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基于相似系数的海温中长期统计预报方法研究
引用本文:李科,苑福利,刘厂. 基于相似系数的海温中长期统计预报方法研究[J]. 海洋学研究, 2021, 39(1): 67-78. DOI: 10.3969/j.issn.1001-909X.2021.01.008
作者姓名:李科  苑福利  刘厂
作者单位: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
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...

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
收稿时间:2021-01-26

Study on the medium and long-term statistical forecasting method of SST based on similarity coefficient
LI Ke,YUAN Fuli,LIU Chang. Study on the medium and long-term statistical forecasting method of SST based on similarity coefficient[J]. Journal of Marine Sciences, 2021, 39(1): 67-78. DOI: 10.3969/j.issn.1001-909X.2021.01.008
Authors:LI Ke  YUAN Fuli  LIU Chang
Affiliation:1. Naval Research Institute of PLA, Tianjin 300061, China; 2. College of Intelligent SystemsScience and Engineering, Harbin Engineering University, Harbin 150001, China
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.
Keywords:time series forecasting  similarity coefficient analysis  medium and long-term SST forecasts  forecast error correction  
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