Statistical Prediction of the South China Sea Surface Height Anomaly
Joint Authors
Sun, Chunjian
Shao, Caixia
Zhang, Weimin
Chai, Xinmin
Wang, Zhimin
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS).
The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms.
Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS.
To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant.
Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term.
Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter.
In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007.
American Psychological Association (APA)
Shao, Caixia& Zhang, Weimin& Sun, Chunjian& Chai, Xinmin& Wang, Zhimin. 2015. Statistical Prediction of the South China Sea Surface Height Anomaly. Advances in Meteorology،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1052850
Modern Language Association (MLA)
Shao, Caixia…[et al.]. Statistical Prediction of the South China Sea Surface Height Anomaly. Advances in Meteorology No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1052850
American Medical Association (AMA)
Shao, Caixia& Zhang, Weimin& Sun, Chunjian& Chai, Xinmin& Wang, Zhimin. Statistical Prediction of the South China Sea Surface Height Anomaly. Advances in Meteorology. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1052850
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1052850