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Technical Note on a track-pattern-based model for Predicting Seasonal Tropical Cyclone Activity over the Western North Pacific

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Advances in Atmospheric Sciences 2013年第5期30卷 1260-1274页

作者： Chang-Hoi HO Joo-Hong KIM Hyeong-Seog KIM Woosuk CHOI Min-Hee LEE Hee-Dong YOO Tae-Ryong KIM Sangwook PARK School of Earth and Environmental Sciences Seoul National University Korea Polar Research Institute Atmospheric and Oceanic Science Princeton University and NOAA/GFDL Forecast Policy Division Korea Meteorological Administration National Typhoon Center Korea Meteorological Administration

Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.

关键词： tropical cyclone western North Pacific seasonal forecast track-pattern-based model hybrid statistical-dynamical approach

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