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

Author:

Son Rackhun12ORCID,Ma Po‐Lun3ORCID,Wang Hailong3ORCID,Rasch Philp J.34ORCID,Wang Shih‐Yu (Simon)5ORCID,Kim Hyungjun678ORCID,Jeong Jee‐Hoon9ORCID,Lim Kyo‐Sun Sunny10ORCID,Yoon Jin‐Ho2ORCID

Affiliation:

1. Department of Biogeochemical Integration Max Planck Institute for Biogeochemistry Jena Germany

2. School of Earth Sciences and Environmental Engineering Gwangju Institute of Science and Technology Gwangju Korea

3. Pacific Northwest National Laboratory Richland WA USA

4. Department of Atmospheric Science University of Washington Seattle WA USA

5. Department of Plants, Soils, and Climate Utah State University Logan UT USA

6. Moon Soul Graduate School of Future Strategy Korea Advanced Institute of Science and Technology Daejeon Korea

7. Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology Daejeon Korea

8. Institute of Industrial Science The University of Tokyo Tokyo Japan

9. Faculty of Earth and Environmental Sciences Chonnam National University Gwangju Korea

10. Department of Atmospheric Sciences, Center for Atmospheric REmote sensing (CARE) Kyungpook National University Daegu Korea

Funder

Biological and Environmental Research

National Research Foundation of Korea

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model;Journal of Advances in Modeling Earth Systems;2024-01

2. Developing novel machine-learning-based fire weather indices;Machine Learning: Science and Technology;2023-03-01

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