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Abstract
This study compares five global machine learning-based weather prediction (MLWP) models (Pangu-Weather, FourCastNet v2, GraphCast, FuXi, and FengWu) using identical ERA5 initial conditions in Eastern Asia and the Western Pacific from June to November 2023. FengWu showed the best performance, followed by FuXi and GraphCast, with FCN2 and Pangu-Weather ranking lower. A multi-model ensemble improved performance, rivaling FengWu. FengWu had the most accurate typhoon track predictions but the largest intensity errors.
Publisher
npj Climate and Atmospheric Science
Published On
Sep 28, 2024
Authors
Cheng-Chin Liu, Kathryn Hsu, Melinda S. Peng, Der-Song Chen, Pao-Liang Chang, Ling-Feng Hsiao, Chin-Tzu Fong, Jing-Shan Hong, Chia-Ping Cheng, Kuo-Chen Lu, Chia-Rong Chen, Hung-Chi Kuo
Tags
machine learning
weather prediction
typhoon tracking
ensemble modeling
Eastern Asia

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