Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

作     者：QIU Xue Xing ZHANG Fu Qing Qiu X X;Zhang F Q

作者机构：Anhui Meteorological Observatory Hefei 230031 China Department of Meteorology Pennsylvania State University University Park PA 16802 USA 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2016年第59卷第1期

页      面：518-532页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：公益性行业(气象)科研专项 

主　　题：En KF Doppler radar data Local extreme rain Predictability 

摘      要：Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter(En KF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with En KF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from En KF assimilation due to small amplitude of initial perturbations could have critical influences to the event s prediction. Forecast could be improved through more cycles of En KF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.