Application of transfer function model based on sea temperature factor in the prediction of green tide scale in the Yellow Sea
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Author: Liu Xu1 2Liang Yingqi1Wang Zhaoyi1Li Zhijie1Wang Zheng1Ji Xuanliang1He Enye1Hi Enye1
United: 1. National Marine Environment Forecast Center , Beijing 100081;
2. School of Economics and Management, Beijing Forestry University , Beijing 100083
Classification number: X55
Publication year·volume·issue (page number): 2022·39·Issue 4 (91-101)
Abstract: In order to seek the green tide scale prediction method based on the ecological factor of the sea temperature, the 2010-2019 National Satellite Ocean Application Center Yellow Sea Green Tide Satellite Remote Sensing Data and the Japan Meteorological Agency combined sea surface temperature data, and used cointegration test and Granger causal testing methods to analyze the long-term equilibrium and causal relationship between the green tide coverage area and sea temperature. The results show that there is long-term cointegration between the two, and the sea temperature is the Granger reason for the change in the scale of green tides. By establishing a cointegration model and transfer function model, a quantitative calculation example study on the impact of sea temperature on green tide scale was carried out. The results show that both models can effectively characterize the change process of green tide scale from 2010 to 2017. The prediction results of green tide scale from 2018 to 2019 are more consistent with the remote sensing real-time, reflecting the reliability and portability of the model. The RMSE of the prediction results of the transfer function model is 160.94 and the MAE is 109.70, which is slightly better than the RMSE (171.40) and MAE (122.48) of the cointegration model, indicating that the data can improve the prediction accuracy after pre-whitening, and the sea temperature and green tide coverage area are dynamically correlated.
Keywords: Green tide; SST; Cointegration model; Transfer function model; Granger causality test; Remote sensing
Abstract: In order to find a method to predict the green tide scale based on sea temperature, the Yellow Sea green tide satellite remote sensing data of the National Satellite Ocean Application Service and the merged sea surface temperature (SST) data of the Japan Meteorological Agency (JMA) from 2010 to 2019 is used to analyze the longterm equilibrium and causeal relationship between green tide coverage area and SST based on co-integration test and Granger causeality test. The results show that there is long-term co-integration between green tide coverage area and SST, and SST is the Granger cause of the green tide scale variation. The quantitative calculation case study of the influence of sea temperature on green tide scale is carried out by establishing a coefficient model and transfer function model It is shown that both models could effectively describe the variation process of green tide scale from 2010 to 2017, and the prediction results of green tide scale from 2018 to 2019 agree well with the remote sensing monitoring data, which reflects the reliability and portability of the models. The root mean square error (RMSE) and mean average error (MAE) of the predicted results of the transfer function model is 160.94 and 109.70, respectively, which is slightly better than the co-integration model with the RMSE of 171.40 and the MAE of 122.48, indicating that the prediction accuracy could be improved by the pre-whitening process of the SST. Moreover, SST and green tide area has a dynamic correlation.
Key words: green tide; sea temperature; coordination model; transfer function model; Granger causeality test;
remote sensing
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