Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused floods in most parts of the state, affecting 2,398,255 people and leading to 183 deaths and 27 missing persons. Due to the severity of this episode, we need to understand its drivers. In this context, the main objective of this study is a multi-scale analysis of the extreme precipitation between 26 April and 5 May, i.e., an analysis of the large-scale patterns of the atmosphere, a description of the synoptic environment, and an analysis of the mesoscale viewpoint (cloud-top features and lightning). Data from different sources (reanalysis, satellite, radar, and pluviometers) were used in this study, and different methods were applied. The National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) registered accumulated rainfall above 400 mm between 26 April and 5 May using 27 pluviometers located in the central-northern part of RS. The monthly volumes reached 667 mm and 803 mm, respectively, for April and May 2024, against a climatological average of 151 mm and 137 mm for these months. The maximum precipitation recorded was 300 mm in a single day on 30 April 2024. From a large-scale point of view, an anomalous heat source in the western Indian Ocean triggered a Rossby wave that contributed to a barotropic anticyclonic anomalous circulation over mid-southeastern Brazil. While the precipitant systems were inhibited over this region (the synoptic view), the anomalous stronger subtropical jet southward of the anticyclonic circulation caused uplift over RS State and, consequently, conditions leading to mesoscale convective system (MCS) development. In addition, the low-level jet east of the Andes transported warm and moist air to southern Brazil, which also interacted with two cold fronts that reached RS during the 10-day period, helping to establish the precipitation. Severe deep MCSs (with a cloud-top temperature lower than −80 °C) were responsible for a high lightning rate (above 10 flashes km⁻² in 10 days) and accumulated precipitation (above 600 mm in 10 days), as observed by satellite measurements. This high volume of rainfall caused an increase in soil moisture, which exceeded a volume fraction of 0.55, making water infiltration into the soil difficult and, consequently, favoring flood occurrence.

This paper analyzes the cloud microphysics in different layers of storms as a function of three‐dimensional total lightning density. A mobile X‐band polarimetric radar and very high frequency (VHF) sources from Lightning Mapping Array (LMA) observations during the 2011/2012 Brazil spring‐summer were used to determine the microphysical signatures of radar vertical profiles and lightning density. This study quantified the behavior of 5.3 million vertical profiles of the horizontal reflectivity (Z_H), differential reflectivity (Z_DR), specific differential phase (K_DP), and correlation coefficient (ρ_HV). The principal changes in the polarimetric variables occurred only for VHF source rate density greater than 14 VHF sources per km² in 4 min. These storms showed an enhanced positive K_DP in the mixed 1 layer (from 0 to −15°C) probably associated with supercooled liquid water signatures, whereas regions with negative Z_DR and K_DP and moderate Z_H in the mixed 2 layer (from −15 to −40°C) were possibly associated with the presence of conical graupel. The glaciated (above −40°C) and upper part of the mixed 2 layers showed a significant trend to negative K_DP with an increase in lightning density, in agreement with vertical alignment of ice particle by the cloud electric field. A conceptual model that presents the microphysical signatures in storms with and without lightning activity was constructed. The observations documented in this study provide an understanding of how the combinations of polarimetric variables could help to identify storms with different lightning density and vice versa.

The number of natural disasters triggered by extreme events is increasing worldwide and significantly impacts modern society. Extreme rainfall is one of the most important factors contributing to these events. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. The primary purpose of this study is to describe the performance of satellite products for extreme rainfall events that caused natural disasters in various climate regimes in Brazil and discuss the contribution of mesoscale convective systems (MCS) to these events. We defined regions based on the climatological rainfall distribution. Cases with rain values above the 99th percentile during 2012–2016 were considered statistically extreme. Our analysis is based on three datasets, with precipitation from (i) rain gauge stations, (ii) different satellite-based estimates, and (iii) mesoscale convective tracking data. The methodology was based on identifying extreme rainfall events, analyzing the performance of satellite precipitation estimates and, finally, quantifying the influence of convective systems on extreme rain. Although all regions of Brazil may be affected by natural disasters caused by extreme rains, the results suggest that the impacts caused in each region are different in magnitude. Convective systems explained over 90% of extreme rains in the case analyzed in Brazil’s south and about 60% to 90% of extreme rains in the case analyzed in the Northeast. In general, satellite products have identified rain events; however, in the southern region of Brazil, products have tended to overestimate rainfall, while other regions have tended to underestimate extreme rain values. The methods used in satellite precipitation estimation products have limitations to accurately identifying specific extreme rain events.

At the end of June 2020, an explosive extratropical cyclone was responsible for an environment in which a squall line developed and caused life and economic losses in Santa Catarina state, southern Brazil. The aims of this case study are the following: (a) to describe the drivers of the cyclogenesis; (b) to investigate through numerical simulations the contribution of sea–air interaction to the development of the cyclone as an explosive system; and (c) to present the physical properties of the clouds associated with the squall line. The cyclogenesis started at 1200 UTC on 30 June 2020 on the border of southern Brazil and Uruguay, having a trough at middle-upper levels as a forcing, which is a common driver of cyclogenesis in the studied region. In addition, the cyclone’s lifecycle followed Bjerknes and Solberg’s conceptual model of cyclone development. A special feature of this cyclone was its fast deepening, reaching the explosive status 12 h after its genesis. A comparison between numerical experiments with sensible and latent turbulent heat fluxes switched on and off showed that the sea–air interaction (turbulent heat fluxes) contributed to the cyclone’s deepening leading it to the explosive status. The cold front, which is a component of the cyclone, favored the development of a pre-frontal squall line, responsible for the rough weather conditions in Santa Catarina state. While satellite images do not clearly show the squall line located ahead of the cold front in the cyclone wave due to their coarse resolution, radar reflectivity data represent the propagation of the squall line over southern Brazil. On 30 June 2020, the clouds in the squall line had more than 10 km of vertical extension and a reflectivity higher than 40 dBZ in some parts of the storm; this is an indicator of hail and, consequently, is a required condition for storm electrification. In fact, electrical activity was registered on this day.