Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

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• 作者：Chuan Gao ; Xinrong Wu ; Rong-Hua Zhang
• 刊名：Advances in Atmospheric Sciences
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：33
• 期：7
• 页码：875-888
• 全文大小：1,168 KB
• 刊物主题：Atmospheric Sciences; Meteorology; Geophysics/Geodesy;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-9533
• 卷排序：33

文摘

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The “observation” of the SST anomaly, which is sampled from a “truth” model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.Key wordsFour-dimensional variational data assimilationintermediate coupled modeltwin experimentENSO prediction