Probabilistic forecasting of tropical cyclones intensity using machine learning model

Abstract This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation of this uncertainty should be provided....

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Bibliographic Details
Published in:Environmental research letters 2023-04, Vol.18 (4), p.44042
Main Authors: Meng, Fan, Yao, Yichen, Wang, Zhibin, Peng, Shiqiu, Xu, Danya, Song, Tao
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer applications
Cyclones
Dynamic models
Forecasting
Learning algorithms
Machine learning
natural hazards
probabilistic forecasting
probabilistic machine learning
Statistical analysis
Statistical methods
tropical cyclone
Tropical cyclones
Uncertainty
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Summary:Abstract This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation of this uncertainty should be provided. Previous studies construct this uncertainty through ensemble or statistical methods, neither of which can directly characterize this uncertainty and suffer from problems such as excessive computational effort. And for this reason, we propose to assess the forecast without this uncertainty through the forecast distribution. Meanwhile, none of the previous studies on TC intensity forecasting by artificial intelligence methods characterize the uncertainty, so this study is a new supplement to data-driven TC forecasting. During the 2010–2020 evaluation period, the model’s point forecast can outperform the current state-of-the-art operational statistic-dynamical model results, and can obtain forecast intervals to provide reliable probabilistic forecasts, which are critical for disaster warnings.
ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/acc8eb