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The statistical quantification of temperature processes for the analysis of urban heat island (UHI) effects and local heat-waves is an increasingly important application domain in smart city dynamic modelling. This leads to the increased importance of real-time heatwave risk management on a fine-grained spatial resolution. This study attempts to analyze and develop new methods for modelling the spatio-temporal behavior of ground temperatures. The developed models consider higher-order stochastic spatial properties such as skewness and kurtosis, which are key components for understanding and describing local temperature fluctuations and UHI's. The developed models are applied to the greater Tokyo metropolitan area for a detailed real-world data case study. The analysis also demonstrates how to statistically incorporate a variety of real data sets. This includes remotely sensed imagery and a variety of ground-based monitoring site data to build models linking city and urban covariates to air temperature. The air temperature models are then used to capture high-resolution spatial emulator outputs for ground surface temperature modelling. The main class of processes studied includes the Tukey g-and-h processes for capturing spatial and temporal aspects of heat processes in urban environments.

Spatiotemporal Analysis of Urban Heatwaves Using Tukey G-and-H Random Field Models