大气科学2017，Vol.41Issue(2)：236-250,15.DOI:10.3878/j.issn.1006-9895.1606.15298
采用不同样本集合同化地面观测对一次飑线过程的影响
Influence of Assimilating Surface Observations on a Squall Line with Different Ensembles
摘要
Abstract
The Weather Research and Forecasting (WRF) model with the hybrid ETKF-3DVAR (ensemble transform Kalman filter-three-dimensional variational data assimilation) data assimilation system is used to investigate the impact of different ensemble generation schemes on a squall line forecast in the Huanghe-Huaihe region in the summer by assimilating the surface observations.Ensembles are created in three different ways-by using the different initial ensemble samples (RCV),by using different model physical process schemes (PPMP),and by combining the first two ensembles (BLE).Based on the increments of model states,assimilating the surface observations mainly adjusts the spatial structures of wind and water vapor mixing ratio below the level of 850 hPa;the RCV scheme mainly updates the wind distribution,the PPMP scheme updates the water vapor mixing ratio,and the BLE scheme has the characteristics of both RCV and PPME Assimilating surface observations can also improve 6-h precipitation forecasts,and the PPMP scheme can give a relatively better performance compared to PPMP and BLE,especially for the prediction of rainfall location and intensity.RCV and BLE schemes present a better simulation for the bow echo,and the performance with BLE is similar to that with RCV.PPMP and RCV schemes can adjust the position and intensity of the cold pool,and also influence the times of appearance and disappearance of the squall line.Generally,PPMP scheme has a greater impact on the squall line than RCV and BLE.关键词
样本生成方案/地面观测/混合同化/中尺度模式WRF/飑线Key words
Ensemble generation scheme/Surface data/Hybrid data assimilation/Mesoscale model WRF/Squall line分类
天文与地球科学引用本文复制引用
李少英,张述文,毛伏平,李彦霖..采用不同样本集合同化地面观测对一次飑线过程的影响[J].大气科学,2017,41(2):236-250,15.基金项目
国家重点基础研究发展计划(973计划)项目2013CB430102,国家自然科学基金项目41575098,高等学校博士学科点专项科研基金项目20120211110019 National Basic Research Program of China (973 Program,Grant 2013CB430102),National Natural Science Foundation of China (Grant 41575098),Specialized Research Fund for the Doctoral Program of Higher Education (Grant 20120211110019) （973计划）
