Multi-State Markov-Switching Model to Detect Regional Heat Wave Patterns

In regional heat wave analyses, geographically close weather stations may exhibit different temperature dynamics. Considering a multi-state Bayesian Markov-switching model, we propose an analysis of the similarities and differences that emerge among six stations in Friuli Venezia Giulia. Increasing the number of states, the model can isolate periods of high and extremely high temperatures, allowing for the identification of heat wave episodes. Heterogeneity in detecting heat wave episodes emerges among stations, mainly reflecting differences and similarities in the morphological conformation of the territory.