报告题目： Statistical-dynamical typhoon intensity predictions in the western North Pacific using track pattern clustering and ocean coupling predictors

报告人：IL-JU MOON  教授，Jeju National University, Jeju, South Korea

邀请人：Jongsuk KIM (中文名金钟硕) 教授

时间：2018年12月11日（星期二）下午3:30

地点：国家重点实验室学术报告厅（农水楼一楼）

报告人简介：

-       The Head of Center, Typhoon Research Center (2014-Present)

-       Professor, College of Ocean Science, Jeju National University, South Korea (2005-Present)

-       Marine Research Associate, Graduate School of Oceanography, University of Rhode Island (2003-2005)

-       Postdoctoral Research Scientist, Graduate School of Oceanography, University of Rhode Island (2000-2003)

-       Ph.D. degree, Physical Oceanography, Seoul National University, Seoul Korea (2000)

-       Master degree, Physical Oceanography, Seoul National University, Seoul Korea (1994)

-       Bachelor’s degree, Oceanography, Pusan National University, Pusan Korea (1991)

He published more than 50 articles on typhoon-ocean interactions, coupled hurricane-wave-ocean modeling, climate change and tropical cyclone activity in Nature, Journal of Geophysical Research, and International Journal of Climatology, etc.

报告简介：

A statistical-dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004-2012 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to initial time. To consider TC-ocean coupling effects due to TC induced vertical mixing and resultant surface cooling, we also developed new potential predictors for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, we used six static, 11 synoptic, and three DAT-based potential predictors. Results from a series of experiments for the training period of 2004 - 2012 using TPC and DAT-based predictors showed remarkably improved TC intensity predictions. The model was tested on predictions of TC intensity for 2013 and 2014, which are not used in the training samples. Relative to the non-clustering approach, the TPC and DAT-based predictors reduced prediction errors about 12-25% between 24-h and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 hours) the present model has the smallest mean absolute errors. After a 24-hour lead time, the present model still shows the skill that is comparable with the best operational models.

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