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Volume 11 Issue 3

Jul.  1994

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 1994 >  11(3): 277-284

Preliminary Study of Reconstruction of a Dynamic System Using an One-Dimensional Time Series

  • 1.

    Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044


doi: 10.1007/BF02658146

  • This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly mean temperature 1D time series and the assumption that the equation for the time-varying evolution of phase-space state variables contains linear and nonlinear quadratic terms, followed by the fitting of the dataset subjected to continua-tion so as to get, by the least square method, the coefficients of the terms, of which those of greater variance contribu-tion are retained for use. Results show that the obtained low-order system may be used to describe nonlinear proper-ties of the short range climate variation shown by monthly mean temperature series.
  • [1] Peng Yongqing, Yan Shaojin, Wang Tongmei, 1995: A Nonlinear Time-lag Differential Equation Model for Predicting Monthly Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 319-324.  doi: 10.1007/BF02656980
    [2] Jeong-Hyeong LEE, Keon-Tae SOHN, 2007: Prediction of Monthly Mean Surface Air Temperature in a Region of China, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 503-508.  doi: 10.1007/s00376-007-0503-1
    [3] GUO Yanjun, DING Yihui, 2011: Impacts of Reference Time Series on the Homogenization of Radiosonde Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1011-1022.  doi: 10.1007/s00376-010-9211-3
    [4] YE Liming, YANG Guixia, Eric VAN RANST, TANG Huajun, 2013: Time-Series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 382-396.  doi: 10.1007/s00376-012-1252-3
    [5] Athanassios A. ARGIRIOU, Zhen LI, Vasileios ARMAOS, Anna MAMARA, Yingling SHI, Zhongwei YAN, 2023: Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1326-1336.  doi: 10.1007/s00376-022-2246-4
    [6] WANG Geli, YANG Peicai, ZHOU Xiuji, 2013: Nonstationary Time Series Prediction by Incorporating External Forces, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1601-1607.  doi: 10.1007/s00376-013-2134-z
    [7] Zhou Jiabin, 1985: A NEW TYPE OF TIME-SERIES-FORECASTING METHOD, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 385-401.  doi: 10.1007/BF02677255
    [8] Zhang Banglin, Liu Jie, Sun Zhaobo, 1993: A New Multidimensional Time Series Forecasting Method Based on the EOF Iteration Scheme, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 243-247.  doi: 10.1007/BF02919147
    [9] Runhua Yang, William H. Klein, 1989: The Synoptic Climatology of Monthly Mean Surface Temperature in Asia in Relation to the 700 hPa Circulation, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 227-238.  doi: 10.1007/BF02658018
    [10] Yan Shaojin, Peng Yongqing, Quo Guang, 1995: Monthly Mean Temperature Prediction Based on a Multi-level Mapping Model of Neural Network BP Type, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 225-232.  doi: 10.1007/BF02656835
    [11] Yan Shaojin, Peng Yongqing, Wang Jianzhong, 1991: Determination of Kolmogorov Entropy of Chaotic Attractor Included in One-Dimensional Time Series of Meteorological Data, ADVANCES IN ATMOSPHERIC SCIENCES, 8, 243-250.  doi: 10.1007/BF02658098
    [12] A. Mary Selvam, J. S. Pethkar, M. K. Kulkarni, 1995: Some Unique Characteristics of Atmospheric Interannual Variability in Rainfall Time Series over India and the United Kingdom, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 377-385.  doi: 10.1007/BF02656987
    [13] LI Zhen, YAN Zhongwei, 2010: Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series (1960--2006), ADVANCES IN ATMOSPHERIC SCIENCES, 27, 777-787.  doi: 10.1007/s00376-009-9052-0
    [14] LI Qingxiang, DONG Wenjie, 2009: Detection and Adjustment of Undocumented Discontinuities in Chinese Temperature Series Using a Composite Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 143-153.  doi: 10.1007/s00376-009-0143-8
    [15] Silvia Alessio, Arnaldo Longhetto, Luo Meixia, 1999: The Space and Time Features of Global SST Anomalies Studied by Complex Principal Component Analysis, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 1-23.  doi: 10.1007/s00376-999-0001-8
    [16] Zhou Jiabin, Wang Yunkuan, Yang Guiying, Wu Jinsheng, 1994: A Forecasting Model of Vector Similarity in Phase Space for Flood and Drought over the Huanghe-Huaihe-Haihe Plain in China, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 224-229.  doi: 10.1007/BF02666548
    [17] Tang Maocang, 1989: Some Annual Variation Characteristics for the Northern Hemispheric Monthly Mean Precipitation Fields, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 186-201.  doi: 10.1007/BF02658015
    [18] BIAN Jianchun, YANG Peicai, 2005: Interdecadal Variations of Phase Delays Between Two Ni(n)o Indices at Different Time Scales, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 122-125.  doi: 10.1007/BF02930875
    [19] Pak-wai CHAN, Wei HAN, Betty MAK, Xiaohao QIN, Yongzhu LIU, Ruoying YIN, Jincheng WANG, 2023: Ground–Space–Sky Observing System Experiment during Tropical Cyclone Mulan in August 2022, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 194-200.  doi: 10.1007/s00376-022-2267-z
    [20] Zhen LI, Zhongwei YAN, Lijuan CAO, Phil D. JONES, 2018: Further-Adjusted Long-Term Temperature Series in China Based on MASH, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 909-917.  doi: 10.1007/s00376-018-7280-x
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    • Manuscript History

    Manuscript received: 10 July 1994
    Manuscript revised: 10 July 1994
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    Preliminary Study of Reconstruction of a Dynamic System Using an One-Dimensional Time Series

    • 1. Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044
    • Manuscript received: 1994-07-10
    • Manuscript revised: 1994-07-10

    Abstract: This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly mean temperature 1D time series and the assumption that the equation for the time-varying evolution of phase-space state variables contains linear and nonlinear quadratic terms, followed by the fitting of the dataset subjected to continua-tion so as to get, by the least square method, the coefficients of the terms, of which those of greater variance contribu-tion are retained for use. Results show that the obtained low-order system may be used to describe nonlinear proper-ties of the short range climate variation shown by monthly mean temperature series.

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