Using deep learning to improve multi-day forecasts of extreme fire weather and behaviour throughout the United States
Authors: Lagerquist Ryan , Kumler Christina 
Published: March 2024

The ignition, spread, and severity of wildfires are driven largely by weather conditions (Jain et al. 2020: https://doi.org/10.1139/er-2020-0019; Liu et al. 2013: https://doi.org/10.1371/journal.pone.0055618). &#160;The main tool for weather prediction across the globe is a set of physical, coupled atmosphere/ocean models, called numerical weather prediction (NWP). &#160;Despite rapid improvements in the last few decades, NWP alone is not sufficient for wildfire prediction, because it does not resolve every process related to wildfire. &#160;One solution is to post-process NWP with statistical models, which correct the NWP model towards better resolving processes related to the phenomenon of interest (here, wildfire). &#160;This post-processing is called model-output statistics (MOS) and typically involves linear regression. &#160;However, recent work has advanced MOS by incorporating more powerful statistical models from deep learning (DL). &#160;We use DL to predict extreme fire weather and behaviour at multi-day lead times throughout the United States.&#160;For fire weather, we have trained U-nets -- a type of deep neural network -- to predict at lead times of 3-240 hours over the United States. &#160;The output (target) variables are seven indices from the Canadian Fire Weather Index System (CFWIS), computed from the ECMWF Reanalysis version 5 (ERA5). &#160;These seven indices include the fine-fuel moisture code (FFMC), initial-spread index (ISI), overall fire-weather index (FWI), etc. &#160;Meanwhile, the input (predictor) variables come from five sources. &#160;The first is a forecast time series of atmospheric state variables (height, temperature, humidity, and wind) from the Global Forecast System (GFS) NWP model. &#160;The second is a forecast time series of surface and subsurface moisture (soil moisture, accumulated precipitation, and snow depth) from the GFS. &#160;The third is a set of constant fields (terrain height/slope/aspect, land-sea mask, etc.) describing the underlying terrain. &#160;The fourth is a lagged time series of CFWIS over the past several days, i.e., past target values. &#160;The fifth is a forecast time series of CFWIS indices, computed by applying the CFWIS functions directly to GFS-forecast weather variables. &#160;These are the uncorrected (GFS-only) CFWIS forecasts, to be corrected by the U-net.&#160;For fire behaviour, we have trained random forests -- ensembles of decision trees -- to predict fire radiative power (FRP) at lead times of 1-48 hours over the United States. &#160;The labels (correct answers) for FRP are obtained from the Regional ABI and VIIRS Emissions (RAVE) merged satellite product. &#160;Predictors for the random forest include the first three sources listed for the U-net above, plus a lagged time series of FRP over the past 24 hours, i.e., past target values.&#160;Both models -- the U-net for fire weather and the random forest for fire behaviour -- are trained with built-in uncertainty quantification. &#160;Thus, at every lead time and grid point, both models provide an expected value and an estimate of their own uncertainty. &#160;We will present objective evaluation results (for both the mean forecast and uncertainty) and explainable artificial intelligence (XAI) to understand what the models have learned, e.g., which spatiotemporal weather patterns in a given area are most conducive to extreme fire weather/behaviour.