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Improved Tropical Cyclone Forecasts with Increased Vertical Resolution in the TRAMS Model 被引量:1

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摘要 The numerical simulation of typhoons has been found to be very sensitive to the vertical resolution of the model.During the updating of the TRAMS model from version 1.0 to 3.0,the horizontal resolution has been increased from 36 km to 9 km,while the vertical layer number only increased from 55 to 65 layers.The lack of high vertical resolution limits the performance of the TRAMS model in typhoon forecasting to a certain extent.In order to study the potential improvement of typhoon forecasting by increasing the vertical resolution,this paper increases the vertical resolution of the TRAMS model from 65 to 125 layers for the first time for a comparative simulation test.The results of the case study with Typhoon Hato(2017)show that the model with high vertical resolution can significantly enhance the warm structure caused by water vapor flux convergence and vertical transport,thus accurately simulating the rapid strengthening process of the typhoon.Meanwhile,the model with 125-layer vertical resolution can simulate the asymmetric structural characteristics of the wind field,which are closer to the observations and can help to reduce the bias in typhoon track forecasting.The improvement of vertical resolution is also trialed by using the batch test results of several landfalling typhoons in 2016-2017.The experimental results show that the typhoon forecast of the model becomes consistent with the observations only when the number of vertical layers of the model increases to about 125 layers,which in turn causes a large computational burden.In the next step,we will try to solve the computational burden problem caused by ultra-high vertical resolution with the top boundary nesting technique,and realize the application of high vertical resolution in the actual operation of the TRAMS model.
作者 徐道生 梁家豪 卢泽彬 张艳霞 黄菲 冯业荣 张邦林 XU Dao-sheng;LIANG Jia-hao;LU Ze-bin;ZHANG Yan-xia;HUANG Fei;FENG Ye-rong;ZHANG Bang-lin(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;College of Ocean and Meteorology,Guangdong Ocean University,Zhanjiang,Guangdong 524088 China;Zhuhai Meteorological Bureau,Zhuhai,Guangdong 519099 China)
出处 《Journal of Tropical Meteorology》 SCIE 2022年第4期377-387,共11页 热带气象学报(英文版)
基金 National Natural Science Foundation of China(U2142213) Open Grants of the State Key Laboratory of Severe Weather(2022LASW-B16) Guangdong Basic and Applied Basic Research Foundation(2022A1515011870) Special Program for Innovation and Development of China Meteorological Administration(CXFZ2021Z006,CXFZ2022P026)。
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