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| 夏季南海台风移动路径的一种客观预报方法 | |
| 引用本文： | 黄小燕,金龙,姚才,黄明策.夏季南海台风移动路径的一种客观预报方法[J].南京气象学院学报,2008,31(2):287-292. |
| 作者姓名： | 黄小燕 金龙 姚才 黄明策 |
| 作者单位： | 1. 南京信息工程大学,大气科学学院,江苏,南京,210044;广西区气象台,广西,南宁,530022 2. 广西气象减灾研究所,广西,南宁,530022 3. 广西区气象台,广西,南宁,530022 |
| 基金项目： | 广西科学研究与技术开发计划项目 |
| 摘 要： | 以1960—2003年共44a夏季的7月、8月、9月西行进入南海海域的台风样本为基础，综合考虑南海台风移动路径的气候持续因子和数值预报产品物理量因子，运用条件数方法选取因子并建立回归方程，进行台风路径预报模型的预报建模研究。通过对比分析发现，基于条件数方法的南海台风移动路径模型具有较好的预报效果，7月、8月、9月3个月24h台风路径预报的平均距离误差为153．9km，预报能力明显高于目前国内外的其他一些台风路径客观预报方法。该方法的预报精度相对于逐步回归方法有了很大的提高，相对于气候持续法也为正的预报技巧水平。 |
| 关 键 词： | 台风路径预报 条件数分析方法 逐步回归方法 气候持续法 |
| 文章编号： | 1000-2022(2008)02-0287-06 |
| 修稿时间： | 2005年12月5日 |
An Objective Technique for Forecasting Typhoon Motion over South China Sea in Summer |
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| HUANG Xiao-yan,JIN Long,YAO Cai,HUANG Ming-ce.An Objective Technique for Forecasting Typhoon Motion over South China Sea in Summer[J].Journal of Nanjing Institute of Meteorology,2008,31(2):287-292. | |
| Authors: | HUANG Xiao-yan JIN Long YAO Cai HUANG Ming-ce |
| Institution: | HUANG Xiao-yan, JIN Long, YAO Cai, HUANG Ming-ce ( 1. School of Atmospheric Sciences,NUIST,Nanjing 210044 ,China; 2. Guangxi Meteorological Observatory,Nanning 530022,China; 3. Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022, China) |
| Abstract: | Based on the samples of typhoons in July ,August and September from 1960 to 2003 over the South China Sea, multivariate regression prediction models of typhoon track for July, August and September developed by using the condition number method in selecting predictors from numerical prediction products and predictors used in the CLIPER model of typhoon track, and prediction experiments indicate that the average error of the three models is 153.9 km, which is smaller than those of objective prediction models currently used in China and western countries. Comparison experiments of new regression models with stepwise regression models and CLIPER models are performed under the conditions of identical modeling and independent samples and identical or almost identical predictor mumber, and results show that new regression models are superior to the stepwise regression models in prediction accuracy, and the forecast skill of new regression models relative to the CLIPER models is positive. |
| Keywords: | typhoon track prediction condition number method stepwise regression CLIPER models |
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