Evaluation of the Global Ocean Data Assimilation System at INCOIS: The Tropical Indian Ocean

被引:75
|
作者
Ravichandran, M. [1 ]
Behringer, D.
Sivareddy, S. [1 ]
Girishkumar, M. S. [1 ]
Chacko, Neethu [1 ]
Harikumar, R. [1 ]
机构
[1] Indian Natl Ctr Ocean Informat Serv, Hyderabad 500090, Andhra Pradesh, India
关键词
Ocean modeling; Ocean data assimilation; Model validation; Model configuration; Tropical Indian Ocean; Wind sensitivity experiment; RESEARCH MOORED ARRAY; INTRASEASONAL VARIABILITY; ROSSBY WAVES; DIPOLE MODE; CURRENTS; BAY; PREDICTION; RESOLUTION; SALINITY; BENGAL;
D O I
10.1016/j.ocemod.2013.05.003
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A new version of NCEP's Global Ocean Data Assimilation System (GODAS), which is based on the Geophysical Fluid Dynamics Laboratory (GFDL) Modular ocean Model version 4.0 (MOM4.0) and a three-dimensional variational (3D-VAR) data assimilation scheme, was configured and operationalized at Indian National Centre for Ocean Information Services (INCOIS). The primary objective of the GODAS at INCOIS (INCOIS-GODAS) is to provide an accurate estimate of the ocean state, which will be used to initialize a coupled model for the seasonal monsoon forecast and also to understand the variability of the ocean at different time scales. In this paper, we assess the quality of ocean analyses in the Tropical Indian Ocean (TIO) obtained from the operational INCOIS-GODAS. In addition to this, we examined the sensitivity of INCOIS-GODAS to different momentum forcing and to the assimilation of temperature and synthetic salinity based on the experiments carried out with different wind products: NCEP2 and QuikSCAT and a free run respectively. The present study reveals that the model with assimilation simulates most of the observed features of temperature, SSHA and currents with reasonably good accuracy in the TIO at both intra-seasonal and inter-annual time scales. The analysis further shows that there was a considerable improvement in the ocean current field, when the model was forced with QuikSCAT winds. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:123 / 135
页数:13
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