Abstract An idealized framework for radar data assimilation has been developed based on the operational data assimilation system of the Deutscher Wetterdienst (DWD) for the regional convection-permitting model of COSMO (COnsortium for Small-scale MOdelling), coupled with an Efficient Modular VOlume scanning RADar Operator (EMVORADO). The data assimilation scheme is the Local Ensemble Transform Kalman Filter (LETKF). The idealized framework is used to explore differences between radial wind and reflectivity observations in storm-scale data assimilation by conducting a series of twin experiments. First, it is shown for both types of data that using the estimated observation errors obtained by the Desroziers method help to reduce the model state errors during cycles. Assimilating only radial winds results in smaller errors in wind components and assimilating reflectivities only results in smaller errors in temperature and microphysical variables, and the latter one requires a shorter spinup time to reconstruct precipitating systems. Both radial wind and reflectivity data are useful to reconstruct the dynamical structure of supercells described by the supercell detection index but radial wind data are more important. However, a considerable amount of spurious convective cells arise if only radial winds are assimilated. Assimilating reflectivities is able to efficiently reduce the spurious convection due to the assimilation of no reflectivity data. Additionally, it is shown that the data assimilation could cause significant biased increases in divergence, vorticity and total specific mass of microphysical variables. Assimilation of radial winds leads to lower increases in divergence and vorticity, while assimilation of reflectivities leads to lower increases in total specific mass. The amount of increase depends on the specification of the observation error statistics and on the number of assimilated data. The 6-h forecasts are skillful for both assimilating radial winds only and assimilating reflectivity only, while the latter one is even better since the skills of the former one are heavily penalized for spurious convection. Overall, radial wind and reflectivity data complement each other, assimilation of both data simultaneously results in the smallest state errors and the lowest biased increase in divergence, vorticity and total specific mass during cycles and subsequently the best 6-h forecasts.

中文翻译：

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在 COSMO-KENDA 系统的理想设置中同化雷达径向风和反射率数据

摘要 基于 Deutscher Wetterdienst (DWD) 的业务数据同化系统，为 COSMO（小规模建模联盟）的区域对流允许模型开发了一个理想的雷达数据同化框架，并结合了高效的模块化体积扫描雷达操作员 (EMVORADO)。数据同化方案是局部集成变换卡尔曼滤波器（LETKF）。理想化框架用于通过进行一系列双实验来探索风暴尺度数据同化中径向风和反射率观测之间的差异。首先，对于这两种类型的数据表明，使用由 Desroziers 方法获得的估计观测误差有助于减少循环期间的模型状态误差。仅同化径向风会导致风分量的误差较小，而同化反射率只会导致温度和微物理变量的误差较小，而后者需要更短的自旋时间来重建降水系统。径向风和反射率数据都有助于重建由超级单体检测指数描述的超级单体的动力学结构，但径向风数据更重要。但是，如果仅同化径向风，则会出现大量虚假对流单元。由于没有反射率数据的同化，吸收反射率能够有效地减少虚假对流。此外，数据同化可能导致差异显着增加，涡度和微物理变量的总比质量。径向风的同化导致散度和涡度的增加较少，而反射率的同化导致总比质量的增加较少。增加量取决于观测误差统计的规范和同化数据的数量。6 小时预报对于仅同化径向风和仅同化反射率都很有技巧，而后者则更好，因为前者的技巧因虚假对流而受到严重惩罚。总体而言，径向风和反射率数据相辅相成，同时同化这两种数据导致最小的状态误差和最低的发散偏差增加，