SST data assimilation experiments using an adaptive variational method  

作　　者：ZHU Jiang WANG Hui ZHOU Guangqing 

机构地区：[1]ICCES,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [2]National Natural Science Foundation of China,Beijing 100083,China

出　　处：《Chinese Science Bulletin》2002年第23期2010-2013,2029,共5页

基　　金：This work was supported by the National Key Basic Research Development Programme (Grant No. G1998040901-5);the National Natural Science Foundation of China (Grant No. 40176009) ;the Hundred Talents Project of the Chinese Academy of Sciences.

摘　　要：An adaptive variational data assimilation method is proposed by Zhu and Kamachi[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.An adaptive variational data assimilation method is proposed by Zhu and Kamachi[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.

关 键 词：data ASSIMILATION ADAPTIVE VARIATIONAL method sea surface temperature OCEANIC mixed layer. 

分 类 号：P732[天文地球—海洋科学]