FireWx Forecasting Wildfire Emissions within NOAA's Rapid-Refresh Forecasting System Using a Modulated Persistence Approach

As wildfires increase in frequency and intensity due to changing climatic conditions, accurate and timely forecasting of wildfire smoke becomes crucial for reducing public health risks and enhancing weather predictions. Traditional smoke forecasting models often rely on a persistence approach, which uses the latest satellite-based estimates of biomass burning emissions, oversimplifying complex meteorological factors influencing the evolution of the smoke emissions over time. The only variable that accounts for the impact of weather on smoke emissions is the effect of precipitation, which is simplistically included in some of the models.
Here, we used the regional Rapid-Refresh Forecasting System coupled with smoke (RRFS-SD), incorporating an Hourly Wildfire Potential (HWP) index. This index integrates variables such as 10-meter wind gusts, downward solar radiation, surface dewpoint depression, precipitation, and soil moisture availability to predict the temporal evolution of wildfire emissions and fire radiative power (FRP). This new methodology, referred to as the modulated persistence approach, was evaluated during two fire periods: the 2019 fire season, aligned with the NOAA/NASA FIREX-AQ field campaign, and the 2020 fire season, characterized by a series of major wildfires across the western US. We demonstrate that using the HWP index to modulate the RRFS-SD wildfire emissions' diurnal variation improves the smoke forecast compared to the traditional persistence method.