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.

中文翻译：

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巴西南部沿海地区附近主要极端气象事件的海陆大气模型 (OLAM) 性能

摘要：沿海地区普遍人口稠密，极易发生极端事件。近年来，巴西南部沿海地区多次受到极端天气事件的影响，造成沿海洪水泛滥，造成经济损失和人员伤亡。了解和提高这些事件的可预测性已成为当地居民面临的主要问题。在这项研究中，最先进的数值模拟被应用于该地区，以评估海陆大气模型代表重大极端事件的能力。该模型采用能够同时表示全球和局部大气现象的高分辨率网格细化技术应用于该地区。确定并模拟了 2000 年至 2018 年间影响巴西南部沿海地区的主要事件。所有选定的事件都与研究区沿海地区附近的气旋和/或反气旋系统有关。这些系统造成大雨、强风和海平面上升，对沿海地区造成影响。将数值模拟的结果与观测数据进行比较，以评估模型性能。该模型很好地模拟了气温和风场。尽管最大正偏差约为 2 hPa，但海平面压力的相关值很高。降水对大多数事件呈现负偏差。最后，结果表明该方法可以详细表示该地区的感热和潜热通量，