As global warming progresses, it is increasingly important to monitor and analyse spatio-temporal patterns of heat waves and other extreme climate-related events that impact urban areas. In this work, we present a novel dynamic spatio-temporal model by combining a state space model (SSM) and a generalised hyperbolic distribution to flexibly describe a spatial–temporal profile of the tail behaviour, skewness and kurtosis of the local urban temperature distribution of the greater Tokyo metropolitan area. Such a model can be used to study local dynamics of temperature effects, specifically those that characterise extreme heat or cold. The focus of the application in this paper will be heat wave events in the greater Tokyo metropolitan area which is known to be prone to some of the most severe heat wave events that have one of the largest population exposures due to high density living in Tokyo city. The advantages the proposed model offers are as follows: it accommodates skewed and fat-tail distributions for temperature profiles; the model can be expressed as a location-scale linear Gaussian SSM which allows the development of an efficient Monte Carlo mixture Kalman Filter solution for the estimation. The proposed model is compared with the Gaussian SSM through application to maximum temperature data in the Tokyo metropolitan area between 1978 - 2016. The result suggests that the proposed model estimates the temperature distribution more accurately than the conventional linear Gaussian SSM and that the predictive variance of our method tends to be smaller than that obtained from the conventional spate time linear Gaussian SSM benchmark model.

随着全球变暖的进展，监测和分析影响城市地区的热浪和其他极端气候相关事件的时空模式变得越来越重要。在这项工作中，我们提出了一种新颖的动态时空模型，通过结合状态空间模型（SSM）和广义双曲分布来灵活描述局部城市温度分布的尾部行为、偏度和峰度的时空轮廓。大东京都市区。这样的模型可用于研究温度效应的局部动态，特别是那些以极热或极冷为特征的模型。本文应用的重点将是大东京都市区的热浪事件，众所周知，该地区容易发生一些最严重的热浪事件，由于东京市的高密度生活，这些热浪事件是人口暴露最多的地区之一。所提出的模型具有以下优点：它适应温度分布的偏斜和厚尾分布；该模型可以表示为位置尺度线性高斯 SSM，它允许开发有效的蒙特卡罗混合卡尔曼滤波器解决方案进行估计。通过应用 1978 年至 2016 年东京都市区的最高气温数据，将所提出的模型与高斯 SSM 进行比较。