Deep Learning Provides Substantial Improvements to County-Level Fire Weather Forecasting Over the Western United States

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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 (DL) 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 DL techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.
Publisher
AMER GEOPHYSICAL UNION
Issue Date
2022-10
Language
English
Article Type
Article
Citation

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, v.14, no.10

ISSN
1942-2466
DOI
10.1029/2022MS002995
URI
http://hdl.handle.net/10203/299198
Appears in Collection
RIMS Journal Papers
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