For typhoon warning centers, effective forecasting of tropical cyclone intensity is always required. The major difficulties and challenges in forecasting tropical cyclone intensity are the complex physical mechanism and the structure of tropical cyclones. The interaction between the tropical cyclone and its environment is also a complex process. In this paper, a model based on support vector machines is developed to yield the 12, 24, 36, 48, 72 h forecasts of tropical cyclone intensity. Furthermore, the forecasts resulting from the proposed model are compared with those from the Joint Typhoon Warning Center. Cross-validation tests are also applied to evaluate the accuracy and the robustness of the proposed model. The results confirm that the proposed model can provide accurate forecasts of tropical cyclone intensity, especially for a long lead-time. When the sample events are classified into five categories according to the Saffir-Simpson scale, the forecasts resulting from the proposed model have the best performance for events in categories 4 and 5. In addition, when a typhoon turns northward, although the water temperature drops rapidly, the proposed model still performs well. In conclusion, the proposed model is useful to improve the forecasts of tropical cyclones intensity.
Research Article|February 07 2013
Forecasting tropical cyclone intensity change in the western North Pacific
Gwo-Fong Lin, Po-Kai Huang, Hsuan-Yu Lin; Forecasting tropical cyclone intensity change in the western North Pacific. Journal of Hydroinformatics 1 July 2013; 15 (3): 952–966. doi: https://doi.org/10.2166/hydro.2013.155
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