Editorial Type:
Article
Article Type:
Research Article

A Dynamical–Statistical Forecasting Model of the Western Pacific Subtropical High Area Index Based on an Improved Self-Memorization Principle

Mei Hong Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China

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Ren Zhang Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China

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Dong Wang Key Laboratory of Surficial Geochemistry, Ministry of Education, and 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, China

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Min Wang Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China

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Kefeng Liu Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China

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Vijay P. Singh Department of Biological and Agricultural Engineering, and Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas

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Print Publication:
01 Nov 2015
DOI:
https://doi.org/10.1175/MWR-D-15-0181.1
Page(s):
4561–4577
Restricted access

Abstract

A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.

Corresponding author address: Mei Hong, Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China. E-mail: flowerrainhm@126.com

Abstract

A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.

Corresponding author address: Mei Hong, Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China. E-mail: flowerrainhm@126.com
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