基于NLS-4DVar方法的雷达资料同化及其在暴雨预报中的应用

The Radar Data Assimilation System Based on NLS-4DVar and Its Application in Heavy Rain Forecast

在基于本征正交分解POD(Proper Orthogonal Decomposition)的集合四维变分同化方法(POD4DEn Var)建立的雷达资料同化系统(PRAS)的基础上,本文利用非线性最小二乘法的集合四维变分同化方法(NLS-4DVar)对PRAS进行改进,解决PRAS在高度非线性情况下的适应性问题,建立了新的雷达资料同化系统(NRAS)。通过观测系统模拟试验OSSEs(Observing System Simulation Experiments)和两次实际暴雨同化试验(2010年7月8日,中国中部地区;2014年3月30日,中国华南地区)对NRAS进行检验,并与PRAS的同化结果进行了对比。结果表明:无论是OSSEs还是实际雷达资料的同化,相对于PRAS,NRAS能够进一步提高同化效果。通过增加迭代的次数,NRAS能够有效地调整初始场的风场和水汽场,进一步提高了降水强度和位置的预报精度。但随着迭代次数的增加,对初始场的调整变小,进而对降水预报效果的改进也减小。试验结果表明NRAS能够有效解决PRAS在高度非线性情况下的应用问题,通过有限次数的迭代,即可得到近似收敛的结果。因而NRAS有望在数值预报中更有效地同化雷达资料,提高中小尺度天气的预报水平。

In this paper,the PODEn4DVar-based radar data assimilation scheme（PRAS） was improved according to the theory of NLS（Non-Linear Least Squares）-4DVar（four-dimensional variational analysis） scheme.This work aims to dealwith the application problem of PRAS under highly nonlinear conditions.As a result,a new radar data assimilation scheme,i.e.NLS-4DVar-based radar data assimilation scheme（NRAS）,was developed.To evaluate whether NRAS can further improve the performance compared to PRAS,the Observing System Simulation Experiments（OSSEs） and real radar data assimilation experiments for two heavy rain events（July 8,2010,central China;March 30,2014,southern China） were conducted in this study.The results demonstrate that,for both the OSSEs and the real radar data assimilation experiments,NRAS can further improve the assimilation result in comparison to PRAS.By increasing iteration times,NRAS can adjust the wind field and water vapor field.This leads to further improvements on the forecast of intensity and location of the rainfall.However,with increases in the iteration times,the adjustment for the initial condition by NRAS becomes smaller,which leads to a smaller improvement on the rainfall forecast.The results indicate that NRAS can effectively deal with the application of PRAS under highly non-linear condition.With fewer iteration times,NRAS can obtain approximate convergence result.NRAS is expected to better assimilate radar data in numerical weather predictions,and thus further improve the prediction of meso-micro scale weather systems.