ABSTRACT Synoptic‐scale weather systems are key drivers of extreme nearshore wave conditions, which, when interacting with intensive coastal urbanization, significantly increase the risk of coastal erosion and associated socio‐economic and environmental damages. Understanding the dominant weather patterns (WPs) linked to coastal hazards is essential for improving forecasting capabilities and enabling proactive disaster risk reduction. This study identifies the primary WPs associated with coastal disasters along the Santa Catarina coast, Southern Brazil, between 1998 and 2020. Disaster dates were derived from the Disaster Information Form (FIDES) and the Damage Assessment Form (AVADAN), resulting in a validated dataset of 105 coastal disaster events. A clustering approach based on the K‐Means algorithm was applied to atmospheric and oceanographic variables, including geopotential height at 1000 hPa, zonal and meridional wind components (from ERA5 reanalysis), and significant wave height (Hs) from WAVERYS. The analysis identified five distinct WPs responsible for driving coastal disasters in the region, predominantly associated with cyclonic and anticyclonic systems. The most recurrent pattern was linked to a cyclonic system near the southern Brazilian coast, producing high‐energy wave events affecting multiple coastal sectors. Another critical WP was characterized by the persistence of an intensified high‐pressure system, generating widespread impacts across the entire coastline. Seasonal variability was evident, with autumn recording the highest frequency of disaster events. The spatial distribution of damages revealed that the northern sector, despite experiencing lower wave heights, was more vulnerable due to the lack of coastal natural protective features. In contrast, the southern sector, protected by extensive dune systems, reported fewer but economically significant impacts. The findings underscore the importance of integrating synoptic‐scale pattern recognition into early warning systems and coastal management practices, providing a scientific basis for risk‐informed decision‐making, disaster preparedness, and adaptation strategies.
Leal et al. (Mon,) studied this question.