Extreme Low Temperature In Winter To Return To The Long-range Correlation And The Probability Distribution Of The Time

In nature and social life, the occurrence of extreme events is very frequent. For example, the recurrences of flood, drought, earthquake and economic recession are extreme events. However, their occurrences are not what we want, as they often bring disasters to lives and property. Therefore, the study issues related to extreme events are necessary. Since many natural events have long-range correlation (ie, show the characteristics of long-term sustainability), if the extreme event series are also associated with long-range correlation, then can use this property directly to predict and assess the extreme events. In order to better predict extreme events, also can study the return time of extreme events, the long-range correlations of return time series with the correlations of original time series are closely related. If the return time series are associated with long-range correlation, then can through the prediction of return time to achieve the purpose of prediction of extreme events. In addition to long-range correlation, the probability distribution of the return time of extreme events is extremely important to describe and understand physical phenomena. Therefore, in recent years, scientists are working on the probability distribution of the return time with the long-range correlation. In the numerical simulation, this kind of return time series is subject to "stretched" exponential distribution.The article is on the basis of the existed research results, further study the long-range correlation and the probability distribution of return time. In the text, the research is object to the return time of extreme low temperature of daily minimum temperature in winter:study the long-range correlation and the probability distribution, also use the Kolmogorov-Smirnov fit test to test its distribution. In the first chapter, it introduces the problem put forward, research status and the main research work; in the second chapter, it introduces the basic knowledge of the long-range correlation and probability distribution of return time; in the third chapter, it introduces the fit test, this is the preparation for the fourth chapter; in the fourth chapter, first, it uses the trended fluctuation analysis to study the long-range correlations of winter daily minimum temperature series and return time series, and reaches this conclusion:The long-range correlation of return time series is weaker than correlation of winter daily minimum temperature series, and in China, both two have the same strength distribution, the long-range correlation of return time series with the correlation of winter daily minimum temperature series are closely related; then, using the Kolmogorov-Smirnov fit test to test the probability distribution of return time with long-range correlation, found when the return time are smaller than the mean return time, this kind of return time series is not subject to the probability distribution which is obtained in the numerical simulation, but it is subject to the probability distribution of a and b are the undetermined coefficients. In the fifth chapter, the predictability of return time series is simply discussed.