基于EEMD和ARIMA的海温预测模型研究
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摘要
类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。
Various types of ocean detection data with high spatial and temporal resolution provides possibilities for the application of signal decomposition and machine learning algorithms.In order to establish an effective sea surface temperature(SST)prediction model,the high time resolution of the SST and the fusion of products were used in this study,introducing the ensemble empirical mode decomposition(EEMD)in the field of signal processing and autoregressive integrated moving average model(ARIMA)in the field of machine learning.Firstly,the SST data were decomposed into several frequency sequences by EEMD method,which was the most suitable for decomposing natural signals.After that the ARIMA was used to predict the sequence of frequencies,and then the predicted results of each sequence were combined.Compared with the previous method of directly using SST data to build prediction model,this method achieves higher accuracy because of its abundant data,and provides a new way for better SST prediction.
Various types of ocean detection data with high spatial and temporal resolution provides possibilities for the application of signal decomposition and machine learning algorithms.In order to establish an effective sea surface temperature(SST)prediction model,the high time resolution of the SST and the fusion of products were used in this study,introducing the ensemble empirical mode decomposition(EEMD)in the field of signal processing and autoregressive integrated moving average model(ARIMA)in the field of machine learning.Firstly,the SST data were decomposed into several frequency sequences by EEMD method,which was the most suitable for decomposing natural signals.After that the ARIMA was used to predict the sequence of frequencies,and then the predicted results of each sequence were combined.Compared with the previous method of directly using SST data to build prediction model,this method achieves higher accuracy because of its abundant data,and provides a new way for better SST prediction.
引文
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[15]PORPORATO A,RIDOLFI L.Multivariate nonlinear prediction of river flows[J].Journal of Hydrology,2001,248(1-4):109-122.
[16]BROCKWELL P J.Time series:Theory and methods[M].Springer-Verlag,2015.
[2]XU Shu-wen,XIA Min-hui,WANG Xiao-chun,et al.Relationship between MJO and tropical wind diurnal cycle with SST diurnal warming[J].Marine Forecasts,2018,35(1):80-94.徐淑雯,夏旻慧,王晓春,等.MJO与热带风场、海温日变化的关系[J].海洋预报,2018,35(1):80-94.
[3]CHEN Jian,JIANG Zhu-hui,SU Xing-tao,et al.Prescribed error estimation and diagnostic analysis in reconstruction of 3-D ocean temperature field from multi-source data[J].Haiyang Xuebao,2018,40(4):15-29.陈建,姜祝辉,宿兴涛,等.多源资料重构三维海温场的先验误差估计和诊断分析[J].海洋学报,2018,40(4):15-29.
[4]FANG Yue-wei,TANG You-min,LI Jun-de,et al.Several statistical models to predict tropical Indian Ocean sea surface temperature anomaly[J].Journal of Marine Sciences,2018,36(1):1-15.方玥炜,唐佑民,李俊德,刘婷.几种统计模型对热带印度洋海温异常的预报[J].海洋学研究,2018,36(1):1-15.
[5]CHEN Si-si,ZHANG Jing-yong,HUANG Gang.Application of time-scale decomposition statistical method in climatic prediction of summer extreme high-temperature events in South China[J].Climatic and Environmental Research,2018,23(2):185-198.陈思思,张井勇,黄刚.时间尺度分离在华南夏季极端高温预测中的应用[J].气候与环境研究,2018,23(2):185-198.
[6]ZHAO Yu-heng,FENG Guo-lin,ZHENG Zhi-hai.Classification of winter typical sea surface temperature variabilities based on statistical method[J].Plateau Meteorology,2017,36(5):1 357-1 367.赵玉衡,封国林,郑志海.基于统计方法的典型冬季海温变率分类研究[J].高原气象,2017,36(5):1 357-1 367.
[7]MAO Wen-shu,WANG Qian-qian,PENG Jun,et al.Characteristics of meiyu precipitation and SVD analysis of precipitation over the Changjiang-Huaihe River valley and the sea surface temperature in the northern Pacific Ocean[J].Journal of Tropical Meteorology,2008,24(3):259-264.毛文书,王谦谦,彭骏,等.江淮梅雨特征及其与北太平洋海温的SVD分析[J].热带气象学报,2008,24(3):259-264.
[8]REYNOLDS R W,RAYNER N A,SMITH T M,et al.An improved in situ and satellite SST analysis for climate[J].Journal of Climate,2002,15(13):1 609-1 625.
[9]O'CARROLL A G,SAUNDERS R W,WATTS J G.The measurement of the sea surface temperature by satellites from 1991to2005[J].Journal of Atmospheric&Oceanic Technology,2005,23(11):1 573-1 582.
[10]WU Zhao-hua,HUANG N E.Ensemble empirical mode decomposition:A noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[11]HUANG N E,WU Zhao-hua.A Review on Hilbert-Hung transform method and its applications to geophysical studies[J].Reviews of Geophysics,2008,46(2):1-23.
[12]HUANG N E.,SHEN Zheng,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings A,1998,454:903-995.
[13]DILLING S,MACVICAR B J.Cleaning high-frequency velocity profile data with autoregressive moving average(ARMA)models[J].Flow Measurement&Instrumentation,2017,54:68-81.
[14]DONG Yin-feng,LI Y,LAI M.Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model[J].Soil Dynamics&Earthquake Engineering,2010,30(3):133-145.
[15]PORPORATO A,RIDOLFI L.Multivariate nonlinear prediction of river flows[J].Journal of Hydrology,2001,248(1-4):109-122.
[16]BROCKWELL P J.Time series:Theory and methods[M].Springer-Verlag,2015.