文章摘要
气温的天气和气候记忆性特征分析——以济南和青岛为例
The memory characteristics of weather and climate of temperature --- Jinan and Qingdao
投稿时间:2021-06-18  修订日期:2022-03-09
DOI:
中文关键词: 济南和青岛  气候记忆性  分数阶导数
英文关键词: Jinan and Qingdao  the memory of the climate  fractional derivative
基金项目:山东省气象局科研项目(2021SDYD07);国家自然科学基金项目(11801332);山东省自然科学基金(ZR2016JL004)
作者单位邮编
刘思宇 山东省气象信息中心 250031
周雪松 山东省气象信息中心 250031
陈澍 山东省气象信息中心 250031
周笑天 山东省气象信息中心 250031
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中文摘要:
   气温的天气和气候记忆性特征分析对于提高气候预测水平具有积极意义。利用济南和青岛1961—2020年逐日、月和年平均气温资料,运用自相关性函数和标准化频率分布分析了上述时间序列的气温记忆性特征和概率分布特征,并利用结构函数法建立了月、年平均气温距平与日平均气温距平之间的分数阶导数关系。分析结果表明:1)济南和青岛的月、年平均气温距平呈现不同程度的记忆性特征,其中年平均气温距平相比于月平均气温距平具有更好的记忆性。2)济南和青岛的月、年平均气温距平与日平均气温距平之间存在分数阶导数关系,济南相应的阶数分别为0.529和0.665,青岛分别为0.553和0.791,两地的月尺度阶数相近,青岛的年尺度阶数大于济南,即青岛月和年平均气温距平的记忆性大于济南。3)济南和青岛的月和年平均气温距平相比于日平均气温距平有不同程度的长尾特征,长尾特征反映了极值温度发生的概率。
英文摘要:
   Analysis on memory characteristics of weather and climate of temperature have a positive significance for improving the level of climate prediction. Based on the daily, monthly and annual average temperature series from 1961 to 2020 in Jinan and Qingdao were used to analyze the characteristics of temperature memory and probability distribution for these time series by using autocorrelation function and normalized frequency distribution. The fractional derivative relationships between monthly, annual average temperature anomaly and daily average temperature anomaly were established by using structure function. The result shows that: 1) The monthly and annual average temperature anomalies in Jinan and Qingdao show different degrees of memory characteristics. Among them, the annual average temperature anomalies have better memory than the monthly average temperature anomalies.. 2) The results show that there is a fractional derivative relationships between monthly, annual average temperature anomaly and daily average temperature anomaly. The corresponding derivative order are Jinan 0.529 and 0.665, Qingdao 0.553 and 0.791. In month scale, the difference between Jinan and Qingdao is very slight. In year scale, the value of order in Qingdao is larger than which in Jinan. The monthly and annual average temperature anomaly of Qingdao has better memory than Jinan. 3) The monthly and annual average temperature anomalies compared with the daily average temperature anomalies in Jinan and Qingdao have different degrees of long tail characteristics, which shows the probability of extreme temperatures.
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