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
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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.
Key wordscloud analysis radar data assimilation data assimilation interval
本文基于一次2007年04月23日发生于中国南部的飑线系统对雷达资料同化频率对同化和预报的影响进行了研究. 同化窗为飑线系统初生时2h, 其中雷达径向风通过三维变分, 反射率通过云分析同化, 云分析中分别使用单参数和双参数方案. 分别对10-, 20-, 30-和60-min同化间隔进行了敏感性试验. 结果显示, 分析场不满足预报模式动力平衡; 因此预报时动力和微物理变量, 同化后需要约20min重新建立动力平衡. 并且客观评分显示, 当使用20-min的同化间隔时, 无论单参数和双参数方案的预报能够更为准确地预报降水. 而更高频的雷达资料同化, 则会造成飑线系统中冷池, 后向气流过强, 从而降低预报质量.
关键词云分析 雷达资料同化 资料同化间隔
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This work was primarily 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).
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