海表面温时间序列的相关性及复杂性研究
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摘要
海洋表面温度是海洋热力、动力过程以及海气相互作用的综合结果,是海洋环境一个重要的参数。为了获得海洋表面温度时空变化的复杂性行为并揭示其内在动力性机制,根据一个改进的样本熵的方法来分析海表面温时间序列的复杂性;同时使用去趋势波动分析方法研究海表面温时间序列的长记忆性。实验结果表明,高纬度地区的海表面温变化的复杂度低于低纬度地区。因为高纬度地区海表面温度的季节性更加明显,序列的规则性更强。相对来说季节性因素对低纬度地区海表面温度的影响不大。不管是长时间序列还是短时间序列高纬度地区的海表面温都具有长记忆性,且序列的不平稳性很强。但是短时间序列的低纬度地区的海表面温的相关性呈现出多种情况,序列足够长时,序列也表现出长记忆性。
Sea surface temperature,as an important parameter of marine environment,is the comprehensive result of ocean thermal and dynamic processes as well as air sea interaction.In order to obtain the complexity of the temporal and spatial variation of ocean surface temperature and reveal its intrinsic dynamic mechanism,the complexity of the sea surface temperature time series is analyzed based on an improved sample entropy method,and the long-term memory of that is studied by the method of detrended fluctuation analysis.The experiment shows that the complexity of sea surface temperature change in high latitudes is lower than that in low latitudes.Because the seasonality of sea surface temperature is more obvious in high latitudes,and the sequence is more regular.Generally speaking,seasonal factors have little effect on sea surface temperature in low latitudes.For long time series or short time series,the sea surface temperature in high latitudes is long-term memory and the sequence is not stable.However,the correlation of sea surface temperature at low latitudes in short time series shows many situations.When the sequence is long enough,the sequence also shows long-term memory.
Sea surface temperature,as an important parameter of marine environment,is the comprehensive result of ocean thermal and dynamic processes as well as air sea interaction.In order to obtain the complexity of the temporal and spatial variation of ocean surface temperature and reveal its intrinsic dynamic mechanism,the complexity of the sea surface temperature time series is analyzed based on an improved sample entropy method,and the long-term memory of that is studied by the method of detrended fluctuation analysis.The experiment shows that the complexity of sea surface temperature change in high latitudes is lower than that in low latitudes.Because the seasonality of sea surface temperature is more obvious in high latitudes,and the sequence is more regular.Generally speaking,seasonal factors have little effect on sea surface temperature in low latitudes.For long time series or short time series,the sea surface temperature in high latitudes is long-term memory and the sequence is not stable.However,the correlation of sea surface temperature at low latitudes in short time series shows many situations.When the sequence is long enough,the sequence also shows long-term memory.
引文
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[3]彭婕.中国近海海表面温度日变化及其影响数值模拟研究[D].北京:国家海洋环境预报研究中心,2013.
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[6]成娟,陈勋,彭虎.基于样本熵的肌电信号起始点检测研究[J].电子学报,2016,44(2):479-484.
[7]COSTA M,GOLDBERGER A L,PENG C K.Multiscale entropy to distinguish physiologic and synthetic RR time series[J].Computers in Cardiology,2002,29:137-140.
[8]HURST H E.Long term storage capacity of reservoirs[J].Transactions of the American Society of Civil Engineers,1951,116:776-808.
[9]PENG C K,BULDYREV S V,GOLDBERGER A L,et al.Long-range correlations in nucleotide sequences[J].Nature,1992,356(6365):168-170.
[10]PENG C K,BULDYREV S V,HAVLIN S,et al.Mosaic organization of DNA nucleotides[J].Physical Review E Statistical Physics Plasmas Fluids&Related Interdisciplinary Topics,1994,49(2):1685-1689.
[11]程静,刘光远.基于情感心电信号的去趋势波动分析研究[J].西南大学学报:自然科学版,2016,38(2):169-175.
[12]李文菁.基于去趋势波动分析的中国气温变化趋势研究[D].湘潭:湘潭大学,2016.
[13]李大夜.基于分形方法的金融市场长记忆性研究[D].北京:对外经济贸易大学,2017.
[14]莫淑红,吕继强,沈冰,等.基于去趋势波动分析的降雨演变特性研究[J].西安理工大学学报,2010,26(2):148-151.
[15]张美兰,金炜东,孙永奎,等.基于多重分形去趋势波动分析的高速列车运行状态识别方法[J].计算机应用研究,2015,32(10):2978-2980.
[16]夏佳楠.时间序列的多尺度不可逆性和复杂度研究[D].北京:北京交通大学,2017.