ABSTRACT: Numerical models of the heat index time series and spatio-temporal fields can be used for a variety of purposes, from the study of the dynamics of heat waves to projections of the influence of future climate on humans. To conduct these studies one must have efficient numerical models that successfully reproduce key features of the real weather processes. In this study, 2 numerical stochastic models of the spatio-temporal non-Gaussian field of the average daily heat index (ADHI) are considered. The field is simulated on an irregular grid determined by the location of weather stations. The first model is based on the method of the inverse distribution function. The second model is constructed using the normalization method. Real data collected at weather stations located in southern Russia are used to both determine the input parameters and to verify the proposed models. It is shown that the first model reproduces the properties of the real field of the ADHI more precisely compared to the second one, but the numerical implementation of the first model is significantly more time consuming. In the future, it is intended to transform the models presented to a numerical model of the conditional spatio-temporal field of the ADHI defined on a dense spatio-temporal grid and to use the model constructed for the stochastic forecasting of the heat index.

中文翻译：

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俄罗斯南部平均日热指数的时空分布的随机模拟

摘要：从研究热浪动态到预测未来气候对人类的影响，热指数时间序列和时空场的数值模型可以用于多种目的。要进行这些研究，必须拥有能够成功重现真实天气过程关键特征的有效数值模型。在这项研究中，考虑了平均日热指数（ADHI）的时空非高斯场的两个数值随机模型。在由气象站位置确定的不规则网格上模拟场。第一个模型基于逆分布函数的方法。使用归一化方法构造第二个模型。在位于俄罗斯南部的气象站收集的实际数据既可用于确定输入参数，也可用于验证所提出的模型。结果表明，与第二个模型相比，第一个模型更精确地再现了ADHI实场的属性，但是第一个模型的数字实现明显耗时。将来，打算将呈现的模型转换为在密集的时空网格上定义的ADHI的条件时空场的数值模型，并将该模型用于热指数的随机预测。但是第一个模型的数值实现明显耗时。将来，打算将呈现的模型转换为在密集的时空网格上定义的ADHI的条件时空场的数值模型，并将该模型用于热指数的随机预测。但是第一个模型的数值实现明显耗时。将来，打算将呈现的模型转换为在密集的时空网格上定义的ADHI的条件时空场的数值模型，并将该模型用于热指数的随机预测。