The purpose of this study is to analyze the dynamic/thermodynamic features of the center and periphery of upper tropospheric cyclonic vortices (UTCV) in Northeast Brazil (NEB). An algorithm was developed to identify the vortex climatology using ERA-Interim, while rainfall impact was assessed using the Climate Prediction Center dataset, from 1980 to 2016. The algorithm was validated by comparison UTCV’s positions obtained by subjective analysis, showing good performance regarding positioning and seasonality. One of the preferential regions of UTCV occurrence was the semiarid region of NEB, which may indicate these systems have a strong influence on the water deficit in the region. Air subsidence and mass divergence at the center and upward motions and convergence on the periphery of the system were observed. Two conceptual skew-t diagrams were generated and the results bring a new perspective about UTCV thermodynamic features, some of which are: in the center, associated with subsidence, low convective available potential energy (CAPE) values and low humidity concentration were detected in a large atmospheric layer due to the subsidence generated by mass convergence at high levels. In contrast, the edge region presented high CAPE values and high atmospheric moisture content, related to the mass convergence in lower troposphere. The advances in knowledge about dynamic/thermodynamic characteristics of this meteorological system generate perspectives for better simulations, improving its predictability. In addition, the proposed algorithm is of great value for future climatological studies of UTCV.

本研究的目的是分析巴西东北部（NEB）上对流层气旋涡旋（UTCV）中心和外围的动力/热力学特征。开发了一种算法来识别使用ERA-Interim的涡旋气候学，同时使用气候预测中心数据集评估1980年至2016年的降雨影响。 该算法通过比较主观分析获得的UTCV位置进行验证，显示出良好的定位和性能季节性。UTCV 发生的优先地区之一是 NEB 的半干旱地区，这可能表明这些系统对该地区的缺水有很强的影响。观测到中心的空气下沉和质量发散以及系统外围的向上运动和收敛。生成了两个概念性 skew-t 图，结果带来了关于 UTCV 热力学特征的新视角，其中一些是：在中心，与沉降相关，低对流可用势能 (CAPE) 值和低湿度浓度被检测到大气层是由高空的质量会聚产生的下沉造成的。相比之下，边缘区域呈现出高 CAPE 值和高大气水分含量，这与对流层低层的质量收敛有关。该气象系统动态/热力学特性知识的进步为更好的模拟提供了前景，提高了其可预测性。此外，所提出的算法对未来UTCV的气候学研究具有重要价值。