In the present study two extreme events that occurred in the East Coast of Northeast Brazil (ENEB) during 2022 and 2023 were evaluated. These events are becoming increasingly frequent in all regions of Brazil, associated with significant material and human losses, emphasizing the significance of a deeper comprehension of these events. ERA5 global reanalysis data, GOES-16 satellite imagery and pluviometric stations were used for the analysis. Model simulations were also conducted using the Model for Prediction Across Scales (MPAS) with variable resolution (60–3 km). Both events corresponded to Easterly Wave Disturbances (EWDs) that occurred under opposite large-scale conditions of the ENSO cycle, since extreme events are becoming increasingly frequent in all regions of Brazil and could be responsible for significant material and human losses. Thus, an emphasis was given to characterize the synoptic conditions. Both analyzed cases occurred along the ENEB, specifically over the Alagoas state. The trough axis penetrating the studied area was observed on both examined dates, with a very characteristic relative vorticity of this tropical disturbance. In general, moisture convergence resulted from the high flow of moisture prevailing over the region combined with upward movements caused by the trough present at low levels, which combined with local factors in the region such as topography, contributed to the increase in rainfall over the study area in both analyzed cases. The MPAS showed excellent spatial representation when compared to station data, highlighting intense precipitation over parts of Alagoas.

中文翻译：

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巴西东北部东海岸极端降水事件：天气研究和 MPAS 模拟


在本研究中，评估了 2022 年和 2023 年在巴西东北部东海岸 (ENEB) 发生的两起极端事件。这些事件在巴西所有地区变得越来越频繁，造成重大物质和人员损失，凸显了更深入理解这些事件的重要性。 ERA5 全球再分析数据、GOES-16 卫星图像和雨量测量站用于分析。模型模拟还使用具有可变分辨率（60-3 公里）的跨尺度预测模型（MPAS）进行。这两个事件都对应于在 ENSO 周期的相反大规模条件下发生的东风波扰动 (EWD)，因为极端事件在巴西所有地区变得越来越频繁，并可能造成重大物质和人员损失。因此，重点是描述天气状况。所分析的两个案例都发生在 ENEB 沿线，特别是阿拉戈斯州。在两个检查日期都观察到了穿过研究区域的槽轴，这种热带扰动具有非常典型的相对涡度。一般来说，水分汇聚是由于该地区盛行的高水分流量加上低层出现的槽引起的向上运动，再加上该地区地形等当地因素，导致研究期间降雨量增加两个分析案例中的面积。与台站数据相比，MPAS 显示出出色的空间代表性，突出显示了阿拉戈斯部分地区的强烈降水。