双分辨率集合卡尔曼滤波同化方案与模拟试验

The dual-resolution approach for ensemble Kalman filter and simulation tests

对于高分辨率中尺度模式集合卡尔曼滤波实际应用的困难是集合预报会耗费大量的时间,而双分辨率集合卡尔曼滤波是由一组低分辨率样本提供同化所需的背景误差协方差矩阵,这种方法可以减少集合预报的时间。为了检验其有效性,利用模拟资料,与标准高分辨率集合卡尔曼滤波方法比较。结果表明:在第一个同化时次,两者对500 hPa水平风场和扰动位温场的分析增量场均与真实增量场的高低值中心位置一致,且结构与真实增量场接近,前者(高分辨率集合卡尔曼滤波)的增量值比后者(双分辨率集合卡尔曼滤波)的增量值更接近真实情况。在连续的预报—同化循环试验中,随着同化次数的增加,两种方法分析变量的均方根误差总体上都是下降的,均表现了较好的同化能力,但后者与前者相比仍存在一定的差距;而在相同的运行环境下,后者的运行时间仅是前者的1/6。

A huge computational cost in the ensemble forecast is primary challenge for the ensemble Kalman filter application into high-resolution mesoscale models under an operational environment.The dual-resolution ensemble Kalman filter algorithm could significantly save the time of computation because its covariance matrix of errors is provided by a group of low resolution samples.Using the simulated data,the dual-resolution ensemble Kalman falter algorithm was tested and it was compared with the high-resolution ensemble Kalman filter method.The results show that in the first assimilation cycle,the center locations of high and low values of simulated increments of horizontal wind field and disturbed potential temperature field at 500 hPa and their observed increment fields are same for both methods,and the structures are close to the observational increment field.The increment value from the approach of the high resolution ensemble Kalman filter is closer to the observational value than that from the dualresolution method.In the forecast-assimilation cycle test,the root mean square errors from the two methods decrease generally with the increase of assimilation number and it suggests that both methods have compatible assimilation abilities.The result from the dual-resolution is poorer than that of the high resolution method,but the running time of the former is only 1/6 of that of the latter.