EOMF-24. Forecasting hourly wildfire emissions using a modulated persistence approach based on the RRFS hourly wildfire potential index

Abstract
As wildfires increase in frequency and severity due to anthropogenic climate change, smoke forecasting becomes fundamental to help minimize exposure to hazardous pollutants, improve weather forecasting, and guide wildfire-fighting operations. Most of the operational forecast assumes a persistence approach in which the latest satellite-based biomass burning emissions estimates remain constant during the forecast duration. However, recent work suggests that predictions can be improved by allowing emissions to covary with fire weather. NOAA's Global Systems Laboratory has developed a new weather forecast model (the Rapid-Refresh Forecasting System (RRFS)) with smoke emissions forecast capabilities from the operational High-Resolution Rapid Refresh coupled with the Smoke (HRRR-Smoke). This RRFS-SD model forecasts experimental weather and smoke at 3km resolution over the contiguous US (CONUS). Here, an hourly wildfire potential (HWP) index is used to modulate persistence-based forecasted biomass burning emissions estimates within the RRFS-SD model. The method is tested against persistence for two fire seasons, 2019, which coincides with the FIREX-AQ campaign and represents a low-intensity fire season, and 2020, characterized by severe fires in the western US. The model performance is evaluated using AERONET and AIRNOW data and in-situ measurements acquired during FIREX-AQ.