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Progress in Predictability Studies in China (2003--2006)


doi: 10.1007/s00376-007-1086-6

  • Since the last International Union of Geodesy and Geophysics General Assembly (2003), predictability studies in China have made significant progress. For dynamic forecasts, two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate, which are superior to the corresponding linear theory. A possible mechanism for the ``spring predictability barrier" phenomenon for the El Ni\~no-Southern Oscillation (ENSO) was provided based on a theoretical model. To improve the forecast skill of an intermediate coupled ENSO model, a new initialization scheme was developed, and its applicability was illustrated by hindcast experiments. Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range (monthly) prediction and successfully applied it to the monthly-scale predictability of short-term climate variations. In statistical forecasts, it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features, and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation. For ensemble forecasts, a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998. Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons. A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation. This new downscaling model showed a relatively higher score than the issued operational forecast.
  • [1] 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
    [2] MU Mu, DUAN Wansuo, XU Hui, WANG Bo, 2006: Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 992-1002.  doi: 10.1007/s00376-006-0992-3
    [3] ZHOU Feifan, DING Ruiqiang, FENG Guolin, FU Zuntao, DUAN Wansuo, 2012: Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011), ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1048-1062.  doi: 10.1007/s00376-012-1204-y
    [4] DUAN Anmin, WU Guoxiong, LIU Yimin, MA Yaoming, ZHAO Ping, 2012: Weather and Climate Effects of the Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 978-992.  doi: 10.1007/s00376-012-1220-y
    [5] 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
    [6] Wansuo DUAN, Lichao YANG, Mu MU, Bin WANG, Xueshun SHEN, Zhiyong MENG, Ruiqiang DING, 2023: Recent Advances in China on the Predictability of Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1521-1547.  doi: 10.1007/s00376-023-2334-0
    [7] DUAN Wansuo, LUO Haiying, 2010: A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 741-749.  doi: 10.1007/s00376-009-9141-0
    [8] ZHANG Jie, Zhenglong LI, Jun LI, Jinglong LI, 2014: Ensemble Retrieval of Atmospheric Temperature Profiles from AIRS, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 559-569.  doi: 10.1007/s00376-013-3094-z
    [9] Zhiyong MENG, Eugene E. CLOTHIAUX, 2022: Contributions of Fuqing ZHANG to Predictability, Data Assimilation, and Dynamics of High Impact Weather: A Tribute, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 676-683.  doi: 10.1007/s00376-021-1362-x
    [10] Se-Hwan YANG, LI Chaofan, and LU Riyu, 2014: Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 779-786.  doi: 10.1007/s00376-013-3172-2
    [11] BEI Naifang, Fuqing ZHANG, 2014: Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 995-1008.  doi: 10.1007/s00376-014-3191-7
    [12] ZHU Benlu, LIN Wantao, ZHANG Yun, 2010: Analysis Study on Perturbation Energy and Predictability of Heavy Precipitation in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 382-392.  doi: 10.1007/s00376-009-8164-x
    [13] WANG Qiang, MU Mu, Henk A. DIJKSTRA, 2012: Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 118-134.  doi: 10.1007/s00376-011-0199-0
    [14] WangHuijun, Xue Feng, Bi Xunqiang, 1997: The Interannual Variability and Predictability in a Global Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 554-562.  doi: 10.1007/s00376-997-0073-2
    [15] Jeong-Hyeong LEE, Byungsoo KIM, Keon-Tae SOHN, Won-Tae KOWN, Seung-Ki MIN, 2005: Climate Change Signal Analysis for Northeast Asian Surface Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 159-171.  doi: 10.1007/BF02918506
    [16] 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
    [17] Zeng Qingcun, Zhang Banglin, Yuan Chongguang, Lu Peisheng, Yang Fanglin, Li Xu, Wang Huijun, 1994: A Note on Some Methods Suitable for Verifying and Correcting the Prediction of Climatic Anomaly, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 121-127.  doi: 10.1007/BF02666540
    [18] Abebe Kebede, Kirsten Warrach-sagi, Thomas Schwitalla, Volker Wulfmeyer, Tesfaye Amdie, Markos Ware, 2024: Assessment of Seasonal Rainfall Prediction in Ethiopia: Evaluating a Dynamic Recurrent Neural Network to Downscale ECMWF-SEAS5 Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3345-1
    [19] DING Yihui, LIU Yiming, SHI Xueli, LI Qingquan, LI Qiaoping, LIU Yan, 2006: Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons inChina byUsing a Nested Regional ClimateModel (RegCM NCC) Part II: The Experimental Seasonal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 487-503.  doi: 10.1007/s00376-006-0323-8
    [20] Yunyun LIU, Zeng-Zhen HU, Renguang WU, Xing YUAN, 2022: Causes and Predictability of the 2021 Spring Southwestern China Severe Drought, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1766-1776.  doi: 10.1007/s00376-022-1428-4

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Manuscript History

Manuscript received: 10 November 2007
Manuscript revised: 10 November 2007
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Progress in Predictability Studies in China (2003--2006)

  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: Since the last International Union of Geodesy and Geophysics General Assembly (2003), predictability studies in China have made significant progress. For dynamic forecasts, two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate, which are superior to the corresponding linear theory. A possible mechanism for the ``spring predictability barrier" phenomenon for the El Ni\~no-Southern Oscillation (ENSO) was provided based on a theoretical model. To improve the forecast skill of an intermediate coupled ENSO model, a new initialization scheme was developed, and its applicability was illustrated by hindcast experiments. Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range (monthly) prediction and successfully applied it to the monthly-scale predictability of short-term climate variations. In statistical forecasts, it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features, and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation. For ensemble forecasts, a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998. Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons. A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation. This new downscaling model showed a relatively higher score than the issued operational forecast.

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