An Economical Approach to Four-dimensional Variational Data Assimilation  

作　　者：王斌 刘娟娟 王曙东 成巍 刘娟 刘成思 Qingnong XIAO Ying-Hwa KUO 

机构地区：[1]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 [2]Graduate School of the Chinese Academy of Sciences, Beijing 100049 [3]College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA [4]Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research,Boulder, CO 80307-3000, USA

出　　处：《Advances in Atmospheric Sciences》2010年第4期715-727,共13页大气科学进展（英文版）

基　　金：the Ministry of Science and Technology of China for funding the 973 project (Grant No. 2004CB418304); the Ministry of Finance of China and the China Meteorological Administration for the Special Project of Meteorological Sector [Grant No. GYHY(QX)2007-6-15]

摘　　要：Four-dimensional variational data assimilation （4DVar） is one of the most promising methods to provide optimal analysis for numerical weather prediction （NWP）. Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimension- reduced projection （DRP）, which is called ＂DRP-4DVar.＂ The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation. To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.Four-dimensional variational data assimilation （4DVar） is one of the most promising methods to provide optimal analysis for numerical weather prediction （NWP）. Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimension- reduced projection （DRP）, which is called ＂DRP-4DVar.＂ The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation. To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.

关 键 词：4DVAR ADJOINT dimension reduction historical sample observing system simulation experiment 

分 类 号：P456.7[天文地球—大气科学及气象学]