| 集合变分混合同化方案在快速循环同化系统中的应用研究 |
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| 引用本文： | 张涵斌,李玉焕,陈敏,冯琎,范水勇,沈海波.集合变分混合同化方案在快速循环同化系统中的应用研究[J].大气科学,2020,44(6):1349-1363. |
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| 作者姓名： | 张涵斌 李玉焕 陈敏 冯琎 范水勇 沈海波 |
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| 作者单位： | 1.北京城市气象研究院，北京 100089 |
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| 基金项目： | 国家重点研究发展计划项目2018YFC1506804，江苏省研究生科研创新计划项目KYCX18_1000 |
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| 摘 要： | 基于北京市气象局快速循环同化系统RMAPS-ST以及对流尺度集合预报系统RMAPS-EN，构建了En-3DVAR集合变分混合同化系统，将该系统应用到业务快速循环同化系统中并进行试验，分别在冷启动与循环启动环境下对比了混合同化系统（Hybrid）与三维变分（3DVAR）的同化预报效果。获得的结论如下:单点试验结果表明，混合同化系统分析增量的分布与集合预报离散度分布具有较好的对应关系；在冷启动和循环启动中，三维变分的分析增量都表现出各向同性的特点，混合同化分析增量均表现出一定的流依赖特征；降水个例分析表明，在冷启动环境中，Hybrid与3DVAR效果相当，而在循环启动中，Hybrid的降水预报相对于3DVAR有较明显的改进效果；批量试验检验结果表明，冷启动中，Hybrid与3DVAR的评分大致相当，而在循环启动中，Hybrid相对于3DVAR的评分有明显改进；集合离散度和背景场误差的相关性分析表明二者在循环启动环境下具有更好的相关性。
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| 关 键 词： | 集合变分混合同化 快速循环同化系统 区域集合预报 三维变分 |
| 收稿时间： | 2020-02-21 |
Implementation of Hybrid En-3DVAR Assimilation Scheme in Rapid Cycling Assimilation System |
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| Institution: | 1.Institute of Urban Meteorology, China Meteorological Administration, Beijing 1000892.Weather Online Institute of Meteorological Applications, Wuxi, Jiangsu 2140003.Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | A hybrid En-3DVAR (ensemble-three-dimensional variational data assimilation) was constructed based on the rapid refresh multi-scale analysis and prediction system–short term (RMAPS-ST) and the RMAPS-ensemble (RMAPS-EN) at Beijing Meteorological Bureau. The hybrid and 3DVAR schemes were conducted in the cold run and cycle run settings based on RMAPS-ST. The authors obtained the following conclusions: The single observation test showed that there was a good correspondence distribution between the hybrid data analysis (DA) increment and the ensemble spread. The hybrid DA-increment exhibited flow-dependent characteristics in both cold run and cycle run, but the 3DVAR DA-increment was isotropic. A rainfall case study revealed that the hybrid and 3DVAR had almost the same influence on the precipitation in the cold run setting, but the hybrid scores were significantly higher than those of the 3DVAR in the cycle run setting. The analysis of the ensemble spread and background error showed that they were better correlated in the cycle run than in the cold run. |
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