An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data
An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data
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DOI：
10.1175/2007mwr2120.1
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发表时间：
2008-03
影响因子：
3.2
通讯作者：
Jidong Gao;M. Xue
中科院分区：
文献类型：
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作者：
Jidong Gao;M. Xue
基于集合卡尔曼滤波（EnKF）方法，提出了一种高效的双分辨率数据同化算法，并利用超级单体风暴的模拟雷达径向速度数据进行了验证。利用EnKF算法在高分辨率和低分辨率网格上同化雷达观测，并从低分辨率集合中估计出与流相关的背景误差协方差。结果表明，由此估计的流相关和动态演变的背景误差协方差在高分辨率网格上产生高质量的分析是有效的。DR方法的优点是能够显著降低EnKF分析的计算成本。在系统中，低分辨率集合提供了与气流相关的背景误差协方差，而单分辨率预报和分析提供了高分辨率的优势，这对解决雷暴内部结构具有重要意义。相对平滑的…
Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the co...