Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

作     者：CHEN Hui YIN Xun Qiang BAO Ying QIAO Fang Li Chen H;Yin X Q;Bao Y;Qiao F L

作者机构：The First Institute of OceanographyState Oceanic Administration Key Laboratory of Marine Science and Numerical ModelingState Oceanic Administration 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2016年第59卷第12期

页      面：484-494页

核心收录：

学科分类：08[工学] 0816[工学-测绘科学与技术] 

基　　金：the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406404) the Public Science and Technology Research Funds Projects of Ocean (Grant No. 201505013) Scientific Research Foundation of the First Institute of Oceanography, State Oceanic Administration (Grant No. 2012G24) 

主　　题：Earth system model Ocean satellite data Ensemble adjustment Kalman filter Data assimilation 

摘      要：Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.