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Ocean Science
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Articles | Volume 14, issue 2
Articles | Volume 14, issue 2
https://doi.org/10.5194/os-14-301-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-14-301-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 2
Research article
 | 
24 Apr 2018
Research article |  | 24 Apr 2018

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Mei Hong, Xi Chen, Ren Zhang, Dong Wang, Shuanghe Shen, and Vijay P. Singh

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Short summary
With the objective of tackling the problem of inaccurate long-term ENSO forecasts, a new forecasting model of the SSTA field was proposed based on a dynamic system reconstruction idea and the principle of self-memorization. The improved model was used to forecast the SSTA field. The forecasted SSTA fields of three types of events are accurate. The improved model also has good forecasting results of the ENSO index. So our model has an advantage in ENSO prediction precision and length.
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