Statistical Prediction of the South China Sea Surface Height Anomaly
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 follo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/907313 |
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| _version_ | 1832561461255208960 |
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| author | Caixia Shao Weimin Zhang Chunjian Sun Xinmin Chai Zhimin Wang |
| author_facet | Caixia Shao Weimin Zhang Chunjian Sun Xinmin Chai Zhimin Wang |
| author_sort | Caixia Shao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d7ef6f514e644a2fa1e4f38f8877cb00 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-d7ef6f514e644a2fa1e4f38f8877cb002025-02-03T01:25:05ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/907313907313Statistical Prediction of the South China Sea Surface Height AnomalyCaixia Shao0Weimin Zhang1Chunjian Sun2Xinmin Chai3Zhimin Wang4National University of Defense Technology, Changsha, Hunan 410073, ChinaNational University of Defense Technology, Changsha, Hunan 410073, ChinaKey Laboratory of Marine Environmental Information Technology, SOA, National Marine Data and Information Service, Tianjin 300171, ChinaKey Laboratory of Marine Environmental Information Technology, SOA, National Marine Data and Information Service, Tianjin 300171, ChinaKey Laboratory of Marine Environmental Information Technology, SOA, National Marine Data and Information Service, Tianjin 300171, ChinaBased 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.http://dx.doi.org/10.1155/2015/907313 |
| spellingShingle | Caixia Shao Weimin Zhang Chunjian Sun Xinmin Chai Zhimin Wang Statistical Prediction of the South China Sea Surface Height Anomaly Advances in Meteorology |
| title | Statistical Prediction of the South China Sea Surface Height Anomaly |
| title_full | Statistical Prediction of the South China Sea Surface Height Anomaly |
| title_fullStr | Statistical Prediction of the South China Sea Surface Height Anomaly |
| title_full_unstemmed | Statistical Prediction of the South China Sea Surface Height Anomaly |
| title_short | Statistical Prediction of the South China Sea Surface Height Anomaly |
| title_sort | statistical prediction of the south china sea surface height anomaly |
| url | http://dx.doi.org/10.1155/2015/907313 |
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