In this study, to meet the need for the accurate prediction of sea level anomaly (SLA), a hybrid model is proposed. In this model, empirical mode decomposition is combined with singular spectrum analysis and least-squares extrapolation to predict satellite-derived SLA. Each intrinsic
mode function series of an empirical mode decomposition is decomposed and reconstructed using singular spectrum analysis. The reconstructed components and the residual series are predicted using least-squares extrapolation. This hybrid model was used for satellite-derived SLAs that were obtained
using multi-mission along-track satellite altimetry data from September 1992 to January 2018, and the prediction errors for 3 years lead times were analysed. The observations and predictions of the principal components for annual or interannual periods correlated well, and the proposed
hybrid model effectively predicted the SLAs. For the 3 years lead time predictions, the mean absolute error and root-mean-square error were 1.03 and 1.32 cm, respectively, which were less than those reported for existing methods.
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Document Type: Research Article
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
First Institute of Oceanography, Ministry of Natural Resources, Qingdao, Shandong, China
Publication date: October 18, 2019
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