A new multi-expert distance for clustering climate parameters: a Caribbean precipitation case study

Abstract

This study investigates precipitation patterns in the Caribbean region using a novel Multi-Expert Distance (MED) metric for clustering analysis. MED integrates multiple climate parameters, including Sea Surface Temperature (SST), wind components at 925 hPa, and Outgoing Longwave Radiation (OLR), with the objective of enhancing spatiotemporal precipitation analysis. This approach offers an alternative to conventional methods that rely on single datasets and Euclidean distances. It combines physical parameters during clustering to enhance accuracy and insights. The analysis encompasses a 43-year period (1979–2021), extending from the Gulf of Mexico to the Caribbean, with a spatial extent that covers the entire region. The MED metric incorporates zone-specific histograms and Kullback-Leibler divergence, enabling dynamic comparisons of atmospheric configurations. The analysis yielded six distinct clusters, each exhibiting unique seasonal and inter-annual precipitation patterns, influenced by regional atmospheric dynamics. The analysis revealed significant transitions and associations between clusters, precipitation levels, and atmospheric conditions. Clusters representing dry conditions exhibited negative SST anomalies, reflecting reduced moisture production. Conversely, clusters exhibiting high precipitation exhibited positive SST anomalies, which are conducive to moisture accumulation. Furthermore, tropical storms and hurricanes were predominantly observed in wetter clusters, underscoring the utility of MED in linking atmospheric phenomena with climatic impacts. The results highlight the effectiveness of the MED in improving both the accuracy and interpretability of clustering algorithms. Beyond its methodological contributions, this work highlights the MED's potential to advance the understanding and forecasting of precipitation regimes, thereby contributing to more robust climate analyses. Such insights are particularly relevant for informing climate adaptation strategies in vulnerable regions, notably the Caribbean. Future research could investigate automated domain segmentation as a means of further refining and optimizing this approach.