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《Chinese Journal of Atmospheric Sciences》 2010-03
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Storm-Scale Ensemble Kalman Filter Data Assimilation Experiments Using Simulated Doppler Radar Data.Part Ⅰ:Perfect Model Tests

LAN Weiren1,4,ZHU Jiang2,Ming XUE 3,Jidong GAO3,and LEI Ting5 1 Graduate University of Chinese Academy of Sciences,Beijing 100049 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmosphere Physics,Chinese Academy of Sciences,Beijing 100029 3 Center for Analysis and Prediction of Storms and School of Meteorology,University of Oklahoma,Norman,Oklahoma 4 Unit 61741,PLA,Beijing 100081 5 Institute of Atmosphere Physics,Chinese Academy of Sciences,Beijing 100029  
This study tested the performance of ensemble Kalman filter(EnKF) under the perfect model assumption for storm-scale data assimilation of simulated Doppler radar observations.A series of observation system simulation experiments have been carried out to check the filter performances of EnKF in different configurations.It is found that the EnKF is very effective in the assimilation of storm-scale under the perfect model assumption if the configuration is properly set.An ensemble of 40 members and localization scale of 6 km are suitable to our experiments.To initialize the ensemble,adding smoothed Gaussian initial perturbations is superior to non-smoothed ones.Inflation that augments ensemble spread is necessary.Although the observation operator for reflectivity is strong nonlinear,assimilation reflectivity data only can bring positive effect to analysis.If both the radial velocity and reflectivity observations are assimilated,analysis will be better than that from single reflectivity assimilation.
【Fund】: 国家自然科学基金资助项目40620120437
【CateGory Index】: P416
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