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首页> 外文期刊>Weather and forecasting >Statistical-Dynamical Typhoon Intensity Predictions in the Western North Pacific Using Track Pattern Clustering and Ocean Coupling Predictors
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Statistical-Dynamical Typhoon Intensity Predictions in the Western North Pacific Using Track Pattern Clustering and Ocean Coupling Predictors

机译：北太太平洋轨道模式聚类和海洋耦合预测因子统计动态台风强度预测

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摘要

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 2004-12 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 the initial time. To consider TC-ocean coupling effects due to TC-induced vertical mixing and resultant surface cooling, new potential predictors were also developed for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, 6 static, 11 synoptic, and 3 DAT-based potential predictors were used. Results from a series of experiments for the training period of 2004-12 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 nonclustering approach, the TPC and DAT-based predictors reduced prediction errors about 12%-25% between 24-and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 h) the present model has the smallest mean absolute errors. After a 24-h lead time, the present model still shows skill that is comparable with the best operational models.
机译：用于预测热带气旋（TC）强度的统计动态模型已经使用曲目模式聚类（TPC）方法和海洋耦合潜在的预测器来开发。基于模糊的C型聚类方法，西北太平洋西部2004 - 12年的TC轨道分为五个集群，调查了其独特的特征。预测模型使用多元线性回归，其中预测和依赖变量是相对于初始时间的最大风速的变化。考虑由于TC诱导的垂直混合引起的TC海洋耦合效应和所得到的表面冷却，还使用深度平均温度（DAT）而不是海表面来开发出用于最大潜在强度（MPI）和强化电位（POT）的新电位预测因子温度（SST）。使用6个静态，11个简易码和3个基于3个基于数据的潜在预测因子。使用TPC和基于数据的预测因子的一系列2004-12训练期的一系列实验显示出显着改善的TC强度预测。该模型对2013年和2014年的TC强度的预测进行了测试，这在训练样本中不使用。相对于非聚簇方法，TPC和基于DAT的预测器减少预测误差约为24至96-H递线之间的12％-25％。该模型也与四个操作动态预测模型进行了比较。在短线下（最多24小时）本模型具有最小的平均绝对误差。经过24小时的提前时间，目前模型仍然显示与最佳操作模型相当的技能。

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  • 来源
    《Weather and forecasting》 |2018年第1期|共19页
  • 作者

    Kim Sung-Hun; Moon Il-Ju; Chu Pao-Shin;

  • 作者单位

    Jeju Natl Univ Interdisciplinary Program Marine Meteorol Typhoon Res Ctr Jeju South Korea;

    Jeju Natl Univ Interdisciplinary Program Marine Meteorol Typhoon Res Ctr Jeju South Korea;

    Univ Hawaii Manoa Sch Ocean &

    Earth Sci &

    Technol Dept Atmospher Sci Honolulu HI 96822 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大气科学（气象学）;
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