摘要
The nonlinear least-squares four-dimensional variational assimilation(NLS-4DVar)method intro-duced here combines the merits of the ensemble Kalman lter and 4DVar assimilation methods.The multigrid NLS-4DVar method can be implemented without adjoint models and also corrects small-to large-scale errors with greater accuracy.In this paper,the multigrid NLS-4DVar method is used in radar radial velocity data assimilations.Observing system simulation experiments were conducted to determine the capability and efficiency of multigrid NLS-4DVar for assimilating radar radial velocity with WRF-ARW(the Advanced Research Weather Research and Forecasting model).The results show signi cant improvement in 24-h cumulative precipitation prediction due to improved initial conditions after assimilating the radar radial velocity.Additionally,the multigrid NLS-4DVar method reduces computational cost.
非线性最小二乘法的集合四维变分同化方法是一种结合了集合卡尔曼滤波和四维变分同化优势的混合同化方法。引入多重网格策略的NLS-4DVar方法不仅可以避免使用伴随模式,而且可以从大尺度到小尺度依次修正误差得到精度更高的分析场。本文将高效的多重网格策略的NLS-4DVar方法应用于雷达径向风数据同化中。通过一组基于ARW-WRF模式的观测系统模拟试验检验该方法对雷达径向风的同化能力和同化效率。试验结果显示,同化雷达径向风数据后,初始场得到明显改进且24小时累计降水预报精度有大幅度提高。与此同时,多重网格策略的NLS-4DVar方法还减少了计算代价,明显提高了计算效率。
基金
supported by the National Key Research and Development Program of China [grant number2016YFA0600203]
the National Natural Science Foundation of China [grant number 41575100]
the Key Research Program of Frontier Sciences,Chinese Academy of Sciences[grant number QYZDY-SSW-DQC012]
