As the climate continues to change and the frequency, severity, and impacts of wildfires increase, the importance of wildfire prediction and detection becomes even more critical. It then becomes essential to improve fire weather and smoke & air quality forecast accuracies in current operational models. This involves taking advantage of recent advancements in wind and lightning forecasts as well as improvements in land-surface (i.e. soil moisture, vegetation greenness/dryness, etc.) modeling. Additional improvements include enhancing the representation of other atmospheric states in models including temperature, moisture, etc.
This project is accelerating wildfire prediction, detection, and forecasting by:
- Improving detection and forecasts of conditions leading to the onset of fires by integrating land surface model states (i.e. vegetation and dryness), wind speeds, relative humidity & Planetary Boundary Layer (PBL) structure, and potential lighting from explicit convection into the Convection Allowing Model (CAM). This will improve Fire Weather Index (FWI) forecasts.
- Improving fire emissions after the onset of fires by integrating information from satellite-derived products into the CAM, improving plume-rise algorithm to determine injection heights, and developing Machine Learning (ML) algorithm to improve the diurnal (day-night) cycle of the Fire Radiative (FRP) power.
- Improving weather forecasts from wildfire smoke by (a) implementing additional wildfire-weather feedback mechanisms by adding the interaction with precipitation physics, (b) including all other relevant aerosol sources (dust, sea salt, and anthropogenic emissions) needed to further improve radiative properties and microphysics, and (c) introducing the assimilation of aerosol optical depth (AOD) in the CAM.