WCD-37. Assimilation of Surface Particulate Matter Observations in the experimental Rapid Refresh Forecast System coupled with Smoke and Dust Model

Abstract
Smoke prediction plays a key role in air quality forecasts during wildfire events. Many factors, such as fire emission sources, plumerise schemes and initial conditions of meteorological fields and smoke particulate matter concentrations will affect the prediction skill of wildfire smoke. In this presentation, we will describe the recent development of surface Particulate Matter (PM) data assimilation scheme for providing accurate smoke initial condition to the Rapid Refresh Forecast System (RRFS) Smoke and Dust model (RRFS-SD) and evaluate the impact of the developed PM data assimilation scheme on RRFS-SD smoke prediction. Results from retrospective runs of the heavy fire events taking place in the US during September 2020 will be presented.