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一种面向工程应用的台风强度统计预测方法
引用本文:文元桥,蔡永庆,耿晓巧.一种面向工程应用的台风强度统计预测方法[J].河海大学学报(自然科学版),2016,44(2):172-177.
作者姓名:文元桥  蔡永庆  耿晓巧
作者单位:武汉理工大学航运学院, 湖北 武汉 430063; 湖北省内河航运技术重点实验室, 湖北 武汉 430063,武汉理工大学航运学院, 湖北 武汉 430063; 湖北省内河航运技术重点实验室, 湖北 武汉 430063,武汉理工大学航运学院, 湖北 武汉 430063; 湖北省内河航运技术重点实验室, 湖北 武汉 430063
摘    要:为实现台风强度的年变化预测,提出了一种基于灰色均生函数模型的预测方法。选取最大风速作为台风强度的指标,利用中国气象局发布的西北太平洋台风资料,建立台风登陆和未登陆情况下的预测模型,做5 a预测,并与观测结果对比。结果表明:灰色均生函数模型对台风强度的预测具有较高精度,年平均值最大绝对误差为4.4 m/s,低于2012年各站25次台风强度的数值模式预测结果平均值;年极值最大绝对误差为11.1 m/s,低于上海台风模式对台风个例的预报结果。

关 键 词:工程应用  灰色均生函数模型  台风强度预测  最大风速  连续时间序列预测  西北太平洋海域
收稿时间:2015/3/9 0:00:00

Typhoon intensity statistical prediction method oriented to engineering application
WEN Yuanqiao,CAI Yongqing and GENG Xiaoqiao.Typhoon intensity statistical prediction method oriented to engineering application[J].Journal of Hohai University (Natural Sciences ),2016,44(2):172-177.
Authors:WEN Yuanqiao  CAI Yongqing and GENG Xiaoqiao
Institution:School of Navigation, Wuhan University of Technology, Wuhan 430063, China; Hubei Inland Shipping Technology Key Laboratory, Wuhan 430063, China,School of Navigation, Wuhan University of Technology, Wuhan 430063, China; Hubei Inland Shipping Technology Key Laboratory, Wuhan 430063, China and School of Navigation, Wuhan University of Technology, Wuhan 430063, China; Hubei Inland Shipping Technology Key Laboratory, Wuhan 430063, China
Abstract:In order to predict the annual variation of the typhoon intensity, a prediction method based on the grey-mean generating function model is proposed. Selecting the maximum wind velocity as the index of typhoon intensity, the prediction models for typhoons arriving or not arriving at coasts are established using data from a Northwest Pacific Ocean typhoon published by the China Meteorological Administration. A five-year prediction was made, and a comparison was made between the predicted results and the observed ones. The results show that the grey-mean generating function model has high accuracy for the prediction of the typhoon intensity. The maximum absolute error of the predicted annual average value is 4. 4 m/s, lower than the average absolute error of forecast results regarding the intensity of 25 typhoons in 2012 obtained from the numerical model. The maximum absolute error of the predicted annual extreme value is 11. 1 m/s, lower than the absolute error of the predicted value obtained from the Shanghai typhoon model.
Keywords:engineering application  grey-mean generating function model  typhoon intensity prediction  maximum wind speed  continuous time series prediction  Northwest Pacific Ocean
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