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气象:2019,45(1):29-37
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基于路径相似的登陆热带气旋降水之动力-统计集合预报模型
丁晨晨,任福民,邱文玉,李国平,吴立广,蒋贤玲
(成都信息工程大学,成都 610225;中国气象科学研究院灾害天气国家重点实验室,北京 100081;南京信息工程大学,南京 210044;中国科学院大气物理研究所,北京 100029)
A Dynamical Statistical Ensemble Forecast Model for Landfalling Tropical Cyclone Precipitation Based on Track Similarity
DING Chenchen,REN Fumin,QIU Wenyu,LI Guoping,WU Liguang,JIANG Xianling
(Chengdu University of Information Technology, Chengdu 610225;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;Nanjing University of Information and Technology, Nanjing 210044;Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029)
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投稿时间:2017-12-26    修订日期:2018-05-11
中文摘要: 数值天气预报 (NWP)过去几十年在热带气旋(TC)预报方面的最大进步是越来越准确的路径预报。对于登陆TC降水的预报,目前以数值模式为代表的技术手段预报能力还十分有限。围绕动力 统计结合之方法研究,初步发展了登陆热带气旋降水(LTP)预报的一种新方法:基于路径相似的登陆热带气旋降水之动力统计集合预报(LTP_DSEF)模型。该方法主要分为五步: TC路径预报、相似路径TC识别、其他特征相似性的判别、TC降水集合预报和最佳预报方案选择;涉及两个关键技术:TC降水分离的客观天气图分析法(OSAT)和TC路径相似面积指数(TSAI)。 LTP_DSEF模型对2012—2016年影响华南地区出现最大日降水量≥100 mm的21个TC的定量降水预报(QPF)试验结果显示,该模型对登陆TC过程降水的预报结果优于动力模式。登陆TC过程降水≥50 mm情况下,建模样本和独立样本平均TS评分均高于动力模式(EC、GFS、T639)相应的最好表现。对LTP_DSEF模型三个最佳方案的参数取值分析显示,起报时刻参数设定为最临近影响时刻即TC对陆地产生降水的前一天12:00 UTC、集合参数取最大值时预报效果稳定趋好。
Abstract:Track forecasting is the greatest progress over past decades in numerical weather prediction (NWP) regarding the tropical cyclones (TCs). But the landfalling TC precipitation prediction capability of NWP models is still very limited. Focusing on combining dynamical and statistical methods, a new method named track similarity based Landfalling Tropical cyclone Precipitation (LTP) Dynamical Statistical Ensemble Forecast (LTP_DSEF) model has been developed. The LTP_DSEF model includes five steps: TC track prediction, identification of analogue TC tracks, identification of other analogue TC characteristics, ensemble forecasting of TC precipitation, and the best scheme selection. There are two key technologies involved: the Objective Synoptic Analysis Technique (OSAT) for partitioning TC precipitation and the objective TC Track Similarity Area Index (TSAI). The application of LTP_DSEF model for 21 TCs with accumulated LTP ≥100 mm in South China during 2012-2016 reveals that the new method shows a better performance than the three NWP models (EC, GFS and T639) in predicting LTP. For ≥50 mm LTP forecasts, the threat score (TS) of LTP_DSEF models results are superior to those of the three NWP models. Analysis of the values of the LTP_DSEF models parameters for three best schemes shows that, the initial time designed to be the closest to the impact time, i.e., 1200 UTC on the previous day with precipitation on land and the ensemble parameter being the maximum are both beneficial to ideal forecasts.
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基金项目:国家自然科学基金面上项目(41375056、41675042)和厦门市科技计划项目(3502Z20174051)共同资助
引用文本:
丁晨晨,任福民,邱文玉,李国平,吴立广,蒋贤玲,2019.基于路径相似的登陆热带气旋降水之动力-统计集合预报模型[J].气象,45(1):29-37.
DING Chenchen,REN Fumin,QIU Wenyu,LI Guoping,WU Liguang,JIANG Xianling,2019.A Dynamical Statistical Ensemble Forecast Model for Landfalling Tropical Cyclone Precipitation Based on Track Similarity[J].Meteor Mon,45(1):29-37.