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Current Capability of Operational Numerical Models in Predicting Tropical Cyclone Intensity in the Western North Pacific

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  • 1 Shanghai Typhoon Institute, CMA, Shanghai, China
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Abstract

Forecasts of tropical cyclone (TC) intensity from six operational models (three global models and three regional models) during 2010 and 2011 are assessed to study the current capability of model guidance in the western North Pacific. The evaluation is performed on both Vmax and Pmin from several aspects, including the relative error, skill assessment, category score, the hitting rate of trend, and so on. It is encouraging to see that the models have had some skills in the prediction of TC intensity, including that two of them are better than a statistical baseline in Vmax at several lead times and three of them show some skill in intensity change. With dissipated cases included, all the models have skills in category and trend forecasting at lead times longer than 24 h or so. The model forecast errors are found to be significantly correlated with initial error and the observed initial intensity. A statistical calibration scheme for model forecasting is proposed based on such an attribute, which is more effective for Pmin than Vmax. The statistically calibrated model forecasts are important in setting up a skillful multimodel consensus, for either the mean or the statistically weighted mean. The Vmax forecasts converted from the calibrated Pmin consensus based on a statistical wind–pressure relationship show significant skill over the baseline and a skillful scheme is also proposed to deal with the delay of the model forecasts in operation.

Corresponding author address: Hui Yu, Shanghai Typhoon Institute, CMA, 166 Puxi Rd., Xuhui District, Shanghai 200030, China. E-mail: yuh@mail.typhoon.gov.cn

Abstract

Forecasts of tropical cyclone (TC) intensity from six operational models (three global models and three regional models) during 2010 and 2011 are assessed to study the current capability of model guidance in the western North Pacific. The evaluation is performed on both Vmax and Pmin from several aspects, including the relative error, skill assessment, category score, the hitting rate of trend, and so on. It is encouraging to see that the models have had some skills in the prediction of TC intensity, including that two of them are better than a statistical baseline in Vmax at several lead times and three of them show some skill in intensity change. With dissipated cases included, all the models have skills in category and trend forecasting at lead times longer than 24 h or so. The model forecast errors are found to be significantly correlated with initial error and the observed initial intensity. A statistical calibration scheme for model forecasting is proposed based on such an attribute, which is more effective for Pmin than Vmax. The statistically calibrated model forecasts are important in setting up a skillful multimodel consensus, for either the mean or the statistically weighted mean. The Vmax forecasts converted from the calibrated Pmin consensus based on a statistical wind–pressure relationship show significant skill over the baseline and a skillful scheme is also proposed to deal with the delay of the model forecasts in operation.

Corresponding author address: Hui Yu, Shanghai Typhoon Institute, CMA, 166 Puxi Rd., Xuhui District, Shanghai 200030, China. E-mail: yuh@mail.typhoon.gov.cn
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