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Main Authors: Gaetan, Carlo, Girardi, Paolo, Musau, Victor Muthama
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2402.10545
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author Gaetan, Carlo
Girardi, Paolo
Musau, Victor Muthama
author_facet Gaetan, Carlo
Girardi, Paolo
Musau, Victor Muthama
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2402_10545
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Spatial quantile clustering of climate data
Gaetan, Carlo
Girardi, Paolo
Musau, Victor Muthama
Methodology
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.
title Spatial quantile clustering of climate data
topic Methodology
url https://arxiv.org/abs/2402.10545