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《大气和海洋科学快报(英文版)》 2010-03
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A Multivariate Empirical Orthogonal Function-Based Scheme for the Balanced Initial Ensemble Generation of an Ensemble Kalman Filter

ZHENG Fei 1 and ZHU Jiang 2 1 International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China  
The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly.
【Fund】: supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX1-YW-12-03);; the National Basic Research Program of China (Grant No. 2010CB951901);; the National Natural Science Foundation of China (Grant No. 40805033)
【CateGory Index】: P435
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【Citations】
Chinese Journal Full-text Database 1 Hits
1 MU Mu & DUAN Wansuo LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;A new approach to studying ENSO predictability:Conditional nonlinear optimal perturbation[J];科学通报(英文版);2003-10
【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 TIAN Xiang-Jun and XIE Zheng-Hui ICCES/LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation[J];大气和海洋科学快报(英文版);2009-03
2 TAN Xiao-Wei1,2 and WANG Bin1 1 Laboratory of numerical modeling for Atmospheric Sciences & Geophysical fluid dynamics (LASG),Institute of Atmospheric Phys- ics (IAP),Chinese Academy of Sciences,Beijing 100029,China 2 Graduate School of the Chinese Academy of Sciences,Beijing 100049,China;Impacts of Initial Perturbations on 24-h Sea-Level Pressure Predictions Near Typhoon Matsa[J];大气和海洋科学快报(英文版);2009-05
3 LI Chong-Yin and LING Jian LASG,Institute of Atmospheric Physics,Chinese Academy of Science,Beijing 100029,China;Physical Essence of the "Predictability Barrier"[J];大气和海洋科学快报(英文版);2009-05
4 SUN Guo-Dong 1 and MU Mu 1, 2 1 The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Key Laboratory of Ocean Circulation and Wave, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;A Preliminary Application of the Differential Evolution Algorithm to Calculate the CNOP[J];大气和海洋科学快报(英文版);2009-06
5 YAN Chang-Xiang 1 and ZHU Jiang 2 1 International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;The Impact of "Bad" Argo Profiles on Ocean Data Assimilation[J];大气和海洋科学快报(英文版);2010-02
6 YAN Chang-Xiang 1,ZHU Jiang 2,and XIE Ji-Ping 1 1 International Center for Climate and Environment Science,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;An Ocean Reanalysis System for the Joining Area of Asia and Indian-Pacific Ocean[J];大气和海洋科学快报(英文版);2010-02
7 WEI Chao 1,2 and DUAN Wan-Suo 1 1 LASG,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China 2 Graduate University of Chinese Academy of Sciences,Beijing 100049,China;The "Spring Predictability Barrier" Phenomenon of ENSO Predictions Generated with the FGOALS-g Model[J];大气和海洋科学快报(英文版);2010-02
8 TAN Xiao-Wei1,2 and WANG Dong-Liang3,1,4 1 Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing 100029, China 2 National Meteorological Center of China Meteorological Administration (CMA), Beijing 100081, China 3 Laboratory of Typhoon Forecast Technique, Shanghai Typhoon Institute of CMA, Shanghai 200030, China 4 Graduate University of the Chinese Academy of Sciences, Beijing 100049, China;Preliminary Study of Sensitive Areas for Several Tropical Cyclone Track Prediction Cases in 2007[J];大气和海洋科学快报(英文版);2010-06
9 WANG Lu1,2 and ZHOU Tian-Jun1 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China 2 Graduate University of the Chinese Academy of Sciences,Beijing 100029,China;Assessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals[J];大气和海洋科学快报(英文版);2012-01
10 WU Guocan1) ZHENG Xiaogu2) LI Yong1)(1)School of Mathematical Science,Beijing Normal University,Key Laboratory of Mathematics and Complex Systems,Ministry of Education;2) College of Global Change and Earth System Science,Beijing Normal University,100875,Beijing,China);IMPROVEMENT ON ENSEMBLE KALMAN FILTER OF NONLINEAR OBSERVATIONAL OPERATOR[J];Journal of Beijing Normal University(Natural Science);2010-06
China Proceedings of conference Full-text Database 3 Hits
1 Xinan Yue,Weixing Wan,Libo Liu,Fei Zheng,Jiuhou Lei, Biqiang Zhao,Guirong Xu,Shun-Rong Zhang,and Jiang Zhu 1 Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing,China. 2 Wuhan Institute of Physics and Mathematics,Chinese Academy of Sciences,Wuhan,China. 3 Graduate School,Chinese Academy of Sciences,Beijing,China. 4 Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing,China. 5 High Altitude Observatory,National Center for Atmospheric Research,Boulder,Colorado,USA. 6 Haystack Observatory,Massachusetts Institute of Technology, Weslford,Massachusetts,USA.;Data assimilation of incoherent scatter radar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter[A];[C];2008
2 YUE Xin-An~(1,2),WAN Wei-Xing~1,LIU Li-Bo~1,NING Bai-Qi~1, ZHAO Bi-Qiang~1,LI Guo-Zhu~1,XIONG Bo~1 1 Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China 2 State Key Laboratory of Space Weather,Chinese Academy of Sciences,Beijing 100190,China;Development of an ionospheric numerical assimilation nowcast and forecast system based on Gauss-Markov Kalman filter An observation system simulation experiment taking example for China and its surrounding area[A];[C];2011
3 ZHAO Hongliang (Key Laboratory of Mechanics on Western Disaster and Environment,Lanzhou University,Lanzhou,Gansu 730000,China);SPATIAL VARIABILITY OF GEOMECHANICAL PARAMETERS ESTIMATION VIA ENSEMBLE KALMAN FILTER METHOD[A];[C];2010
【Secondary Citations】
Chinese Journal Full-text Database 3 Hits
1 Mu Mu , Duan Wansuo and Wang Jiacheng LASG, Institute of Atmospheric physics, Chinese ,Academy of Sciences,Beijing 100029;The Predictability Problems in Numerical Weather and Climate Prediction[J];大气科学进展(英文版);2002-02
2 MU Mu LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Nonlinear singular vectors and nonlinear singular values[J];中国科学(D辑:地球科学)(英文版);2000-04
3 MU Mu WANG JiachengLASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Nonlinear fastest growing perturbation and the first kind of predictability[J];中国科学(D辑:地球科学)(英文版);2001-12
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