Time-Expanded Sampling Approach for Ensemble Kalman Filter:Experiment Assimilation of Simulated Soundings  

作　　者：杨毅 弓中强 王金艳 刘鑫华 

机构地区：[1]Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,Key Laboratoryof Arid Climatic Change and Disaster Reduction of Gansu Province,College ofAtmospheric Sciences,Lanzhou University [2]National Meteorological Center of China Meteorological Admistration

出　　处：《Acta meteorologica Sinica》2011年第5期558-567,共10页

基　　金：Supported by the National Natural Science Foundation of China (40805044);Natural Science Foundation of Gansu Province(1010RJZA118);Fundmental Research Fund for Central Universities Science and Technology Development Program of China(lzujbky-2010-12)

摘　　要：In the Ensemble Kalman Filter（EnKF） data assimilation-prediction system,most of the computation time is spent on the prediction runs of ensemble members.A limited or small ensemble size does reduce the computational cost,but an excessively small ensemble size usually leads to filter divergence,especially when there are model errors.In order to improve the efficiency of the EnKF data assimilation-prediction system and prevent it against filter divergence,a time-expanded sampling approach for EnKF based on the WRF（Weather Research and Forecasting） model is used to assimilate simulated sounding data.The approach samples a series of perturbed state vectors from Nb member prediction runs not only at the analysis time（as the conventional approach does） but also at equally separated time levels（time interval is △t） before and after the analysis time with M times.All the above sampled state vectors are used to construct the ensemble and compute the background covariance for the analysis,so the ensemble size is increased from Nb to Nb＋2M×Nb=（1＋2M）×Nb） without increasing the number of prediction runs（it is still Nb）.This reduces the computational cost.A series of experiments are conducted to investigate the impact of △t（the time interval of time-expanded sampling） and M（the maximum sampling times） on the analysis.The results show that if t and M are properly selected,the time-expanded sampling approach achieves the similar effect to that from the conventional approach with an ensemble size of（1＋2M）× Nb,but the number of prediction runs is greatly reduced.In the Ensemble Kalman Filter（EnKF） data assimilation-prediction system,most of the computation time is spent on the prediction runs of ensemble members.A limited or small ensemble size does reduce the computational cost,but an excessively small ensemble size usually leads to filter divergence,especially when there are model errors.In order to improve the efficiency of the EnKF data assimilation-prediction system and prevent it against filter divergence,a time-expanded sampling approach for EnKF based on the WRF（Weather Research and Forecasting） model is used to assimilate simulated sounding data.The approach samples a series of perturbed state vectors from Nb member prediction runs not only at the analysis time（as the conventional approach does） but also at equally separated time levels（time interval is △t） before and after the analysis time with M times.All the above sampled state vectors are used to construct the ensemble and compute the background covariance for the analysis,so the ensemble size is increased from Nb to Nb＋2M×Nb=（1＋2M）×Nb） without increasing the number of prediction runs（it is still Nb）.This reduces the computational cost.A series of experiments are conducted to investigate the impact of △t（the time interval of time-expanded sampling） and M（the maximum sampling times） on the analysis.The results show that if t and M are properly selected,the time-expanded sampling approach achieves the similar effect to that from the conventional approach with an ensemble size of（1＋2M）× Nb,but the number of prediction runs is greatly reduced.

关 键 词：ASSIMILATION ENKF time-expanded sampling 

分 类 号：P456[天文地球—大气科学及气象学]