ABSTRACT: 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.
KEY WORDS: Numerical modeling · Cyclone · Anticyclone · Extreme precipitation · Extreme winds · Model validation · Coastal flooding · Hurricane Catarina
Full text in pdf format Supplementary material | Cite this article as: de Souza DC, Ramos da Silva R
(2021) Ocean-Land Atmosphere Model (OLAM) performance for major extreme meteorological events near the coastal region of southern Brazil. Clim Res 84:1-21. https://doi.org/10.3354/cr01651
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