Classification of synoptic weather patterns associated with extreme wave events in different regions of Western South Atlantic

Matheus Bonjour Laviola Silva (), Danilo Couto Souza (), Fernando Tulio Camilo Barreto (), Rodrigo Tecchio (), Renan Freitas Pimentel dos Anjos (), Carolina Barnez Gramcianinov () and Ricardo Camargo ()
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Matheus Bonjour Laviola Silva: University of São Paulo
Danilo Couto Souza: University of São Paulo
Fernando Tulio Camilo Barreto: OceanPact Serviços Marítimos
Rodrigo Tecchio: University of São Paulo
Renan Freitas Pimentel dos Anjos: University of São Paulo
Carolina Barnez Gramcianinov: Helmholtz-Zentrum Hereon
Ricardo Camargo: University of São Paulo

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 8, No 35, 9853-9877

Abstract: Abstract The identification of weather patterns and associated surface waves for the Southwestern Atlantic Ocean is the goal of the present work. For this purpose, a K-means algorithm was adopted to group data into similar atmospheric conditions considering 25 years of reanalysis data (1993-2017) of zonal and meridional wind components and geopotential height at 1000 hPa. Three points (Vitoria, Santos and Rio Grande) along the Brazilian coast were chosen to evaluate the wave extremes and which Weather Patterns are associated with the extremes in these three different regions. The knee point detection method was used to determine the ideal number of centroids for representing the Weather Patterns at each point. The dates corresponding to each WP were used to plot the average wave field associated with each WP. The results indicate that WPs are dominated by both cyclones and anti-cyclones in the domain. Cyclones with a south/southwest fetch induce extreme waves in Santos and Rio Grande, while for Vitoria, extreme wave generation is more dominant due to the influence of the post-frontal high.

Keywords: Weather Patterns; Machine Learning; K-means; Extreme Waves (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07166-7

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