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首页> 外文期刊>Hydrological Processes >Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula
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Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula

机译:使用copula建模邻近地区的双变量降雨分布并生成双变量相关降雨数据

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摘要

The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North-East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision.
机译:由于相似的气候和地理特征,印度相邻气象分区的降雨模式相似。单独分析这些气象分区的降雨模式可能并不总能捕获相关性和尾部相关性。此外,单独生成多元降雨数据可能无法保持相关性。在这项研究中,使用copula方法来推导邻近气象分区的季风降雨的双变量分布。为此使用了不同的Archimedean copula,并根据非参数检验和尾部相关系数选择了最佳的copula。然后,将拟合的copula用于导出双变量分布,联合返回期和条件分布。用拟合的copula生成双变量降雨数据,并随着样本量的增加而观察到,生成的数据能够捕获相关性以及尾部相关性。该方法论通过印度东北部两个相邻的气象部门:阿萨姆邦(Assam)和梅加拉亚邦(Meghalaya)气象部门以及那加兰邦,曼尼普尔邦(Manipur),米佐拉姆(Mizoram)和Tripura气象部门的案例研究得到了证明。

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