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基于近极值事件广义态密度估计方法，针对1961-2013年中国近极端温度异常变化事件构建了近极值广义态密度的参数，定义rp为近极端温度变化异常事件最概然强度，max为近极端温度变化异常事件最大聚集程度，研究了1961-2013年中国近极端温度异常变化事件的聚集特征.结果表明：夏季，西北地区西部、华南地区以及西南地区南部，当日最高温度变化量达到离高温正距平阈值1.0-2.8℃并达到44%的聚集程度时，应及时给出极端高温异常增温事件的预警信息；在华南地区、西南地区南部以及西藏地区，当出现日最高温度变化量高于高温负距平阈值0.5-2.5℃的近极端高温异常降温事件并达到34%的聚集程度时，下一时刻出现极端高温异常降温事件的概率最大.冬季，对于西南地区，当日最低温度变化量达到离低温正距平阈值1.0-2.0℃并达到32%的聚集程度时，下一时刻最有可能发生极端低温异常增温事件；西南地区，华南地区和江南地区当日最低温度变化比低温负距平阈值高1.0-4.0℃时近极端低温异常降温事件会聚集发生.因此近极端温度变化异常事件最概然强度rp和近极端温度变化异常事件最大聚集程度max在一定程度上给出了下一刻极端温度异常变化事件的预警信息.The events near their extreme values are termed nearly extreme events. The generalized density of states is proposed that is defined by a probability density function. The rate of nearly-extreme events to the total sample size at a given point is the crowding of nearly extreme events, which is an important index used in many fields. Based on the estimation of the generalized state density of nearly extreme events, the parameters of the generalized state density of nearly-extreme anomalous temperature events are constructed with the temperature daily maximum data in summer and daily minimum data recorded in China in winter in 1961-2013. The daily maximum and minimum temperatures recorded at 174 observed stations in 1961-2013 are selected based on the requirement of data continuity from the climate dataset over China, released by the China Meteorological Administration. According to the analysis of the single station Nanjing, the maximum probability density of occurrence about nearly extremely anomalous temperature is marked as max and the corresponding r of max is marked as rp, which indicates that when the difference between nearly extremely anomalous events and extremely anomalous events is rp, the probability of occurrence is maximum. Then rp is defined as the most probable intensity of nearly extremely anomalous temperature events. max and rp can show the crowding degree characteristics about nearly extremely anomalous temperature events and can carry significant physical meanings in the practical application. So the spatial distribution characteristics of max and rp about nearly extremely anomalous temperature events in China in summer and winter are analyzed respectively. In summer, in the west part of Northwest China, South China and south part of Southwest China easily happen the extremely warming events when the most probable intensity of nearly extremely warming temperature event rp values are 1.0℃ and 2.8℃ and the maximum probability density of occurrence about nearly extremely warming temperature max is up to 44%. In South China, south part of Southwest China and Xizang easily occur the extremely cooling events when the most probable intensity of nearly extremely cooling temperature event rp values are 0.5℃ and 2.5℃ and the maximum probability density of occurrence about nearly extremely cooling temperature max is up to 34%. In winter, the warning information about extremely warming events should give to Southwest China when the most probable intensity of nearly extremely warming temperature events rp values are 1℃ and 2℃ and the maximum probability density of occurrence about nearly extremely warming temperature max is up to 32%. The warning information about extremely cooling events should give to Southwest China, South China and south part of the Yangtze River when the most probable intensity of nearly extremely cooling temperature events rp are 1.0℃ and 4.0℃. Therefore, the maximum probability density of occurrence max and the most probable intensity rp of nearly extremely anomalous temperature events can give some early warning information about the coming extremely anomalous temperature events.
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Keywords:
- generalized state density of nearly extremely events /
- the most probable intensity /
- the maximum crowding degree /
- nearly extremely anomalous temperature events
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[23] Bonsal B R, Zhang X B, Vincent L A 2001J. Clim. 14 1959
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[1] Houghton J T, Ding Y, Griggs D J 2007Contribution of Working Group I to the Third Assessment Report of the Intergovemmental Panel Climate Change (Cambridge:Cambridge University Press) p156
[2] Wei F Y, Cao H X, Wang L P 2003J. Appl. Meteo. Sci. 14 79(in Chinese)[魏凤英, 曹鸿兴, 王丽萍2003应用气象学报14 79]
[3] Gong Z Q, Zhao J H, Feng G L, Chou J F 2015Sci. China:Ear. Sci. 58 404
[4] Gong Z Q, Feng G L, Ren F M, Li J P 2013Theor. Appl. Climatol. 7 1
[5] Qian Z H, Hou W, Yang P, Feng G L 2011Acta Phys. Sin. 60 109204(in Chinese)[钱忠华, 侯威, 杨萍, 封国林2011 60 109204]
[6] Gong Z Q, Wang X J, Zhi R, Feng G L 2009Acta Phys. Sin. 58 4342(in Chinese)[龚志强, 王晓娟, 支蓉, 封国林2009 58 4342]
[7] Redner S, Petersen M R 2006Phys. Rev. E 74 061114
[8] Coles S 2001An Introduction to Statistical Modeling of Extreme Values (London:Springer-Verlag) pp45-72
[9] Katz R W, Parlange M B, Naveau P 2002Adv. Water Res. 25 1287
[10] Piani C, Haerter J O, Coppola E 2010Theor. Appl. Climatol. 99 187
[11] Pakes A G, Steutel F W 1997Aust. J. Stat. 13 255
[12] Katz R W, Brown B G 1992Clim. Change 21 289
[13] Sabhapandit S, Majumdar S N 2007Phys. Rev. Lett. 98 4055
[14] Burkhardt T W, Gyrgyi G, Moloney N R, Rcz Z 2007Phys. Rev. E 76 041119
[15] Buchholz D, Wichmann E H 1986Commun. Math. Phys. 106 321
[16] Lin J G, Huang C, Zhuang Q Y, Zhu L P 2010Insur. Math. Econ. 47 13
[17] Yan Z W, Yang C 2000Climat. Environ. Res. 5 267(in Chinese)[严中伟, 杨赤2000气候与环境研究5 267]
[18] Pan X H, Zhai P M 2002Meteorol. Month. 28 28(in Chinese)[潘晓华, 翟盘茂2002气象28 28]
[19] Wang H J, Zhou G Q, Zhao Y 2000J. Appl. Meteor. Sci. 11 40(in Chinese)[王会军, 周广庆, 赵彦2000应用气象学报11 40]
[20] Shi L, Ni Y Q 2001Acta Meteor. Sin. 59 685(in Chinese)[史历, 倪允琪2001气象学报59 685]
[21] Liu W W, An S Q, Liu G S, Guo A H 2003Meteorol. Month. 29 14(in Chinese)[刘巍巍, 安顺清, 刘庚山, 郭安红2003气象29 14]
[22] Tang M C, Zhang J, Wang J X 1987Plat. Meteorol. 6 150(in Chinese)[汤懋苍, 张建, 王敬香1987高原气象6 150]
[23] Bonsal B R, Zhang X B, Vincent L A 2001J. Clim. 14 1959
[24] Wang Z C 2008Statistic and Thermodynamic Physics (4th Ed.) (Beijing:China Higher Education Press) p256(in Chinese)[汪志诚2008热力学统计物理(第4版) (北京:高等教育出版社)第256页]
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