摘要
天气和气候要素时间序列特征及关系的研究,对于提高极端天气和极端气候事件影响的认识,以及提高模拟及预报预测极端天气气候事件,都会起到积极的作用。利用沈阳市1951~2010年的日平均气温资料,经处理后得到不同时间尺度(日、月和年)的平均气温距平时间序列,分别代表着平均气温距平的时间序列和气候序列,运用自相关函数和归一化概率密度函数分析了上述时间序列的自相关性和概率密度函数的长尾特征,在此基础上,利用二阶结构函数建立了月、年平均气温距平与日平均气温距平的分数阶导数关系,即气候时间序列和天气时间序列之间的分数阶导数关系。研究结果表明:沈阳日、月、年平均气温距平时间序列分别呈现出无记忆性、短期记忆性和长期记忆性的特征;相比日、月平均气温距平序列,年平均气温距平序列的归一化概率密度函数呈现出明显的长尾特征,意味着气候极端事件发生的概率要大于天气极端事件发生的概率。这些结果表明,月、年平均气温距平序列(气候要素时间序列)与日平均气温距平序列(天气要素时间序列)之间存在着分数阶导数关系,计算出相应的导数阶数分别为0.524和0.83。
Research on characteristics of time series of weather and climate factors and theirrelationship will improve our understanding on effect of weather and climate extreme events, and willenhance our abilityon simulation and prediction of weather and climate extreme events. In this paper, the average air temperature anomaly time series with different time scales(daily,monthly and yearly scales) from 1951 to 2010 in Shenyang city were selected,which represents the time series of weather and climate factors. The characteristics of autocorrelation and long-trail probability distribution for these time series was analyzed by using the autocorrelation function and the normalized probability density function. Furthermore, thefractional derivative relationships between monthly, yearly average air temperature anomaly and daily average air temperature anomaly were established by using second order structure function,i.e., the fractional order derivative relationship between the time series of climate and weather factors. The results showed that the time series of daily, monthly, and yearly average air temperature anomaly in Shenyang city presented the characteristics of non-memory, short-term memory and long-term memory, respectively. The normalized probability function of yearlyaverage air temperature anomaly series showed obvious long-tail trait, compared to that of daily and monthly air average temperature anomaly series. Which means that the probability of climate extreme events is greater than that of weather extreme events.These results suggested that there were fractional derivative relationships between the monthly,yearlyaverage air temperature anomaly series(time series of climate elements)and daily average air temperature anomaly series(time series of weather elements), the calculated order ofderivative were 0.524 and 0.83,respectively.
引文
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