Spatial quantile clustering of climate data
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910334066884608 |
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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 |