预分析初始扰动样本对降维投影四维变分同化性能的影响

Influence of pre-analyzing the initial perturbation sample on DRP-4DVar assimilation performance

降维投影四维变分同化(DRP-4DVar)方法的背景误差协方差是由基于历史预报的扰动样本统计得到的,为了改进降维投影四维变分同化系统中背景误差协方差的流依赖特性,提出了对初始扰动样本进行预分析的新思路,即在对背景场分析之前,利用降维投影四维变分同化系统本身对每个样本进行预先分析,使得统计出的背景误差协方差随实际的天气形势而变化,从而实现其在真正意义上的流依赖,且在循环同化时能够避免滤波发散现象的出现。试验结果表明,对样本进行预先分析能够通过改善同化系统中背景误差协方差的空间结构和流依赖特性,来进一步改进降维投影四维变分同化方法的性能,为数值模式提供更精确的初始场,从而提高了基本模式变量的预报精度,并改善了对强降水的模拟能力。相比较而言,对所有初始扰动样本都进行了预分析的同化试验能够得到最优的分析和预报。

The dimension-reduced projection-four dimensional variational data assimilation （DRP-4DVar） approach utilizes the historical-forecast-based initial perturbation samples to estimate the background error covariance （BEC）. Then the new idea of pre-analyzing the initial perturbation samples is presented to improve the flow-dependent property of BEC in the DRP-4DVar system. Before analyzing the background field, each sample is pre-analyzed by using the DRP-4DVar system so that the newly estimated BEC will evolve with the actual weather situation. Consequently, the real flow-dependency of BEC can be achieved and meanwhile the filter divergence can be effectively prevented in cycling runs. The experiment results show that, by ameliorating the spatical structure and flow-dependency of the BEC, the pre-analyzed samples indeed help to further improve the performance of DRP-4DVar, which provides more accurate initial field for the numerical forecast model. Not only the forecasting precision of basic model variables can be improved, but also the simulated precipitation is ameliorated. By contrast, the assimilation run with all of the initial perturbation samples pre-analyzed obtains the optimal analysis and forecasts