A Sub-seasonal Prediction Method and Performance Evaluation for the Northwest Pacific Tropical Cyclone Genesis and Tracks

Using the tropical cyclone (TC) data of reforecast experiments from 11 dynamical models in the World Climate Research Program and World Weather Research Program sub-seasonal to seasonal prediction project dataset, this study evaluated skills of 11 dynamical models in predicting the TC genesis and track on sub-seasonal time scale over the Western North Pacific, which were compared to a statistical model that was developed by regularized logistic regression. The performances of dynamical models to predict the TC activity at the climatic, interannual and sub-seasonal time scales on forecast skills were analyzed in this study. Results show that: (1) The performance of dynamical model to predict the climatological seasonal cycle of TC activity over the Western North Pacific has a critical impact on sub-seasonal forecast skills. If the dynamical model can well reproduce TC activity at the climatic and interannual time scales, there is an expected skill improvement of TC genesis and track on sub-seasonal time scale by improving model’s ability to forecast intra-seasonal oscillation modulation on TC activity. (2) Most of dynamical models have better skill in TC track prediction than cyclogenesis at sub-seasonal time scale, reflecting the lower skill of predicted TC intensity in models. The lower skill of cyclogenesis restricts the improvement of TC track prediction. Better prediction of climatic and interannual cyclogenesis is potentially conducive to the improvement in TC track prediction of dynamic models.