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

doi:10.3969/j.issn.1001-909X.2021.01.008

Abstract

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

CLC Number:

P731.31

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.

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