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Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality

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A new machine learning model, Fire Intensity and spRead forecAst (FIRA), accurately forecasts wildfire spread and intensity. This improves air quality models by providing dynamic fire data, leading to better predictions of smoke impacts on public health.

Keywords:
monitoring, forecasting, predictionpollution: urban and regionalwildland fire model

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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Computer Science

Background:

  • Wildfires release hazardous pollutants, impacting air quality and public health.
  • Current air quality forecast (AQF) models often lack real-time fire spread dynamics, limiting prediction accuracy.

Purpose of the Study:

  • To develop a novel machine learning (ML) model, Fire Intensity and spRead forecAst (FIRA), for predicting wildfire spread and intensity.
  • To enhance AQF models by integrating dynamic fire characteristics like spatial distribution and fire radiative power (FRP).

Main Methods:

  • Developed the FIRA ML model using 2020 CONUS and historical California fire data.
  • Applied FIRA's FRP predictions as input to the Unified Forecast System coupled with smoke (UFS-Smoke) model.
  • Evaluated FIRA's performance against near-real-time fire products using a September 2020 California fire case.

Main Results:

  • FIRA demonstrated strong performance in capturing fire spread (R-squared ≈ 0.7) and spatial similarity (≈95%).
  • FIRA predictions showed good agreement with UFS-Smoke simulations, indicating accurate representation of future fire activities.
  • Scaled FIRA predictions significantly improved predictions of aerosol optical depth, 3D smoke content, and surface PM2.5 concentrations compared to the baseline UFS-Smoke model.

Conclusions:

  • The FIRA model offers a significant advancement in forecasting wildfire behavior for AQF applications.
  • Integrating FIRA into AQF systems can lead to more accurate predictions of wildfire smoke impacts on air quality and public health.
  • While FIRA may underestimate fire intensity, applying scaling factors effectively mitigates this uncertainty.