中国极端温度事件的检测方法
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摘要
近几十年来,极端气候事件对国民经济和生态环境造成了严重影响,引起了人们越来越多的关注.联合国政府间气候变化专门委员会(IPCC)的评估报告中就提及了极端温度事件.对极端温度的研究集中在对其定义的分析.马柱国等在1951-2003年1月和7月平均温度的基础上添加一个修订值作为极端温度的阈值.Andrew (1999)等定义连续2-5 d内最高温度大于或等于给定温度阈值则为极端温度事件.另外,用各个不同的指数分析极端温度事件也是一个主要研究方向,主要指数有:冷夜、暖夜、冷日、暖日、热浪持续指数、寒潮持续指数、霜冻指数、极端温度指数等.除此之外,目前的研究还关注极端温度事件的发生趋势.全球气候正经历一次以变暖为主要特征的显著变化.已有的研究表明,全球陆面温度的升高过程中多数地区的最低温度升高明显,其变化幅度高于最高温度的升高,因而表现出一种日夜增暖的不对称性.我国近几十年的日最高温度略有增加,最低温度显著增加.在最近40—50年中,部分区域极端最低温度和平均最低温度有明显上升,尤其以北方冬季更为突出.
本文讨论了稳健均值与稳健中值方法及滑动窗口稳健均值与稳健中值方法检测极端高温和极端低温事件,并将改进后的方法与传统的方法进行比较说明.
第二章介绍了累积量的定义、性质及三阶不变量、四阶不变量的估计算法,另外介绍了有关Cornish- Fisher展开的简单知识和应用.
第三章给出了稳健均值和稳健中值的检测方法,对我国近50年的夏季和冬季的最高温度进行实际计算,并从时间和空间角度来比较说明改进方法的优势.
第四章中类似地定义了滑动窗口的稳健均值和滑动窗口的稳健中值检测方法,并通过数值模拟说明改进后的方法也比之前的好,最后也对我国近50年的夏季和冬季的最高温度进行实例计算分析极端高温、低温事件的变化趋势.
In recent decades, extreme climate events caused serious influence on the national economy and environment, so it attracts growing interests. The Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC) refers to the effects of extreme temperature events. Research on extreme temperature focuses on the analysis of its definition. Ma Zhuguo et al added a revised value as the threshold value of the extreme temperature which based on January and July average temperature in the 1951-2003 year. Andrew et al (1999) defined that if the maximum temperature is greater than or equal to a given threshold value of temperature within 2-5 days, then we can say it is an extreme event. In addition, analysis of the extreme temperature events using different indices is also a major research direction. The main indices include cold night, warm night, cold day, warm day, the index of continuous heat wave, the index of continuous cold wave, frost index, and extreme temperature index and so on. In addition, the trend of the extreme temperature is also studied. The main feature of global climate is becoming warm. Existing studies indicated that the lowest temperature rise significantly in most regions as the temperature of the global surface land increase, and the lowest temperature changes faster than the maximum temperature. Therefore it demonstrated an asymmetry of warming day and warming night. In the recent decades, the maximum temperature increased slightly, and the minimum temperature increased significantly. In the last 40-50 years, the extreme minimum temperature and average minimum temperature of some regions increased significantly, especially in winter of the north in Chine.
This dissertation discussed the robust mean value method and median value method, the robust mean and median detection method of sliding window to detect the extreme high and low temperature events. We compared the improved methods with traditional methods.
Chapter 2 described the definition and property of the cumulant and the estimation algorithm of three-order invariant and four-order invariant. Meanwhile the Cornish-Fisher expansion was introduced.
Chapter 3 gave a robust mean detection method and a robust median detection method. We actually calculated the maximum temperature of the summer and winter over the past 50 years in China. The advantages of the improved methods were illustrated from the perspective of time and space.
Chapter 4 defined a robust mean detection method and a robust median detection method of sliding window similarly. The improved method is better than the other methods through numerical simulation. At last we used new detection method to calculate the maximum temperature of the summer and winter over the past 50 years in China, and analyzed the changing trend of extreme high and low temperature events.
本文讨论了稳健均值与稳健中值方法及滑动窗口稳健均值与稳健中值方法检测极端高温和极端低温事件,并将改进后的方法与传统的方法进行比较说明.
第二章介绍了累积量的定义、性质及三阶不变量、四阶不变量的估计算法,另外介绍了有关Cornish- Fisher展开的简单知识和应用.
第三章给出了稳健均值和稳健中值的检测方法,对我国近50年的夏季和冬季的最高温度进行实际计算,并从时间和空间角度来比较说明改进方法的优势.
第四章中类似地定义了滑动窗口的稳健均值和滑动窗口的稳健中值检测方法,并通过数值模拟说明改进后的方法也比之前的好,最后也对我国近50年的夏季和冬季的最高温度进行实例计算分析极端高温、低温事件的变化趋势.
In recent decades, extreme climate events caused serious influence on the national economy and environment, so it attracts growing interests. The Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC) refers to the effects of extreme temperature events. Research on extreme temperature focuses on the analysis of its definition. Ma Zhuguo et al added a revised value as the threshold value of the extreme temperature which based on January and July average temperature in the 1951-2003 year. Andrew et al (1999) defined that if the maximum temperature is greater than or equal to a given threshold value of temperature within 2-5 days, then we can say it is an extreme event. In addition, analysis of the extreme temperature events using different indices is also a major research direction. The main indices include cold night, warm night, cold day, warm day, the index of continuous heat wave, the index of continuous cold wave, frost index, and extreme temperature index and so on. In addition, the trend of the extreme temperature is also studied. The main feature of global climate is becoming warm. Existing studies indicated that the lowest temperature rise significantly in most regions as the temperature of the global surface land increase, and the lowest temperature changes faster than the maximum temperature. Therefore it demonstrated an asymmetry of warming day and warming night. In the recent decades, the maximum temperature increased slightly, and the minimum temperature increased significantly. In the last 40-50 years, the extreme minimum temperature and average minimum temperature of some regions increased significantly, especially in winter of the north in Chine.
This dissertation discussed the robust mean value method and median value method, the robust mean and median detection method of sliding window to detect the extreme high and low temperature events. We compared the improved methods with traditional methods.
Chapter 2 described the definition and property of the cumulant and the estimation algorithm of three-order invariant and four-order invariant. Meanwhile the Cornish-Fisher expansion was introduced.
Chapter 3 gave a robust mean detection method and a robust median detection method. We actually calculated the maximum temperature of the summer and winter over the past 50 years in China. The advantages of the improved methods were illustrated from the perspective of time and space.
Chapter 4 defined a robust mean detection method and a robust median detection method of sliding window similarly. The improved method is better than the other methods through numerical simulation. At last we used new detection method to calculate the maximum temperature of the summer and winter over the past 50 years in China, and analyzed the changing trend of extreme high and low temperature events.
引文
[1] Meehl G A, Karl T R, Easterlling D R et al.An introduction to trends in extreme weather and climate events:Observations, socioeconomic impacts, terrestrial ecological impacts, and model projections [J]. Bull Amer Meteor Soc, 2000, 81 (3): 413-416.
[2] Easterlling D R, Evens J L, Groisman P Ya. Observed variability and trends in extreme climate events:A brief review [J]. Bull Amer Meteor Soc, 2000, 81 (3):417-424.
[3]么枕生,丁裕国.气候统计[M].北京:气象出版社,1990:48-51.
[4]曲延禄,阎书源,张程道.我国近地面气温极值合地面最大风速极值的渐近分布[J].气象学报,1988,46(2):187-193.
[5]章大全,钱忠华.稳健中值检测方法研究近50年中国极端气温变化趋势[J].物理学报,2008,57(7):4634-4640.
[6]丁一汇,任国玉,石广玉等.气候变化国家评估报告(I):中国气候变化的历史和未来趋势[J].气候变化研究进展,2006,(2):3-8.
[7]唐国利,任国玉.近百年中国地表气温变化趋势的再分析[J].气候与环境研究,2005,10(4):791-798.
[8] Zhao Zong ci, Akimasa Sumi, Chikako Harada et a1.Projections of extreme temperature over East Asia for the 21st century as simulated by the CCSR/NIES2 coupled model, eds by WMO and CMA, Proceedings of International Symposium on Climate Change [M].Beijing: China Meteorological Press,2003:158-164.
[9]丁一汇,任国玉,赵宗慈,徐影,罗勇,李巧萍,张锦.中国气候变化的检测及预估[J].沙漠及绿洲气象,2007,1(1):1-10.
[10] Meehl G A, Zwiers F W, Evans J, Knutson T, Mearns L and Whetton P. Trends in extreme weather and climate events: issues related to modeling extremes in projections of future climate change [J]. Bulletin of the American Meteorological Society.2000, 81(3):427-436.
[11] Hampel F R. The breakdown points of the mean combined with some rejection rules [J].Technometrics, 1985, 27(2):95-107.
[12] Beirlant J, Goegebeur Y, Segers J, Teugels J, De Waal D, Ferro C. Statistics of Extremes [M]. Wiley, Chichester, 2004.
[13] Coles S. An Introduction to Statistical Modeling of Extreme Values [M]. London: Springer, 2001.
[14] Embrechts P, Kluppelberg C, Mikosch T. Modeling Extremal Events for Insurance and Finance [M]. Springer, Berlin, 1997.
[15] Reiss R D, Thomas M. Statistical Analysis of Extreme Values [M]. Birkhauser,Basel, 1997.
[16] Lanzante J R. Resistant, robust and non-parametric techniques for the analysis of climate data: theory and examples, including applications to historical radiosonde station data [J]. International Journal of Climatology.1996, 16(11):1197-1226.
[17]茆诗松,程依明,濮晓龙.概率论与数理统计教程[M].北京:高等教育出版社,2004.
[18]韦博成.参数统计教程[M].北京:高等教育出版社,2006.
[19]田应辉,阳妮,冷志魁.概率论与数理统计[M].北京:高等教育出版社,2002.
[20]宗序平.概率论与数理统计[M].北京:机械工业出版社,2004.
[21] Johnson N L, Kotz S and Balakrishnan N. Continuous Univariate Distributions [M]. John Wiley, NewYork, 1994.
[22] Stuart A and Ord J K. Kendall's Advanced Theory of Statistics, 6th ed [M].London:Charles Griffin, 1994.
[23]吴纯杰,王兆军.基于Cornish-Fisher展开的稳健综合控制图设计[J].应用概率统计,2009,25(3):257-265.
[24] Chan L K, Cui H J. Skewness correction X and R charts for skewed distributions [J].Naval Research Logistics, 2003, 50:1-19.
[25] Bai D S, Choi L S. X and R charts for skewed populations [J]. Journal of Quality Technology, 1995, 27:120-131.
[26]张仁铎.空间变异理论及应用[M].北京:科学出版社,2005.
[27]徐钟济.蒙特卡洛方法[M].上海:上海科技出版社,1985:1-50.
[28]丁裕国,张耀存.降水气候特征的随机模拟试验[J].南京气象学院学报,1989,12(2):146-154.
[29] Manfred M.CLIM-X-DETECT: A Fortran 90 program for robust detection of extremes against a time-dependent background in climate records [M] . Computers & Geosciences,2006,32:141-144.
[30]丁一汇,任国玉.气候变化国家评估报告[M].北京:科学出版社,2007.
[31]任国玉,徐铭志,初子莹,郭军,李庆祥,刘小宁,王颖.近54年中国地面气温变化[J].气候与环境研究,2005,10(4):717-727.
[32]马柱国,符淙斌,任小波等.中国北方年极端温度的变化趋势与区域增高温的联系[J].地理学报,2003,58(增刊):11-20.
[33] Andrew F, David E, Bryan W. Climate variability and the frequency of extreme temperature events for nine sites across Canada: implications for power usage [J].Journal of Climate,1999,12(7):2490-2502.
[34] Vincent L A,Peterson T C,Barros V R,et a1. Observed Trend’s in the Indices of daily temperature extremes in south America 1960-2000 [J].Journal of Climate,2005,12(8):5011-5023.
[2] Easterlling D R, Evens J L, Groisman P Ya. Observed variability and trends in extreme climate events:A brief review [J]. Bull Amer Meteor Soc, 2000, 81 (3):417-424.
[3]么枕生,丁裕国.气候统计[M].北京:气象出版社,1990:48-51.
[4]曲延禄,阎书源,张程道.我国近地面气温极值合地面最大风速极值的渐近分布[J].气象学报,1988,46(2):187-193.
[5]章大全,钱忠华.稳健中值检测方法研究近50年中国极端气温变化趋势[J].物理学报,2008,57(7):4634-4640.
[6]丁一汇,任国玉,石广玉等.气候变化国家评估报告(I):中国气候变化的历史和未来趋势[J].气候变化研究进展,2006,(2):3-8.
[7]唐国利,任国玉.近百年中国地表气温变化趋势的再分析[J].气候与环境研究,2005,10(4):791-798.
[8] Zhao Zong ci, Akimasa Sumi, Chikako Harada et a1.Projections of extreme temperature over East Asia for the 21st century as simulated by the CCSR/NIES2 coupled model, eds by WMO and CMA, Proceedings of International Symposium on Climate Change [M].Beijing: China Meteorological Press,2003:158-164.
[9]丁一汇,任国玉,赵宗慈,徐影,罗勇,李巧萍,张锦.中国气候变化的检测及预估[J].沙漠及绿洲气象,2007,1(1):1-10.
[10] Meehl G A, Zwiers F W, Evans J, Knutson T, Mearns L and Whetton P. Trends in extreme weather and climate events: issues related to modeling extremes in projections of future climate change [J]. Bulletin of the American Meteorological Society.2000, 81(3):427-436.
[11] Hampel F R. The breakdown points of the mean combined with some rejection rules [J].Technometrics, 1985, 27(2):95-107.
[12] Beirlant J, Goegebeur Y, Segers J, Teugels J, De Waal D, Ferro C. Statistics of Extremes [M]. Wiley, Chichester, 2004.
[13] Coles S. An Introduction to Statistical Modeling of Extreme Values [M]. London: Springer, 2001.
[14] Embrechts P, Kluppelberg C, Mikosch T. Modeling Extremal Events for Insurance and Finance [M]. Springer, Berlin, 1997.
[15] Reiss R D, Thomas M. Statistical Analysis of Extreme Values [M]. Birkhauser,Basel, 1997.
[16] Lanzante J R. Resistant, robust and non-parametric techniques for the analysis of climate data: theory and examples, including applications to historical radiosonde station data [J]. International Journal of Climatology.1996, 16(11):1197-1226.
[17]茆诗松,程依明,濮晓龙.概率论与数理统计教程[M].北京:高等教育出版社,2004.
[18]韦博成.参数统计教程[M].北京:高等教育出版社,2006.
[19]田应辉,阳妮,冷志魁.概率论与数理统计[M].北京:高等教育出版社,2002.
[20]宗序平.概率论与数理统计[M].北京:机械工业出版社,2004.
[21] Johnson N L, Kotz S and Balakrishnan N. Continuous Univariate Distributions [M]. John Wiley, NewYork, 1994.
[22] Stuart A and Ord J K. Kendall's Advanced Theory of Statistics, 6th ed [M].London:Charles Griffin, 1994.
[23]吴纯杰,王兆军.基于Cornish-Fisher展开的稳健综合控制图设计[J].应用概率统计,2009,25(3):257-265.
[24] Chan L K, Cui H J. Skewness correction X and R charts for skewed distributions [J].Naval Research Logistics, 2003, 50:1-19.
[25] Bai D S, Choi L S. X and R charts for skewed populations [J]. Journal of Quality Technology, 1995, 27:120-131.
[26]张仁铎.空间变异理论及应用[M].北京:科学出版社,2005.
[27]徐钟济.蒙特卡洛方法[M].上海:上海科技出版社,1985:1-50.
[28]丁裕国,张耀存.降水气候特征的随机模拟试验[J].南京气象学院学报,1989,12(2):146-154.
[29] Manfred M.CLIM-X-DETECT: A Fortran 90 program for robust detection of extremes against a time-dependent background in climate records [M] . Computers & Geosciences,2006,32:141-144.
[30]丁一汇,任国玉.气候变化国家评估报告[M].北京:科学出版社,2007.
[31]任国玉,徐铭志,初子莹,郭军,李庆祥,刘小宁,王颖.近54年中国地面气温变化[J].气候与环境研究,2005,10(4):717-727.
[32]马柱国,符淙斌,任小波等.中国北方年极端温度的变化趋势与区域增高温的联系[J].地理学报,2003,58(增刊):11-20.
[33] Andrew F, David E, Bryan W. Climate variability and the frequency of extreme temperature events for nine sites across Canada: implications for power usage [J].Journal of Climate,1999,12(7):2490-2502.
[34] Vincent L A,Peterson T C,Barros V R,et a1. Observed Trend’s in the Indices of daily temperature extremes in south America 1960-2000 [J].Journal of Climate,2005,12(8):5011-5023.
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