Spatial quantile clustering of climate data

Carlo Gaetan (), Paolo Girardi () and Victor Muthama Musau ()
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Carlo Gaetan: Università Ca’ Foscari
Paolo Girardi: Università Ca’ Foscari
Victor Muthama Musau: Kirinyaga University

Advances in Data Analysis and Classification, 2025, vol. 19, issue 1, No 7, 147-175

Abstract: Abstract In the era of climate change, the distribution of climate variables evolves with changes not limited to the mean value. Consequently, clustering algorithms based on central tendency could produce misleading results when used to summarize spatial and/or temporal patterns. We present a novel approach to spatial clustering of time series based on quantiles using a Bayesian framework that incorporates a spatial dependence layer based on a Markov random field. A series of simulations tested the proposal, then applied to the sea surface temperature of the Mediterranean Sea, one of the first seas to be affected by the effects of climate change.

Keywords: Asymmetric Laplace distribution; Markov random field; Model-based clustering; Time series; 91C20; 60G60; 62P12; 37M10; 62F15 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-024-00580-y

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