PNNL

Abstract

The recent wildfires in the western United States during 2018 and 2020 caused record-breaking fire damage and casualties. Despite remarkable advances in fire modeling and weather forecasting, it remains challenging to anticipate catastrophic wildfire events and associated damage. One key missing component is a fire weather prediction system with sufficiently long lead time capable of providing useful regional details. Here, we develop a hybrid prediction model of wildfire danger called CFS with super resolution (CFS-SR) as a proof of concept to fill that void. The CFS-SR model is constructed by integrating the Climate Forecast System version 2 with a deep learning technique from Single Image Super Resolution, a method widely used in enhancing image resolution. We show that for the 2018-2019 fire season, the CFS-SR model significantly improves accuracy in forecasting fire weather at lead times of up to 7 days with an enhanced spatial resolution up to 4 km. This level of high resolution provides county-level fire weather forecast, making it more practical for allocating resources to mitigate wildfire danger. Our study demonstrates that a proper combination of ensemble climate predictions with deep learning techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.