DC Field | Value | Language |
---|---|---|
dc.contributor.author | Son, Rackhun | ko |
dc.contributor.author | Ma, Po-Lun | ko |
dc.contributor.author | Wang, Hailong | ko |
dc.contributor.author | Rasch, Philp J. | ko |
dc.contributor.author | Wang, Shih-Yu (Simon) | ko |
dc.contributor.author | Kim, Hyungjun | ko |
dc.contributor.author | Jeong, Jee-Hoon | ko |
dc.contributor.author | Lim, Kyo-Sun Sunny | ko |
dc.contributor.author | Yoon, Jin-Ho | ko |
dc.date.accessioned | 2022-11-01T09:00:38Z | - |
dc.date.available | 2022-11-01T09:00:38Z | - |
dc.date.created | 2022-11-01 | - |
dc.date.created | 2022-11-01 | - |
dc.date.created | 2022-11-01 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, v.14, no.10 | - |
dc.identifier.issn | 1942-2466 | - |
dc.identifier.uri | http://hdl.handle.net/10203/299198 | - |
dc.description.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 (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. | - |
dc.language | English | - |
dc.publisher | AMER GEOPHYSICAL UNION | - |
dc.title | Deep Learning Provides Substantial Improvements to County-Level Fire Weather Forecasting Over the Western United States | - |
dc.type | Article | - |
dc.identifier.wosid | 000868680200001 | - |
dc.identifier.scopusid | 2-s2.0-85141680343 | - |
dc.type.rims | ART | - |
dc.citation.volume | 14 | - |
dc.citation.issue | 10 | - |
dc.citation.publicationname | JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS | - |
dc.identifier.doi | 10.1029/2022MS002995 | - |
dc.contributor.localauthor | Kim, Hyungjun | - |
dc.contributor.nonIdAuthor | Son, Rackhun | - |
dc.contributor.nonIdAuthor | Ma, Po-Lun | - |
dc.contributor.nonIdAuthor | Wang, Hailong | - |
dc.contributor.nonIdAuthor | Rasch, Philp J. | - |
dc.contributor.nonIdAuthor | Wang, Shih-Yu (Simon) | - |
dc.contributor.nonIdAuthor | Jeong, Jee-Hoon | - |
dc.contributor.nonIdAuthor | Lim, Kyo-Sun Sunny | - |
dc.contributor.nonIdAuthor | Yoon, Jin-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | wildfire | - |
dc.subject.keywordAuthor | fire weather prediction | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | bias correction | - |
dc.subject.keywordAuthor | downscaling | - |
dc.subject.keywordPlus | DANGER RATING SYSTEM | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | IMAGE | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.