Forecast System with Smoke and Dust (RRFS-SD) [https://rapidrefresh.noaa.gov/RRFS-SD/] and within the suite of UFS applications from regional to global scale. Retrospective simulations are compared with surface and satellite observations during a recent dust event.