Recent Advances in Predictability Studies in China (1999-2002)

MU Mu; DUAN Wansuo; CHOU Jifan

doi:10.1007/BF02915570

Impact Factor: 5.8

Volume 21 Issue 3

May 2004

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2004 > 21(3): 437-443

Recent Advances in Predictability Studies in China (1999-2002)

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 Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Beijing Meteorological Training Center, China Meteorological Administration, Beijing 100081

doi: 10.1007/BF02915570

Abstract
References
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Abstract:
Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealed by NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process.
- predictability,prediction,perturbation,computational uncertainty,weather,climate
References

[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]	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
[4]	Zhong Qing, Chen Jiatian, Sun Zuoling, 2002: Elimination of Computational Systematic Errors and Improvements of Weather and Climate System Models in Relation to Baroclinic Primitive Equations, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 1103-1112. doi: 10.1007/s00376-002-0068-y
[5]	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
[6]	Yuejian ZHU, 2005: Ensemble Forecast: A New Approach to Uncertainty and Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 781-788. doi: 10.1007/BF02918678
[7]	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
[8]	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
[9]	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
[10]	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
[11]	Qian ZOU, Quanjia ZHONG, Jiangyu MAO, Ruiqiang DING, Deyu LU, Jianping LI, Xuan LI, 2023: Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 501-513. doi: 10.1007/s00376-022-1376-z
[12]	Xin LIU, Jing CHEN, Yongzhu LIU, Zhenhua HUO, Zhizhen XU, Fajing CHEN, Jing WANG, Yanan MA, Yumeng HAN, 2024: An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 545-563. doi: 10.1007/s00376-023-3035-4
[13]	Xiangdong ZHANG, Thomas JUNG, Muyin WANG, Yong LUO, Tido SEMMLER, Andrew ORR, 2018: Preface to the Special Issue: Towards Improving Understanding and Prediction of Arctic Change and Its Linkage with Eurasian Mid-latitude Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1-4. doi: 10.1007/s00376-017-7004-7
[14]	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
[15]	Wang Huijun, 1999: A Preliminary Study on the Polar Climate Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 361-366. doi: 10.1007/s00376-999-0015-2
[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]	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
[18]	Guokun DAI, Mu MU, 2020: Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 307-317. doi: 10.1007/s00376-019-9222-7
[19]	Lu WANG, Xueshun SHEN, Juanjuan LIU, Bin WANG, 2020: Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 817-831. doi: 10.1007/s00376-020-9262-z
[20]	XU Hui, DUAN Wansuo, 2008: What Kind of Initial Errors Cause the Severest Prediction Uncertainty of El Nino in Zebiak-Cane Model, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 577-584. doi: 10.1007/s00376-008-0577-4

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Manuscript History

Manuscript received: 10 May 2004

Manuscript revised: 10 May 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

Recent Advances in Predictability Studies in China (1999-2002)

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 Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Beijing Meteorological Training Center, China Meteorological Administration, Beijing 100081

Manuscript received: 2004-05-10
Manuscript revised: 2004-05-10

Abstract: Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealed by NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process.

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