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Volume 27 Issue 4

Jul.  2010

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(4): 845-854

The Prediction of Non-stationary Climate Series Based on Empirical Mode Decomposition

  • 1.

    Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Chinese Academy of Meteorological Sciences, Beijing 100081


doi: 10.1007/s00376-009-9128-x

  • This paper proposes a new approach which we refer to as ``segregated prediction" to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method (EMD), which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.
  • [1] 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
    [2] XIAO Ziniu, LIU Hua, ZHANG De, 2012: Progress in Climate Prediction and Weather Forecast Operations in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 943-957.  doi: 10.1007/s00376-012-1194-9
    [3] Tianbao XU, Zhicong YIN, Xiaoqing MA, Yanyan HUANG, Huijun WANG, 2023: Hybrid Seasonal Prediction of Meridional Temperature Gradient Associated with “Warm Arctic–Cold Eurasia”, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1649-1661.  doi: 10.1007/s00376-023-2226-3
    [4] Wang Huijun, Zhou Guangqing, Lin Zhaohui, Zhao Yan, Guo Yufu, Ma Zhuguo, 2001: Recent Researches on the Short-Term Climate Prediction at IAP－A Brief Review, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 929-936.
    [5] WU Lingyun, ZHANG Jingyong, 2015: The Relationship between Spring Soil Moisture and Summer Hot Extremes over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1660-1668.  doi: 10.1007/s00376-015-5003-0
    [6] Zhizhen XU, Jing CHEN, Mu MU, Guokun DAI, Yanan MA, 2022: A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1432-1450.  doi: 10.1007/s00376-022-1341-x
    [7] 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
    [8] Hongqin ZHANG, Xiangjun TIAN, Wei CHENG, Lipeng JIANG, 2020: System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP): System Formulation and Preliminary Evaluation, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1267-1284.  doi: 10.1007/s00376-020-9252-1
    [9] Wang Shaowu, Zhu Jinhong, 2001: A Review on Seasonal Climate Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 197-208.  doi: 10.1007/s00376-001-0013-5
    [10] Weiwei WANG, Song YANG, Tuantuan ZHANG, Qingquan LI, Wei WEI, 2022: Sub-seasonal Prediction of the South China Sea Summer Monsoon Onset in the NCEP Climate Forecast System Version 2, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1969-1981.  doi: 10.1007/s00376-022-1403-0
    [11] Mu Mu, Duan Wansuo, Wang Jiacheng, 2002: The Predictability Problems in Numerical Weather and Climate Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 191-204.  doi: 10.1007/s00376-002-0016-x
    [12] WANG Huijun, FAN Ke, SUN Jianqi, LI Shuanglin, LIN Zhaohui, ZHOU Guangqing, CHEN Lijuan, LANG Xianmei, LI Fang, ZHU Yali, CHEN Hong, ZHENG Fei, 2015: A Review of Seasonal Climate Prediction Research in China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 149-168.  doi: 10.1007/s00376-014-0016-7
    [13] Peng Yongqing, Zhu Yufeng, Yan Shaojin, 1994: Preliminary Study of Reconstruction of a Dynamic System Using an One-Dimensional Time Series, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 277-284.  doi: 10.1007/BF02658146
    [14] Wang Huijun, Zhou Guangqing, Zhao Yan, 2000: An Effective Method for Correcting the Seasonal-Interannual Prediction of Summer Climate Anomaly, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 234-240.  doi: 10.1007/s00376-000-0006-9
    [15] Nathan SNOOK, Qinghong ZHANG, 2016: A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1102-1103.  doi: 10.1007/s00376-016-6170-3
    [16] Lili LEI, Yangjinxi GE, Zhe-Min TAN, Yi ZHANG, Kekuan CHU, Xin QIU, Qifeng QIAN, 2022: Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1816-1832.  doi: 10.1007/s00376-022-1444-4
    [17] Li Maicun, Yao Dirong, 1985: SOME RESULTS OF APPLICATIONS OF STATISTICAL METHOD TO CLIMATE CHANGES AND SHORT-TERM CLIMATE PREDICTION IN CHINA, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 271-281.  doi: 10.1007/BF02677243
    [18] ZHOU Feifan, MU Mu, 2012: The Time and Regime Dependencies of Sensitive Areas for Tropical Cyclone Prediction Using the CNOP Method, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 705-716.  doi: 10.1007/s00376-012-1174-0
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    [20] JIANG Zhina, MU Mu, 2009: A Comparison Study of the Methods of Conditional Nonlinear Optimal Perturbations and Singular Vectors in Ensemble Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 465-470.  doi: 10.1007/s00376-009-0465-6
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    • Manuscript History

    Manuscript received: 10 July 2010
    Manuscript revised: 10 July 2010
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    The Prediction of Non-stationary Climate Series Based on Empirical Mode Decomposition

    • 1. Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Chinese Academy of Meteorological Sciences, Beijing 100081
    • Manuscript received: 2010-07-10
    • Manuscript revised: 2010-07-10

    Abstract: This paper proposes a new approach which we refer to as ``segregated prediction" to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method (EMD), which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.

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