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
Mei Hong
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Dong Wang
Key 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
Shuanghe Shen
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Vijay P. Singh
Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843, USA
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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...
