Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China  

作　　者：Yujie PAN Mingjun WANG 

机构地区：[1]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education/Joint International Research Laboratory of Climate and Environment Change,Nanjing University of Information Science and Technology [2]Key Laboratory of Mesoscale Severe Weather/Ministry of Education and School of Atmospheric Sciences,Nanjing University

出　　处：《Advances in Atmospheric Sciences》2019年第2期160-172,共13页大气科学进展（英文版）

基　　金：supported by the National Key R&D Program of China (Grant No.2017YFC1502104);the National Natural Science Foundation of China (Grant Nos.41775099 and 41605026);Grant No.NJCAR2016ZD02,and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)

摘　　要：Assimilation configurations have significant impacts on analysis results and subsequent forecasts. A squall line system that occurred on 23 April 2007 over southern China was used to investigate the impacts of the data assimilation frequency of radar data on analyses and forecasts. A three-dimensional variational system was used to assimilate radial velocity data,and a cloud analysis system was used for reflectivity assimilation with a 2-h assimilation window covering the initial stage of the squall line. Two operators of radar reflectivity for cloud analyses corresponding to single-and double-moment schemes were used. In this study, we examined the sensitivity of assimilation frequency using 10-, 20-, 30-, and 60-min assimilation intervals. The results showed that analysis fields were not consistent with model dynamics and microphysics in general;thus, model states, including dynamic and microphysical variables, required approximately 20 min to reach a new balance after data assimilation in all experiments. Moreover, a 20-min data assimilation interval generally produced better forecasts for both single-and double-moment schemes in terms of equitable threat and bias scores. We conclude that a higher data assimilation frequency can produce a more intense cold pool and rear inflow jets but does not necessarily lead to a better forecast.Assimilation configurations have significant impacts on analysis results and subsequent forecasts. A squall line system that occurred on 23 April 2007 over southern China was used to investigate the impacts of the data assimilation frequency of radar data on analyses and forecasts. A three-dimensional variational system was used to assimilate radial velocity data,and a cloud analysis system was used for reflectivity assimilation with a 2-h assimilation window covering the initial stage of the squall line. Two operators of radar reflectivity for cloud analyses corresponding to single-and double-moment schemes were used. In this study, we examined the sensitivity of assimilation frequency using 10-, 20-, 30-, and 60-min assimilation intervals. The results showed that analysis fields were not consistent with model dynamics and microphysics in general;thus, model states, including dynamic and microphysical variables, required approximately 20 min to reach a new balance after data assimilation in all experiments. Moreover, a 20-min data assimilation interval generally produced better forecasts for both single-and double-moment schemes in terms of equitable threat and bias scores. We conclude that a higher data assimilation frequency can produce a more intense cold pool and rear inflow jets but does not necessarily lead to a better forecast.

关 键 词：CLOUD analysis radar DATA ASSIMILATION DATA ASSIMILATION INTERVAL 

分 类 号：O4[理学—物理]