基于海温因子的传递函数模型在黄海绿潮规模预测中的应用  

Application of transfer function model in predicting the green tide scale in the Yellow Sea based on sea surface temperature

作　　者：刘旭[1,2] 梁颖祺[1] 王兆毅[1] 李志杰 王峥[1] 季轩梁 何恩业[1] LIU Xu;LIANG Yingqi;WANG Zhaoyi;LI Zhijie;WANG Zheng;Xuanliang;HE Enye(National Marine Envirormertal Fonecasting Center,Beijing 100081,China;School of Ecomomics&Management,Beijing Fonestry Urniversiy,Beijing 100083,China)

机构地区：[1]国家海洋环境预报中心,北京100081 [2]北京林业大学经济管理学院,北京100083

出　　处：《海洋预报》2022年第4期91-101,共11页Marine Forecasts

基　　金：自然资源部海洋环境探测技术与应用重点实验室开放基金课题(MESTA-2020-B012);国家重点研发计划(2018YFC1407402);南方海洋科学与工程广东省实验室(珠海)自主科研项目(SML2020SP008)。

摘　　要：为寻求基于海温生态因子的绿潮规模预测方法,采用2010—2019年国家卫星海洋应用中心黄海绿潮卫星遥感资料和日本气象厅融合海表温度数据,利用协整检验和Granger因果检验方法对绿潮覆盖面积和海温之间的长期均衡和因果关系进行分析。结果显示:两者间存在长期协整性,海温是绿潮规模变化的Granger原因。通过建立协整模型和传递函数模型,开展了海温对绿潮规模影响的定量计算实例研究。结果表明:两种模型均可有效地刻画2010—2017年绿潮规模的变化过程,2018—2019年绿潮规模的预测结果与遥感实况较吻合,体现了模型的可靠性和可移植性。传递函数模型预测结果的RMSE为160.94,MAE为109.70,整体略优于协整模型的RMSE(171.40)和MAE(122.48),说明数据经预白化处理后可提高预测精准度,海温与绿潮覆盖面积具有动态相关性。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 long-term equilibrium and causal relationship between green tide coverage area and SST based on co-integration test and Granger causality 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 cointegration model and transfer function model It is shown that both models could effectively depict 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.

关 键 词：绿潮 海温 协整模型 传递函数模型 GRANGER因果检验 遥感 

分 类 号：X55[环境科学与工程—环境工程]