海洋学研究 ›› 2021, Vol. 39 ›› Issue (1): 67-78.DOI: 10.3969/j.issn.1001-909X.2021.01.008

• 研究报道 • 上一篇    下一篇

基于相似系数的海温中长期统计预报方法研究

李科1, 苑福利1, 刘厂2   

  1. 1.中国人民解放军 海军研究院,天津 300061;
    2.哈尔滨工程大学 智能科学与工程学院,黑龙江 哈尔滨 150001
  • 收稿日期:2021-01-26 出版日期:2021-03-15 发布日期:2021-03-15
  • 作者简介:李科(1980-),男,湖南省长沙市人,副研究员,主要从事海洋测绘和海洋环境保障论证研究。E-mail: Like235@sina.com

Study on the medium and long-term statistical forecasting method of SST based on similarity coefficient

LI Ke1, YUAN Fuli1, LIU Chang2   

  1. 1. Naval Research Institute of PLA, Tianjin 300061, China;
    2. College of Intelligent SystemsScience and Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2021-01-26 Online:2021-03-15 Published:2021-03-15

摘要: 海温是认知和研究大气、海洋物理性质和演变规律的重要海洋环境动力要素之一,也是海洋预报组成中不可或缺的组成部分,对海温的分布及演变规律进行研究对海洋经济、环保和国防安全具有重要的意义。本文提出了一种基于相似系数的海温统计预报方法,利用数理统计分析方法,构建基于相似系数的统计预报模型来实现海温的单点时间序列预报。在南海海域海温预报中长期实验中,该方法比气候态预报和ARIMA预报方法的结果更优,证明该方法的有效性并为海温的中长期预报提供了新思路。

关键词: 时间序列预报, 相似系数分析, 海温中长期预报, 预报误差订正

Abstract: Sea Surface Temperature(SST) is one of the important marine environmental dynamics elements in the cognition and study of the physical properties and evolution of the atmosphere and ocean, and is also an indispensable component in the composition of marine forecasting. The study of the distribution and evolution of SST is of great significance to the marine economy, environmental protection and national defense security. In this study, a statistical forecasting method based on the similarity coefficient was proposed, and a statistical forecasting model based on the similarity coefficient was constructed to realize the single-point time series forecasting of SST. In the medium and long-term experiments of SST forecasting in the South China Sea, this method has better results than those from Optimal Climate Normals forecasting and ARIMA forecasting methods, which proves the effectiveness of this method and provides a new idea for the medium and long-term forecasting of SST.

Key words: time series forecasting, similarity coefficient analysis, medium and long-term SST forecasts, forecast error correction

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