Vertical Structure of Heavy Rainfall Events in Brazil

Abstract

Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulation within 1 h. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, one case study focusing on polarimetric variables is included. The results reveal that the generating system (synoptic or mesoscale) of intense rain events significantly influences the rainfall pattern, mainly in the South, Southeast, and Midwest regions. Regional CFADs unveil primary convective columns with 40–50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely, specific warnings.

1. Introduction

Brazil is a vast country with a north–south extent of 4394 km. Consequently, its five regions are distributed across a wide range of latitudes, ranging from 5° N to 33° S, and, as a result, each region has a distinct climatic regime, as illustrated by [1].

A significant portion of precipitating systems that define the pluviometric regime of a region gives rise to episodes of intense rainfall, often considered extreme. However, there is no consensus regarding the definition of the term in the literature, as it varies depending on data availability, study area, and the objectives of each research. For instance, works such as [2,3,4,5] use fixed thresholds to characterize episodes of intense rainfall. The authors of [6] define extreme rainfall for the Southern region of Brazil when the 50 mm isohyet encompasses a minimum area of 10,000 km². Refs. [7,8] define an extreme event as when daily precipitation exceeds a certain percentage of the seasonal climatological total for a specific station. Additionally, some authors choose to assess intense rainfall through the use of percentiles, such as [9,10,11]. This methodology is widely used when identifying precipitation extremes based on typical precipitation patterns in a given region.

Although several methods exist to define it, a significant portion of studies on the subject consider daily-scale precipitation. However, many events associated with flash floods, runoff, or rapid inundations occur over a short period. Studies are scarce regarding intense rainfall events on a sub-daily scale in Brazil, and existing studies focus on specific regions, such as [11]. Therefore, there is still a need for a more comprehensive evaluation of events occurring over a short period across the country to identify similar characteristics and possibly enhance predictability.

With the inclusion of meteorological radars, particularly those with dual polarization, the understanding of the microphysical structure of precipitation improves in the absence of in situ measurements and microphysical processes within observed precipitation [12]. This improvement results from the availability of new variables capable of inferring additional parameters compared to single-polarization (conventional) radars [13]. Over the last ten years, Brazil has acquired a network of polarimetric radars covering parts of the country, although the majority are still of the conventional type. Despite limitations in observations of physical cloud processes with conventional radars (though not nonexistent), the country lacks information on the subject, necessitating its inclusion in research.

In this context, the main objective of this study is to investigate the structure and microphysical aspects associated with clouds generating intense rainfall over Brazil, leading to high accumulation in a very short period (1 h). Thus, the study explores the possibility of any patterns in the clouds that may generate such events. However, it is important to note that this study does not differentiate between synoptic systems responsible for event generation to truly ascertain whether there is any similarity between all types of systems. Therefore, this study can be conducted for different regions of Brazil, regardless of the meteorological systems affecting them.

Finally, it is worth highlighting the innovative content in this study. No other works in the country have assessed the structure of intense rainfall events in the very short term using countrywide, polarimetric radar data. Therefore, a significant portion of the results is limited to comparison and discussion with the international literature.

2. Materials and Methods

2.1. Data and Study Area

The data used to identify intense rainfall events were obtained from automatic rain gauges provided by the National Institute of Meteorology (INMET), with a frequency of 1-h intervals, covering the period from 2016 to 2020, totaling five years of data. Additionally, Constant Altitude Plan Position Indicator (CAPPI) data from nine S-band polarimetric radars (2–4 GHz) operated by the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) were employed. Given that the polarimetric radar network covers only specific regions of Brazil, additional single-polarization S-band radars from two other agencies were incorporated: six from the Department of Airspace Control (DECEA) and four from the Amazon Protection System (SIPAM). Consequently, this study focuses on areas delineated by the radar coverage, which is distributed across all regions of Brazil (Figure 1). The National Institute for Space Research (INPE) provided the data for each radar.

Table 1. S-band radars operated by CEMADEN, DECEA, and SIPAM used for the analyses.

Radar	UF	Polarization	Frequency
Almenara/CEMADEN	MG	Dual-polarization	10 min
Jaraguari/CEMADEN	MS	Dual-polarization	12 min
Maceio/CEMADEN	AL	Dual-polarization	10 min
Natal/CEMADEN	RN	Dual-polarization	10 min
Petrolina/CEMADEN	PE	Dual-polarization	10 min
Salvador/CEMADEN	BA	Dual-polarization	10 min
Santa Tereza/CEMADEN	ES	Dual-polarization	10 min
São Francisco/CEMADEN	MG	Dual-polarization	10 min
Três Marias/CEMADEN	MG	Dual-polarization	10 min
Canguçu/DECEA	RS	Single polarization	10 min
Gama/DECEA	GO	Single polarization	10 min
Morro da Igreja/DECEA	SC	Single polarization	10 min
Pico do Couto/DECEA	RJ	Single polarization	10 min
Santiago/DECEA	RS	Single polarization	12 min
São Roque/DECEA	SP	Single polarization	10 min
Manaus/SIPAM	AM	Single polarization	12 min
Santarém/SIPAM	PA	Single polarization	12 min
São Luiz/SIPAM	MA	Single polarization	12 min
Belém/SIPAM	PA	Single polarization	12 min

lists the 19 radars utilized for the analyses in this study.

2.2. Case Selection and Validation

The careful selection of case studies is essential for the accurate analysis of intense precipitation events. In this study, we employed a method to identify and select relevant cases, considering both the location of meteorological stations and the recorded rainfall intensity.

We initially applied a filter to select INMET stations situated within the radar domains for identifying intense rainfall events. To calculate the distance between two points on a sphere based on their geographical coordinates, we employed the Haversine formula, represented by Equation (1).

$$D=2Rarcsin\sqrt{si{n}^2\left({\displaystyle \frac{{\varphi }_2-{\varphi }_1}{2}}\right)+cos\left({\varphi }_1\right)cos\left({\varphi }_2\right)si{n}^2\left({\displaystyle \frac{{\lambda }_{lon,2}-{\lambda }_{lon,1}}{2}}\right)}$$

(1)

where D is the distance in km, R is the radius of the Earth, $\varphi$ is the latitude, and ${\lambda }_{lon}$ is the longitude.

To determine the INMET stations within the radar coverage area, the distance between the stations and the central points of the radars was calculated, considering that the stations must be within a specific range to avoid analysis issues. Stations too close to the radar may fall within the blind cone area, where no data are available. Conversely, if the selected cases are too far from the radar center, only information from the upper parts of the clouds might be available. Based on [14], the upper distance limit was set to 150 km to obtain more accurate data, and the lower limit was calculated according to the elevations of each radar, resulting in a range of 50 to 150 km. Figure 1 shows the stations that comply with these thresholds, ensuring that the complete vertical profile of the clouds is effectively analyzed.

Moreover, we established a fixed threshold of 40 mm/h to classify rainfall as intense, regardless of the region where it occurs, thereby enabling an analysis focused on the clouds’ capacity to generate high-intensity precipitation. Using these criteria, we selected 385 cases of intense rainfall for detailed analysis.

To validate the 385 pre-selected rainfall cases, we compared the INMET station records with radar data. We created a 5 × 5 km area around the station and analyzed the radar reflectivity within this area over the course of one hour, as shown in Figure 2. If any pixel during this period registered a reflectivity ≥ 40 dBZ, the case was validated. The time with the highest reflectivity within this hour was recorded as the PMAX. After validation, 196 cases were excluded for not meeting the reflectivity threshold or due to the lack of available radar data.

2.3. Vertical Structure

To analyze the vertical structure of rain clouds in different regions of Brazil, we used the CFAD methodology, converting radar data to a gridded dataset and calculating the water and ice content of the clouds (VIL and VII). Given that the radars employed have different polarization characteristics, we focused on the reflectivity variable for CFAD analysis. For a more detailed examination of cloud microphysics, we conducted a case study, with methodologies and processes described in greater detail in the following sections.

2.3.1. Tracking

To develop the analyses, we first tracked the selected cases using the TATHU (Tracking and Analysis of Thunderstorms) [15] software developed by INPE. In its radar module, we utilized reflectivity data (CAPPI 3 km) from radars to identify and track rain clouds. Using an established threshold of 35 dBZ [16], TATHU creates a contour around the storm cells that exhibit reflectivity equal to or greater than the chosen threshold. By adding a temporal series of radar data, TATHU recognizes the storm cells in each dataset, allowing for the determination of cloud movement over time.

Since the analyses aim to evaluate cloud evolution before and after intense rainfall, we selected an analysis period of 2 h before and 1 h after each recorded rainfall event. Georeferenced files of the storm cell contours were extracted for each time step during the cloud’s life cycle for all selected cases (Figure 3).

2.3.2. Vertically Integrated Liquid/Ice

Single-polarization radars have a limited number of variables compared to polarimetric radars. However, Brazil still has a relatively small network of polarimetric radars compared to conventional ones, which motivated their use in this study. Since two types of radars were employed, different methodologies were applied to calculate the water and ice content within the cloud, as outlined below.

For single-polarization radars, the integrated liquid water content, VIL, was calculated based on [17]. VIL represents the relationship between the liquid water content and radar reflectivity and is computed along the vertical column using Equation (2).

$$VIL\left(Z\right)=3.4\times {10}^{-6}{\int }_{h_{base}}^h{Z}^{4/7}·dh\left[{\mathrm{kg}/\mathrm{m}}^2\right]$$

(2)

where Z is the reflectivity factor (${\mathrm{mm}}^6{\mathrm{m}}^{-3}$), and $h_{base}$ and h are the height (meters) of the base of the precipitation column and the 0-degree height, respectively.

VII (Vertically Integrated Ice), as discussed in [18], is calculated using Equation (3):

$$VII\left(Z\right)=6.07\times {10}^{-3}{\int }_{h_{-{10}^{\circ }\mathrm{C}}}^{h_{-{40}^{\circ }\mathrm{C}}}{Z}^{4/7}·dh\left[{\mathrm{kg}/\mathrm{m}}^2\right]$$

(3)

where Z is the reflectivity factor (${\mathrm{mm}}^6{\mathrm{m}}^{-3}$), and $h_{-{10}^{\circ }\mathrm{C}}$ and $h_{-{40}^{\circ }\mathrm{C}}$ are the heights of the isotherms of −10 °C and −40 °C, respectively, which are obtained from the soundings closest to the location of each radar.

Vertical integration is limited to the thermodynamic layer between −10 °C and −40 °C, which is the ice growth layer within a storm [19].

Calculations related to water content can lead to errors when using classical relationships [20]. In the case of polarimetric radars, equations using polarimetric variables are utilized, which studies confirm are more efficient than classical relationships [21,22,23,24,25,26]. Therefore, in this study, for cases covered by polarimetric radars, VIL was calculated through the integration of the liquid water content (LWC) [26] from the base to the height of the 0 °C isotherm.

$$VIL\left(K_{dp}\right)={\int }_0^{h}2.25K_{dp}^{0.723}·dh$$

(4)

Similarly, we calculated the ice content (ice water content—IWC) for cases covered by polarimetric radars based on [27]) through Equation (5):

$$VII(K_{dp},Z)={\int }_{h_{-{10}^{\circ }C}}^{h_{-{40}^{\circ }C}}0.71K_{dp}^{0.65}{Z}^{0.28}·dh$$

(5)

The VIL integrations in both equations were performed up to the height closest to the 0 °C isotherm (obtained from the nearest sounding to each radar). Since the maximum column height is below the freezing level, only liquid water was considered, excluding contributions from potential ice particles, as shown in [16]. The determined VIL and VII values were spatialized and saved separately from the original data for each selected time interval. Analyses are visualized in the form of boxplots, allowing the assessment of different statistics concerning the obtained data.

2.3.3. Contoured Frequency by Altitude Diagram (CFAD)

Employing the methodology proposed by [28], this study investigated the structure of clouds producing intense rainfall. The analysis period aligns with the one used by [19], who examined the periods 30 min before and 20 min after hail events, totaling a 1-hour window. In this study, the PMAX served as the reference period, as we acknowledged that rainfall may occur at varying times within the period, unlike hail, which has a specific onset.

This phase represents the final step in verifying and excluding cases. Following the selection of the PMAX and the analysis period, we identified and excluded cases with missing data or insufficient duration, arriving at the final number of selected events.

The CFADs were developed through a series of steps illustrated in Figure 4. First, we applied a mask to the VIL data using storm contour shapefiles from the tracker (Step 1). Next, we identified the pixel with the highest VIL value within the storm and created a 5 × 5 km area centered on this point (Step 2 and Step 3). The location (lat/lon) of this highest VIL point was recorded (Step 3). We then used reflectivity data to construct the CFAD, incorporating the previously created 5 × 5 km area at the recorded coordinates (Step 4). Finally, we extracted the vertical column data, spanning heights from 2 to 16 km, as shown in Step 5. This process was repeated for each time step from 30 min before to 20 min after the PMAX. The CFAD methodology evaluates the frequency of specific height values, resulting in 25 vertical profiles per event.

Since the aim is to assess the structure of storms that occurred within a specific region, each CFAD was constructed by grouping all the events from that locality. For instance, we selected 17 events in the South region. Given that 25 vertical profiles were extracted for each event, the total number of profiles for the South region was calculated as 17 × 25 = 425. Consequently, the CFAD for the South region was built based on 425 vertical profiles.

3. Results and Discussion

3.1. Events

After completing thorough verification and validation processes, we identified 83 heavy rain events during the period from 2016 to 2020, encompassing a total of 5 years of data, as outlined in

Table 2. Number of events selected by region.

Region	Number of Events
South	17
Southeast	29
Midwest	14
North	15
Northeast	08

and categorized by region.

3.2. Regional Categorization

In this section, we present the results obtained from calculating the water content (VIL), ice content (VII), and CFADs for the selected events. The analyses were conducted by comparing the differences between the selected events within the same region, juxtaposing them with other regions in the country. It is important to note that regional categorization serves as a means to group information, and we acknowledge that locations within the same region share similar (though not identical) characteristics concerning climate and typical meteorological events. Additionally, various factors, such as orography linked to storm structure and evolution [29,30], convection type, and the system responsible for precipitation generation, can influence these results. Nevertheless, despite these influencing factors, the study’s primary objective is to discern any patterns in the behavior of the analyzed variables, disregarding differentiation among events and assuming that they all contributed to intense rainfall.

VIL and VII

Figure 5 displays boxplots derived from the VIL calculations, organized by region, for each analyzed time frame. Initially, the South region (Figure 5e) exhibits the highest water content values, followed by the Midwest region (Figure 5c), North region (Figure 5a), Southeast region (Figure 5d), and finally the Northeast region (Figure 5b). We established this hierarchy without taking into account the outlier values. The Southern and Central-West regions demonstrate similar behavior concerning medians (50th percentile, second quartile). The first 20 min of the event (at moments −30 min and −20 min) show lower medians, with values increasing at each subsequent time interval. Furthermore, we observed the highest median value at the −10 min mark for both regions. This outcome suggests that the selected events in these regions experienced a rapid increase in water content from one moment to the next. The median values remained high in the time intervals following the PMAX, which is indicative that the rainfall may have persisted for several minutes throughout the cumulative period rather than occurring in just a few minutes. Additionally, some similar characteristics are noticeable among tropical regions (Figure 5a,b). The instants with the lowest median values are the first and last ones analyzed, respectively, and the highest values are concentrated between −20 min (−24 min, North region) and +10 min (+12 min, North region). Additionally, the moment featuring the highest median value is at −10 min (−12 min, North region). In contrast to the South and Midwest regions, the VIL calculation for the North and Northeast regions shows both intensification and de-intensification of water content within the analyzed instants. In the first two regions, only VIL intensification is well-characterized. Therefore, it is suggested that the selected events in the North and Northeast regions may have had shorter durations compared to the South and Midwest. This information aligns with the findings of [31], where the authors report that rain cells belonging to typical cloud clusters in the Amazon (in both rainy and dry seasons) have a life cycle of 0.6 h, indicating a short duration. Lastly, the Southeast region shows some similarity in the positioning of instants with higher and lower water content with the North region, concentrating the highest values between −20 min and the PMAX. However, unlike the other regions, VIL extremes (outliers) are observed in almost every instant, which may be associated with the variability of the selected events, discussed later in Section 3.3.

While it is possible to infer certain information, as discussed above, there may be associated uncertainties. One such uncertainty arises from the methodology of the VIL calculation, where we integrated water content up to the nearest height of the 0 °C isotherm to account solely for the liquid water portion, as the VII, which considers the approximate ice layer, was also calculated. Since no differentiation was made regarding the type of system (deep or not) that generated the events, there is high variability in the types of clouds that may (or may not) have been considered. This variability may have influenced some results, especially in tropical regions where the formation of warm clouds is normal. Additionally, in the Northeast region, for instance, the analysis grouped VIL values calculated using both polarimetric and conventional radar equations, another factor that could influence the results. When examining the difference between the South and North regions, it was particularly questioned why North clouds exhibit lower VIL values than the South clouds, given that the North region is warmer and should theoretically have clouds with a higher quantity of liquid water. Beyond the details mentioned above, this specific result may have been influenced by the fact that no calibration was performed between the used radars. As discussed in [32], Canguçu and Morro da Igreja radars (located in the Southern region) tend to slightly overestimate reflectivity values compared to the TRMM satellite radar, which was used as a reference. However, in the Northern region, Ref. [33] shows that the Belém and Manaus radars exhibit high underestimation compared to the TRMM radar. In other words, the lack of radar calibration could have been a determining factor in the differences found between the Southern and Northern regions in the VIL calculation.

Figure 6 illustrates the calculation of ice content (VII) for the selected events. In general, we see that a greater variability in the positions of the medians for the highest VII values is observed compared to the VIL values. Particularly concerning the regions of interest, the Southern region (Figure 6e) exhibits notable disparities in ice content compared to other regions. Several studies consider the subtropical region of the South Atlantic (which includes the South Brazilian region) as one of the areas on the planet most affected by severe convective events [34,35,36,37]. Such events involve clouds with deep vertical development, reaching very low temperatures, and consequently, they form a substantial amount of ice. Due to the presented values, Figure 6e (related to the South region) uses a different scale compared to the others. We adjusted this to provide a clearer visualization of the results obtained among the regions.

In contrast to the South region, in the North region (Figure 6a), there is minimal ice formation in the clouds. The North Brazilian region is situated in the tropical region of the Earth, which is the part of the globe with the highest solar incidence, making it warmer [38]. Although the tropical region is also influenced by deep convection events [39,40], due to the higher temperatures, there is also the formation of warm clouds over the region, which practically do not produce ice.

The Midwest (Figure 6c) and Southeast (Figure 6d) regions exhibit outlier values in moments preceding the PMAX, indicating that the studied clouds have ice peaks up to 30 min before the PMAX occurs. Moreover, in the Midwest region, the highest median of VII values occurs immediately after the PMAX, the exact moment with the highest VIL (Figure 5c). Similarly, in the Northeast region (Figure 6b), the highest median occurs at the moment just before the PMAX, which is also the exact moment as the highest median for water content (Figure 5b). Based on these results, we suggest that for the Northeast and Midwest regions, the onset of precipitation occurred around this moment. At that point, there was already sufficient water in the cloud to overcome gravity, and there was still ice present that could melt, sustaining intense precipitation for a longer duration.

3.3. CFADs

Constructing regional CFADs involves utilizing data from all selected radars, irrespective of their polarization. As conventional radars lack polarimetric variables, we cannot assess microphysical parameters across all events in this analysis. Therefore, we used only the reflectivity variable, which is common to both radar types.

Figure 7 displays the CFAD for the North region of Brazil, incorporating the 15 selected events in the area, constructed with 375 vertical profiles of clouds generating intense rainfall. In the figure, we observe an area with higher frequency values between the 40–45 dBZ and 50–55 dBZ intervals along the vertical axis for all analyzed time intervals. The reflectivity CFADs in this study were created using a 5 × 5 km sample (25 km²) centered on the pixel with the highest VIL for each time interval of each studied event. We used TATHU to track the core of the storm and then retrieved the vertical profiles, ensuring that the sample was centered on the highest VIL (Section 2.3.3). As described in Section 2.3.2, VIL was calculated from the liquid water content (LWC), indicating that the pixel with the highest VIL, used as the center of the sample, corresponds to a region with elevated LWC. According to [41], areas with high LWC values are closely linked to the location and intensity of updrafts within clouds. Consequently, we designated the region with higher frequency values along the vertical axis as the updraft region. This approach is also supported by [19].

At the −36 min mark in Figure 7, we observe the presence of an updraft, indicating that the clouds are already in the growth phase. Between −24 and −12 min before the PMAX, there is a reduction in their height, but at −12 min, we observe a peak frequency in the 45–50 dBZ range, between 4 and 5 km in altitude. We suggest that this characteristic transition between −24 and −12 min is associated with the moment when the updraft is still transporting droplets to the higher parts of the cloud (−24 min). Shortly after, the process of collection and coalescence begins, making the droplets larger, and as they become heavier, they concentrate at lower altitudes (peak frequency at −12 min). At the PMAX moment, the updraft height increases again, reaching around 13 km, and we observe two peak frequencies, between 3 and 4 km and between 5 and 6 km. This moment indicates that the cloud is still in the growth phase, being fed by the updraft. Since at −12 min there was already a higher concentration of droplets between 4 and 5 km, and the updraft at the PMAX is still active, some of these droplets are again transported upward, while others concentrate in even lower regions of the clouds, generating the observed dual peaks. Finally, in the moments after the PMAX, the peak frequencies progressively concentrate in the lower parts of the clouds; the height of the most active part of the clouds decreases, indicating that they are entering the dissipation phase; and precipitation is already occurring. It is noteworthy that the PMAX period is merely a reference period, not necessarily associated with the onset of precipitation, as it may extend for several minutes throughout the cumulative period.

Figure 8 presents the CFAD for the Northeast region of Brazil, constructed from 8 events, i.e., 200 vertical profiles. In the initial moment (−30 min), we observe that the updraft of the clouds is still in development, unlike the Northern region, where at −36 min, the clouds already have a formed updraft. Therefore, the events in the Northeast region exhibited a faster development than those in the North region. At −20 min, there is a strengthening of the updraft. At the moment just before the PMAX (−10 min), frequency values around 35% are observed in the 45–50 dBZ range between 2 and 4 km, and the total height of the ascending air column is reduced. From this, we suggested that the concentration of the highest frequency between 2 and 4 km is associated with the presence of raindrops that concentrate in the lower parts of the clouds, indicating that precipitation may have started around this moment. This information corroborates with the result from Figure 5b, where the median of the highest VIL value is at the same moment. In other words, considering all analyzed events in the Northeast region, it is the moment when the clouds have the highest amount of liquid water.

At the PMAX moment, there is again a strengthening of the updraft, presenting frequency peaks, although not very intense, in higher regions of the clouds (between 9 and 10 km and 12–13 km), indicating the presence of ice. In Figure 6b, the moment with the highest amount of ice is also at −10 min, but as there might be a delay concerning reflectivity, and the data frequency is every 10 min, the presence of ice is more clearly observed at the PMAX and not at −10 min. At +10 min, two frequency peaks are observed, between 6 and 7 km and between 8 and 9 km, which are associated with the weakening of the updraft, which is no longer strong enough to keep the ice in the higher parts of the clouds. Therefore, it starts to fall within the cloud, generating peaks at different heights.

Finally, at the last moment, we observe reduced cloud activity as they are in the process of dissipation.

In Figure 9, Figure 10, and Figure 11, corresponding to the CFADs of the Midwest, Southeast, and South regions, respectively, we observe minimal variability in frequency across the analyzed time intervals. To investigate this characteristic, we calculated the standard deviation of reflectivity values for each moment of the analysis, as shown in Figure 12. The 75th percentile value (9.95 dBZ) of the deviation dataset is delimited by a vertical dashed line.

The South, Southeast, and Midwest regions (purple, red, and green, respectively) emerge as the only areas where deviations surpass the 75th percentile (points to the right of the percentile line), specifically starting from a height of 5 km (horizontal dashed line). Additionally, the South region (purple) generally exhibits the highest deviation values among the three regions, followed by the Southeast, and lastly the Midwest. These findings align with their respective geographical characteristics. In contrast to tropical regions, areas at higher latitudes experience a more significant temperature gradient throughout the year. This condition contributes to a variety of phenomena, impacting overall precipitation in these regions. Due to the diverse range of events influencing these areas and generating distinct cloud structures, Figure 12 reveals that the most significant deviations are not localized in the lower parts of the clouds but occur above 5 km in height. Essentially, the intense rainfall events selected in the South, Southeast, and Midwest regions may have originated from clouds with both deep vertical development and shallower structures [42,43,44]. Notably, the Midwest, despite having tropical characteristics, is also affected by extratropical systems [45].

This characteristic becomes evident in Figure 12, where, despite showing high variability in the data, the Midwest region (among the three above the 75th percentile) exhibits the smallest deviations.

Another factor that can significantly contribute to the high variability found in the standard deviation analysis of the South and Southeast regions is important to highlight. This factor is related to the moment when atmospheric systems reach the radar during the 1-h data analysis. Within this interval, it is possible to analyze both systems that formed and evolved within this time window and systems that were fully formed before being captured by the radars. In the South and Southeast regions of Brazil, this possibility is more frequent, particularly due to the common occurrence of persistent meteorological systems such as cold fronts that can cross these regions throughout the observation period. This difference in the origin and stage of development of the systems can be an additional factor contributing to the lower variability observed in the CFADs of these regions during the analyzed period. This analysis also applies to the northern and northeastern regions. However, as observed, the standard deviations of the events analyzed in these regions are lower, indicating a more uniform structure among the systems and enabling the identification of some patterns.

As this study does not differentiate between the systems responsible for generating events and the time of year in which they occur, the high variability of events in the South, Southeast, and Midwest regions is the factor behind the generation of CFADs with less pronounced results. Nevertheless, despite the CFAD in Figure 11 showing high deviations in the dataset, some characteristics are observed. The first three moments do not exhibit significant frequency variations, but at the PMAX and +10 min moments, peaks of frequency in lower parts of the clouds are noticeable, suggesting a higher concentration of hydrometeors in those regions. Finally, at the +20 min moment, a weakening of the updraft is observed, already associated with the dissipation phase of the clouds.

Reflectivity serves as a variable for making associations and assumptions about the physical and morphological characteristics of hydrometeors. To obtain specific information, we need to use polarimetric variables since their values are directly linked to these characteristics. However, as the goal of this section is to analyze all regions of Brazil, and since not all regions are covered by polarimetric radars, we used reflectivity as the analytical variable. Although it is impossible to definitively determine the microphysical characteristics of hydrometeors present in the clouds, we can infer some information based on the literature. For example, clouds with intense vertical development typically reach very cold top temperatures as temperature decreases with height. Ref. [41] mentions that if a cloud exceeds the height corresponding to 0 °C, we can observe supercooled droplets. As the cloud reaches more negative temperatures, the presence of ice crystals, graupel, and hail becomes apparent. According to the authors, the probability of ice being present in a cloud with a top temperature of −13 °C is 100%. Therefore, using this information and relying on the theory of warm and cold cloud formation, we can make some, albeit limited, associations with the microphysical processes that occur.

3.4. Case Study

In the previous Section 3.3, microphysical analysis was not feasible due to the use of data from radars with different polarizations to generate CFADs. To examine such microphysical characteristics, we conducted a case study in the Southeast region of Brazil, aiming to evaluate specific details present in the clouds and stimulate the development of future research using these parameters.

Among the five regions examined in this study, the Southeast region exhibited the highest number of selected events covered by polarimetric radars. Therefore, it was chosen as the focus area for this case study. Figure 13 illustrates the locations of the polarimetric radars used in this study along with the automatic stations of INMET where the events were selected.

It is important to highlight that all criteria for selecting stations and events remain consistent with the overall methodology of this study. Furthermore, the same methodologies employed in the study for calculating integrated water (VIL) and ice (VII) content within clouds and constructing CFADs are also applied here. However, unlike Section 3.3, which exclusively examines the CFADs of reflectivity, this section includes an analysis of the CFAD for the $Z_{DR}$ variable.

After applying all validation criteria, we selected seven intense rain events that accumulated more than 40 mm in 1 h. Figure 14 presents the time series of VIL and VII calculations for the seven studied cases. Comparing the VIL and VII life cycles with radar reflectivity fields for each event, we observed some differences and similarities. As seen in Figure 14a,b,e,f, there is an increase in VIL and VII values just before the PMAX moment (dashed vertical line). Immediately after this increase, there is a sharp decrease in values. This suggests that the rise may be associated with convective development, where raindrops and ice particles grow in the cloud, resulting in high reflectivity values. As precipitation begins, VIL and VII values start to decrease. Despite these four events sharing a common characteristic (isolated developing cores), some nuances are notable. The events in Figure 14a,b started their life cycle as small, isolated cores; reached maturity; and dissipated shortly after. However, the event in Figure 14e showed an increase (mainly in ice content) but maintained high VIL and VII values for almost an hour before decreasing. Among the seven events studied, this was the one with the highest rainfall accumulation, exceeding 100 mm in 2 h. As discussed in the previous case study analysis, there is an indication that this event generated rainfall in less than 2 h, given the proximity of the PMAX times for both cases that compose this event. This information aligns with the VIL and VII life cycle since after 00 UTC, the values for both variables begin to reduce. In Figure 14f, there is an increase in VIL and VII minutes before the PMAX moment, followed by a decline. However, in subsequent instants, the life cycle shows a second peak in water and ice content. This feature is observed because, during the stage when the cloud is most intense, it splits into two cores, with the part responsible for the highest reflectivity reaching its peak and dissipating. On the other hand, the other core intensifies again, resulting in the second observed peak in VIL and VII values.

The events in Figure 14c,d were cores belonging to larger-scale precipitating systems, so, in general, they did not show significant variations in the VIL and VII life cycles. However, the main difference between them is that the first (at a certain point) separated from the larger system and showed a peak of intensification.

Finally, and similarly to the previous ones, there are a few variations throughout the instances of the VIL and VII life cycle in the event in Figure 14g. This event was a small and localized core, which exhibited practically constant reflectivity values over the analyzed instances. Its peak of VIL and VII does not align with the PMAX moment, suggesting that the cloud responsible for the event generated rain more steadily over the 1 h recording period.

In Figure 15, the CFAD of reflectivity for the seven selected cases is presented. Overall, there is no clear change in the vertical structure of the events over time. However, some characteristics stand out: The highest frequency values are observed between 45 and 50 dBZ, from the base up to approximately 4 km in height. At the −10 min moment, there is an increase in frequency values, suggesting a strengthening of the updraft and the presence of ice since the PMAX is the instant of the highest reflectivity observed in the rain gauges, and the presence of ice in the clouds facilitates higher reflectivities. At the PMAX moment, the previously more structured column begins to decrease in height, possibly indicating the onset of precipitation. In the subsequent moments, there is no notable weakening of the clouds; therefore, it is suggested that the rain, in general, did not occur in a single moment but rather more continuously. Ref. [19] generated reflectivity CFADs for 16 hail events in the Southern region of Brazil. In contrast to the results obtained in this case study, the author found the formation of a more intense convective column, which quickly dissipated after hail started at the surface.

The calculation of the CFAD using the $Z_{DR}$ variable is shown in Figure 16. At the −30 min and −20 min moments, the highest frequency values are within the 0 and 1 dB interval, indicating the presence of spherical drops in the lower parts of the clouds. Additionally, although low, there are frequency values within the 2.5–3 dB range, indicating that the clouds were in a growth phase. At the −10 min moment, the frequency values in the lower parts of the clouds decrease. In contrast, the convective column increases in height. This increase in the frequency of positive $Z_{DR}$ values in higher parts of the clouds is associated with the $Z_{DR}$ columns, also identified in previous case studies [46,47,48,49]. Due to a strengthening of the updraft, drops and droplets are transported to higher regions, which eventually freeze through processes like aggregation and accretion [50], resulting in a frequency peak in negative $Z_{DR}$ intervals at an altitude of 8 km. At the PMAX moment, liquid drops fall within the cloud and grow through collision and coalescence processes, displaying the highest frequency values in the lower areas of the clouds. In the subsequent moments, frequency values decrease, but the column remains with a clear division of positive $Z_{DR}$ values up to 6 km in height and negative values above this height. As the main column’s weakening is not very evident, it is suggested, as highlighted in Figure 15, that the rain occurred continuously after the cloud initiated the precipitation process.

4. Conclusions

Despite the different techniques in the literature for defining intense rainfall in Brazil, many studies on the subject consider rainfall occurrence on a daily scale. However, numerous events associated with flash floods and rapid flooding occur over a short period. In Brazil, studies are scarce regarding sub-daily intense rainfall events, and existing studies focus on very specific regions. Therefore, the main objective of this study was to examine the structure and microphysical aspects associated with clouds generating intense rainfall over Brazil, resulting in high accumulation in a very short time (1 h). Intense rainfall events were selected from hourly data from automatic rain gauges of the National Institute of Meteorology. After applying various validation criteria, 83 events were chosen and distributed across the five Brazilian regions. The selected intense rainfall events exhibit the main convective column with reflectivity values ranging between 40 and 50 dBZ, generally extending to approximately 6 km in height in all regions of Brazil. Events in the Northeast region were faster compared to those in the North, as within the analyzed period, the North region already had formed clouds at the first analysis instant, while in the Northeast region, clouds were still developing. In the South, Southeast, and Midwest regions, reflectivity values did not show significant variations throughout the analyzed hours. However, the divergence of events considered in these regions was the factor responsible for the less expressive results, as each system can generate different cloud structures. This conclusion was drawn from calculating the standard deviations of events for each region, concerning height. From this calculation, we observed that the only regions where the deviations exceed the 75th percentile are these three regions. Additionally, the highest deviations are observed from 5 km in height, confirming the variability of the vertical structures of the events studied in these specific regions.

The VIL and VII life cycles show that events in the South region had the highest values among the regions throughout the analyzed moments. This result aligns with the fact that intense events that typically affect the region have a deep vertical development, reaching very low top temperatures conducive to ice formation in clouds, often characterized as severe events. Events in the North and Northeast regions were faster than those in the Southern and Central-Western regions, as in the former, both intensification and de-intensification of VIL and VII values are observed, while in the latter, only the intensification of values is well evidenced.

Furthermore, the case study in the Southeast region showed that the selected events exhibited an abrupt increase in VIL and VII near the PMAX instant and then a drop when the events were isolated development cells. In contrast, when the events belonged to larger systems, there were not many variations in VIL and VII.

Based on all the analyses conducted, it is concluded that short-term intense rainfall events exhibit rapid intensification in their structure, regardless of the region in Brazil. The momentary intensification of the ascending current and an indication of melting in subsequent moments appear to be key factors for the intensification of the descending current, consequently leading to the onset of intense precipitation.

Furthermore, the type of system responsible for generating these events significantly influences the results, particularly in the Southern, Southeastern, and Central-Western regions, as it becomes challenging to identify certain patterns when various types of cloud structures are analyzed. Therefore, it is suggested that future studies analyze these events by separating the types of synoptic (or mesoscale) systems responsible for their generation. Additionally, it is valuable to analyze similar moments of the clouds, distinguishing between events that formed within the radar domain and more prolonged systems that may already be in a mature phase when entering the radar domain.

Finally, due to the rapid intensification of clouds occurring within a matter of minutes, the predictability of this type of rainfall is complex, complicating the timely issuance of specific warnings.