Probabilistic forecasting of tropical cyclones intensity using machine learning model
In one sentence. Machine learning gives probabilistic tropical cyclone intensity forecasts with intervals, and its point forecasts outperform an operational statistical-dynamical model over 2010–2020.
Abstract
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.
Labels
- Topic
- Extremes & hazards, Probabilistic & ensemble forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1088/1748-9326/acc8eb OpenAlex:W4361264505
- Added
- 2026-10-22
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