多物理ETKF在暴雨集合预报中的初步应用

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Precipitation Ensemble Prediction

基于集合转换卡尔曼滤波(ETKF)的初值扰动方法是目前集合预报领域热点方法之一,但应用在短期集合预报中仍存在离散度不够、误差较大等问题。考虑到在区域短期集合预报中,模式不确定性和边界不确定性的影响不能忽略,本文尝试在ETKF生成分析扰动的过程中,同时考虑初值不确定性、物理不确定性与边界不确定性,进而构建多初值、多物理、多边界ETKF集合,并以2010年9月30日到10月8日海南岛特大暴雨作为研究个例,对其在暴雨集合预报中的应用展开初步研究,重点分析多种物理参数化过程对预报结果的影响。结果表明,多物理过程的ETKF(多物理ETKF)和单物理过程的ETKF(单一ETKF)均优于对照预报,多物理ETKF优势更加明显,其均方根误差、离散度等指标均得到很好的改善;对于降水采用SAL方法进行检验,发现多物理ETKF对于降水位置的预报有明显的改善,对于特大暴雨的强度预报也略有改善。研究表明,在ETKF初值扰动中加入多种物理过程,可以有效改善短期集合的离散度,提高预报准确率,有良好的发展前景和应用潜力。

The optimal initial perturbation method using the ensemble transform Kalman filter （ETKF） is a point of intense popular interest in ensemble prediction. However, problems remain with respect to short-term ensemble prediction, such as insufficient ensemble spread, too large a prediction error, and so on. In this study, multi-physical parameterizations and boundary perturbations were introduced into the initial ETKF, and a heavy rainfall event that occurred in Hainan Province during 30 September to 6 October 2010 was simulated, as an example, using the single-physical ETKF and multi-physical ETKF in WRF3.5. The main results were as follows： All ensemble schemes outperformed the contrast forecast, with the multi-physical ETKF found to be the best. The RMSE and ensemble spread were well improved. For the multi-physical ETKF, the improvement in the location of heavy rain was obvious. The results indicate that the introduction of a variety of physical processes in the initial perturbations for the ETKF could significantly amplify the ensemble spread and improve the ensemble forecast of each quantity. The application of the physical ETKF method may have great potential in precipitation ensemble prediction.