基于动力降尺度的区域集合预报初值扰动构建方法研究

利用全球集合预报系统资料(Global Ensemble Forecast System,GEFS),基于WRF中尺度模式构建了区域集合预报系统,区域集合初值的构建采用两种方案,一种是GEFS全球集合预报初值场直接动力降尺度(称为DOWN集合),另一种是提取GEFS全球集合降尺度后的扰动场,并叠加到区域数值预报系统(北京快速更新循环数值预报系统:Beijing Rapid Update Cycle System,BJ-RUC)分析场上构建集合初值场(称为D-RUC集合)。进行了批量试验,通过对比发现D-RUC集合的中小尺度扰动增长优于DOWN集合,而大尺度扰动分量的增长两者相当,说明与高分辨率分析场叠加可以促进动力降尺度扰动的中小尺度扰动分量的增长。集合预报扰动准确性检验结果显示,短预报时效内DOWN集合扰动明显低估了预报误差,在预报误差较大的位置扰动较小,而D-RUC集合能够更好地识别预报场中哪些位置预报误差较大,而哪些位置预报误差较小。集合预报检验结果表明,D-RUC方法能显著改善短时效预报效果,集合离散度有所增加、均方根误差有所减少,概率预报评分显示D-RUC集合比DOWN集合在短预报时效占优。降水个例分析结果表明D-RUC方法能显著改善短时效内的降水概率预报效果。

Study on Initial Perturbation Construction Method for Regional Ensemble Forecast Based on Dynamical Downscaling

Using Global Ensemble Forecast System (GEFS) data, a regional ensemble forecast system based on WRF model is constructed. Two initialization schemes are tested to form the initial states of regional ensemble (namely D RUC ensemble). One is the direct dynamical downscaling of GEFS initial states (namely DOWN ensemble), and the other is the overlaying the downscaled initial perturbations of GEFS onto the analysis of high resolution regional numerical weather prediction (NWP) system, named Beijing Rapid Update Cycle (BJ RUC) system. Using the two methods, a series of ensemble forecast tests are conducted, and the results show that the small scale components of D RUC perturbations grow more rapidly than those of DOWN perturbations. For short term forecast, the DOWN perturbations tend to underestimate the forecast error while the D RUC perturbation tends to identify where the forecast error is large and where the forecast error is small. Ensemble forecast verification shows that the D RUC ensemble has larger spread and smaller root mean square error than DOWN ensemble at short forecast lead time, and the probabilistic scores of D RUC are also better for short term forecast. Typical precipitation case study shows that D RUC ensemble can provide better probabilistic precipitation forecast than DOWN ensemble.