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Volume 21 Issue 2

Mar.  2004

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2004 >  21(2): 296-299

On Spatiotemporal Series Analysis and Its Application to Predict the Regional Short Term Climate Process

  • 1.

    Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029


doi: 10.1007/BF02915717

  • Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.
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    [2] 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
    [3] 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
    [4] 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
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    [7] 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
    [8] 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
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    [11] 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
    [12] XIA Zhiye, CHEN Hongbin, XU Lisheng, WANG Yongqian, 2015: Extended Range (10-30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1583-1591.  doi: 10.1007/s00376-015-4252-2
    [13] 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
    [14] ZHANG Hanbin, CHEN Jing, ZHI Xiefei, WANG Yi, WANG Yanan, 2015: Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1143-1155.  doi: 10.1007/s00376-015-4232-6
    [15] Xia LIU, Qiang WANG, Mu MU, 2018: Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1362-1371.  doi: 10.1007/s00376-018-8003-z
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    • Manuscript History

    Manuscript received: 10 March 2004
    Manuscript revised: 10 March 2004
    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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    On Spatiotemporal Series Analysis and Its Application to Predict the Regional Short Term Climate Process

    • 1. Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029
    • Manuscript received: 2004-03-10
    • Manuscript revised: 2004-03-10

    Abstract: Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

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