An Evaluation of Tropical Cyclone Genesis Forecast over the Western North Pacific and the South China Sea from the CMA-TRAMS

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外文摘要：Tropical cyclone(TC)genesis forecasting is essential for daily operational practices during the typhoon season.The updated version of the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS)offers forecasters reliable numerical weather prediction(NWP)products with improved configurations and fine resolution.While traditional evaluation of typhoon forecasts has focused on track and intensity,the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions.This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea.Based on previous research and typhoon observation data over five years,a set of localized,objective criteria has been proposed.The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts,pre-dicting 6 out of 22 TCs with a forecast lead time of up to 144 h.Additionally,over 80%of the total could be predicted 72 h in advance.The model also showed an average TC genesis position error of 218.3 km,comparable to the track errors of operational models according to the annual evaluation.The study also briefly investigated the forecast of Noul(2011).The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis,con-sistent with the analysis field.The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized three-dimensional structure of the typhoon.These results can enhance understanding of the mechanism behind typhoon genesis,fine-tune model configurations and dynamical frameworks,and provide reliable forecasts for forecasters.

外文关键词：

CMA-TRAMScyclogenesisnumerical weather predictiontropical cyclone

作者：

李梦婕、陈子通、戴光丰、田群、梁卓轩、林青、张艳霞

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作者单位：

Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA,Guangzhou 510641 China

State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081 China

Guangdong Meteorological Observatory,Guangzhou 510641 China

基金：

Science and Technology Innovation Project of Guangdong Provincial Water Resources Department广东省科技计划China Meteorological Administration Key Innovation Team of Tropical MeteorologyOpen Research Program of the State Key Laboratory of Severe Weather

项目编号：

2022-012022A1515011870CMA2023ZD082022LASW-B18

出版年：

2024

热带气象学报(英文版)

中国气象局广州热带海洋气象研究所

热带气象学报(英文版)

影响因子：0.169

ISSN：1006-8775

年,卷(期)：2024.30(1)

参考文献量33