Coastal regions are generally densely populated and have become highly vulnerable to the occurrence of extreme events. In recent years, Brazil’s southern coastal region has been affected by several different extreme weather events that have caused coastal flooding, with economic losses as well as fatalities. Understanding and improving the predictability of these events has become a major issue for the local population. In this study, state-of-the-art numerical modeling was applied to this region to assess the ability of the Ocean-Land Atmosphere Model to represent major extreme events. The model was applied to the region with a high-resolution grid refinement technique capable of simultaneously representing global and local atmospheric phenomena. The main events that affected Brazil’s southern coastal region between 2000 and 2018 were identified and then simulated. All selected events were associated with cyclonic and/or anticyclonic systems near the coastal region of the study area. Those systems were responsible for bringing heavy rain, strong winds and sea level rise, causing impacts for the coastal region. The results of the numerical simulations were compared with observational data to evaluate model performance. The model simulated well the air temperature and wind fields. Correlation values for sea level pressure were high despite a maximum positive bias of approximately 2 hPa. Precipitation presented a negative bias for most events. Finally, the results show that the methodology allowed for a detailed representation of sensible and latent heat fluxes for the region, allowing a better representation of local mesoscale features.
A hybrid method is applied to generate a high‐resolution regional downscaling of atmospheric conditions to the southern coast of Brazil. The method consists of applying a principal component analysis to daily fields of the sea level pressure (SLP) data from the NCEP‐CFSR reanalysis. A cluster analysis (K‐means) is then applied to the 87 principal components that explained 95% of the variance of the time series. Daily atmospheric conditions were clustered into 36 weather types which represent the most predominant conditions observed in the study area. The estimated weather types were able to represent the major atmospheric systems affecting local climate, including the cyclones and anticyclones that are usually present in this region. Then, we applied the numerical Ocean‐Land‐Atmosphere Model (OLAM) to dynamically downscale the atmospheric condition that is closest to the centroid of each cluster. The model was set with a global grid and a refining approach with 6 km grid spacing over the coastal region of south Brazil. This approach allowed us to represent simultaneously the planetary waves and the local mesoscale systems, and their mutual interactions. The results provided new high‐resolution atmospheric fields for the coastal region and showed that the model was capable of resolving the major local mesoscale features. The main advantage of applying such a method is in reducing the number of numerical simulations (lower computational cost) at the same time it represents the totality of the atmospheric conditions observed in the study area. The final results consist in detailed information of the local climate that can be related to injuries to the coastal area and thus is useful to support decision‐makers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.