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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 2 | Copyright
Ocean Sci., 14, 301-320, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 Hong1,2, Xi Chen1, Ren Zhang1,2, Dong Wang3, Shuanghe Shen2, and Vijay P. Singh4 Mei Hong et al.
  • 1Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China
  • 2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
  • 3Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, 210093, China
  • 4Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843, USA

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15%. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55% is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

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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.
With the objective of tackling the problem of inaccurate long-term ENSO forecasts, a new...