论文总字数：27420字

目 录

1 引言 5

2 数据来源及预处理 6

2.1 数据来源 6

2.2缺测数据处理 6

2.3气温的均一性检验 6

2.4气温订正 12

3 长江沿岸特大城市的CDD指数特征分析 13

3.1 CDD指数的逐月对比分析 14

3.2 CDD指数的区域对比分析 21

3.3 CDD指数的小波分析 24

4 结论与讨论 31

参考文献 33

致谢 34

长江沿岸特大城市基于温度的天气衍生品特征分析

吴丹

, China

Abstract : In this paper, firstly, the daily temperature data of Chongqing,Wuhan, Nanjing, Shanghai and other stations from 1961 to 2017 are tested for homogeneity with sliding t test and standard normality homogenization test (SNHT method), then a stepwise multiple linear regression method is used for revising the air temperature after the discontinuities in the air temperature sequence, the cooling degree day (CDD) from April to October in Chongqing, Wuhan, Nanjing, and Shanghai was calculated based on the corrected temperature data. The CDD index of the four places was analyzed with the Morlet wavelet analysis method and the M-K test method,combining the linear trend, coefficient of variation, relative variability of climatic tendency and other statistics in the meantime. The results are as follows: （1）On the change of time, the average CDD index of Chongqing, Wuhan, Nanjing and Shanghai from April to October increased from April to July gradually and decreased from August to October gradually, the trend of variation in CDD index was opposite. The rising trend of CDD index in August was the smallest from April to October, and the mutation in rising trend of CDD index appeared in the 1990s mainly; (2) In terms of spatial change, the average CDD index of four places in April and May decreased continuously from west to east in the geographical position. The overall upward trend of the CDD index was ranked as follows: Shanghaigt; Wuhangt; Beijing gt;Chongqing; The correlation of CDD index between Nanjing and Shanghai is very good, the correlation coefficient is around 0.9, and the correlation between Chongqing and the other three places is poor, the correlation coefficient is around 0.6, and the relevance of CDD index from April and October in four places was the worst in June; (3) On the cycle change, there was a three-year cycle for the CDD index in August for these four cities and the CDD index of Nanjing and Shanghai in July still has a 3.5-year cycle. The CDD index of Wuhan in July has a three-year cycle; The time scale of cycle with the most intense oscillations of the CDD index in Nanjing and Shanghai are the same.

Key words: weather derivatives; CDD index; Morlet wavelet analysis; Mann-Kendall test.

1 引言

每当提及天气风险时，人们通常可能会认为是台风、洪水、暴雨、冰雹等灾难性天气所带来的影响，而实际上，一般的天气事件也会带来一定的风险，并且发生的概率更高。例如酷暑会使居民用电量增加，电力行业会因此获益，但酷暑也会导致人们出行意愿的降低，旅游行业会因此受到负面影响，这就是一般天气风险，天气风险给天气敏感行业带来了收入的不稳定性，在寻找避险渠道的需求下，天气衍生性商品应运而生。

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