Assimilation of Surface PM2.5 Observations in Rapid Refresh Forecast System-Smoke/Dust

The extent and frequency of wildfires are increasing across North America, especially in the western United States (U.S.) and Canada. Aerosols released from these fires can significantly impact air quality and visibility in the immediate vicinity of the fires and downwind across the continental U.S. and beyond. To help address these challenges, the High-Resolution Rapid Refresh-Smoke (HRRR-Smoke) model has been operational since 2020, predicting the three-dimensional transport of smoke from fires detected by satellite fire radiative power (FRP). The “Smoke” components not only provide additional hourly smoke predictions but also enhance fire weather forecasts by incorporating aerosol feedback into the radiation scheme. Currently, the NOAA Global Systems Laboratory (GSL) is developing its successor, the Rapid Refresh Forecast System-Smoke/Dust (RRFS-SD). This advanced modeling system features improved model physics, fire weather, and data assimilation capabilities and includes additional satellite-detected dust tracers. One of the key improvements is the integration of AirNow surface PM2.5 observations through the Joint Effort for Data assimilation Integration (JEDI) framework, aiming at refining initial conditions for more accurate smoke and dust forecasts. This presentation highlights these recent developments and presents some preliminary testing results from sensitivity studies of identifying smoke/dust-dominated PM2.5 observations to limit contributions from other sources and system tunings in retrospective runs.