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Article

Temporal Evolution of Lightning Properties in the Metropolitan Area of São Paulo (MASP) During the CHUVA-Vale Campaign

by
Raquel Gonçalves Pereira
1,2,
Enrique Vieira Mattos
1,
Thiago Souza Biscaro
3 and
Michelle Simões Reboita
1,*
1
Natural Resources Institute, Federal University of Itajubá (UNIFEI), Itajubá 37500-903, Minas Gerais, Brazil
2
Postgraduate Program in Environmental Studies and Water Resources, Federal University of Itajubá (UNIFEI), Itajubá 37500-903, Minas Gerais, Brazil
3
Division of Satellites and Meteorological Sensors, National Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São Paulo, Brazil
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(4), 426; https://doi.org/10.3390/atmos16040426
Submission received: 28 February 2025 / Revised: 23 March 2025 / Accepted: 1 April 2025 / Published: 6 April 2025
(This article belongs to the Section Meteorology)
Browse Figures
Versions Notes

Abstract

:
Lightning is associated with severe thunderstorm events and causes hundreds of deaths annually in Brazil. Additionally, it is responsible for losses amounting to millions in Brazil’s electricity and telecommunication sectors. Between November 2011 and March 2012, the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign was conducted in the Vale do Paraíba region and the Metropolitan Area of São Paulo (MASP), located in southeastern São Paulo state, Brazil, to enhance the understanding of cloud processes, including lightning. During the campaign, several instruments were available: a meteorological radar, lightning location systems, rain gauges, a vertical-pointing radar, a surface tower, and others. In this context, the main goal of this study was to evaluate the temporal evolution of lightning properties, such as frequency, type (cloud-to-ground (CG) and intracloud (IC) lightning), peak current, length, and duration, in the MASP between November 2011 and March 2012. To achieve this objective, lightning data from the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) were utilized. The maximum amount of lightning for the BrasilDAT (322,598 events/month) occurred in January, while for the SPLMA (150,566 events/month), it occurred in February, suggesting that thunderstorms displayed typical summer behavior in the studied region. Most of lightning registered by the BrasilDAT were concentrated between 2:00 and 5:00 pm local time, with a maximum of 5.0 × 104, 6.2 × 103, and 95 events/month.hour for IC, −CG, and +CG lightning, respectively. These results are associated with the favorable conditions of diurnal atmospheric instability caused by surface heating. Regarding the lightning properties from the SPLMA, longer-duration lightning (up to 0.4 s) and larger spatial extension (up to 14 km) occurred during the nighttime period (0–6:00 am local time), while the highest lightning frequency (up to 9 × 104 events month−1 h−1) was observed in the afternoon (3–4:00 pm local time).

1. Introduction

Several key factors are necessary for the formation of thunderstorm-associated clouds, including humidity (the amount of water vapor available in the atmosphere), instability (variation in temperature with height), and air-lifting mechanisms (such as convection, orography, and air convergence along frontal surfaces) [1]. Thunderstorms have the potential for lightning formation, which occurs due to collisions between graupel and ice crystals within a region of the cloud characterized by strong updrafts and the presence of supercooled liquid water droplets [2]. The gravitational process facilitates the separation of electrical charges and increases the electric field within the cloud. If this process reaches a threshold where the dielectric strength of the air is exceeded, lightning is generated [1]. Lightning is classified based on its formation and dissipation region into: (i) cloud-to-ground (CG); (ii) ground-to-cloud (GC); (iii) intracloud (IC), occurring within the same cloud; and (iv) inter-cloud (CC), occurring between two or more clouds [3]. IC lightning is the most common type, followed by CG lightning [4,5]. Given the relative ease of measurement and the significant societal impact of CG lightning, this type has been the most extensively studied and is the best understood.
It is estimated that approximately 1.4 billion lightning events occur globally each year [6,7]. In only Brazil, around 96 million lightning events occur annually [8], resulting in hundreds of fatalities each year [9]. Additionally, lightning causes financial losses amounting to millions in the electrical and telecommunications sectors. However, lightning also plays an important role in weather and climate by contributing to the maintenance of the global atmospheric electrical circuit and the production of nitrogen oxides (NOx) [10].
Several studies have utilized Lightning Location Systems (LLSs) and satellite sensor data to map the temporal and spatial distribution of lightning. These studies indicate that convection intensifies from afternoon to night, influencing cloud formation and lightning occurrence. Studies conducted in different regions, such as in the USA [11,12], the Plata Basin [13], Argentina [14], the state of Amazonas, and the Bolivian Altiplano [15], have documented that convection predominantly occurs during the period between the late afternoon and nighttime, with peak intensity observed in the afternoon (in line with the daily insolation cycle). This temporal characteristic of convection is linked to the time of day when conditions conducive to instability and the accumulation of Convective Available Potential Energy (CAPE) for intense convection are established. On a regional scale, the lightning rate is linked to daytime solar heating [6,16,17].
In Brazil, several studies have investigated the diurnal cycle of lightning [8,18,19,20,21,22,23]. Pinto Jr et al. [18] analyzed lightning data from the National Integrated Lightning Detection Network (RINDAT) and found that lightning activity in the Minas Gerais state peaks between 4:00 pm and 6:00 pm local time. Similarly, Naccarato [19] examined lightning occurrences in the southeast region and observed a peak between 3:00 pm and 4:00 pm local time. Furthermore, Naccarato [19] noted that the peak in negative CG lightning occurs 1–2 h before the peak in positive CG lightning. This finding aligns with the fact that positive lightning predominantly occurs in the later stages of convective thunderstorms [5].
Oda et al. [8] analyzed the spatiotemporal distribution of total lightning flash density across Brazil over a three-year period (2018, 2019, and 2020) using data from the Geostationary Operational Environmental Satellite-16 (GOES-16). Their study found that the greatest lightning activity generally occurred between 12:00 pm and 6:00 pm local time. However, peak lightning activity varied across different regions of Brazil due to the influence of various meteorological systems. For example, along the northeast coast of Brazil—where peak lightning occurs between 9:00 am and 12:00 pm—lightning activity is strongly influenced by sea breezes [23]. In contrast, the southern region experiences lightning throughout the day due to the year-round influence of cold fronts, resulting in no well-defined diurnal cycle [24]. Similarly, the south-central region is affected by frontal systems, leading to the absence of a preferential time for lightning activity. A distinct nighttime peak was observed in the region stretching 500 to 1000 km inland, parallel to the coast in the north-northeast, with maximum activity occurring between 6:00 pm and 11:00 pm and again between 12:00 am and 3:00 am. This pattern is likely associated with squall lines originating from the Amazon coast [25]. Conversely, in the southeast region, lightning activity predominantly occurs in the late afternoon and early evening, which is associated with daily convection.
LLSs, such as the Lightning Mapping Array (LMA), provide detailed three-dimensional (3D) information on the spatial distribution of Very High Frequency (VHF) radiation, enabling the precise mapping of lightning formation and propagation [26]. For the first time in Brazil, between November 2011 and March 2012, LMA measurements were carried out during the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign [27]. During the campaign, rain gauges, a weather radar, a disdrometer, a microwave radiometer, a vertical-pointing radar, a Lidar, GPS, and a surface tower were installed in São José dos Campos city, along with 12 LMA stations in the MASP region. The campaign aimed to improve the understanding of cloud evolution into storm systems that produce lightning. The diverse dataset collected contributed to advancing knowledge of cloud development processes, electrification mechanisms, and lightning generation. Lightning properties derived from the LMA data were analyzed, providing an opportunity to study the diurnal behavior of lightning activity. For example, Bailey et al. [20] used VHF source data from the São Paulo Lightning Mapping Array (SPLMA) network in the MASP during the summer of 2011. Their analysis revealed that February and March exhibited the highest lightning activity in the region, with peak occurrence in the afternoon, around 4:00 pm local time. These results suggested that, while each region has its own characteristic peak time for electrical activity, the diurnal variability of thunderstorms generally follows a similar pattern throughout the day. In terms of lightning length, Chronis et al. [21] analyzed data from the CHUVA-Vale campaign and found that lightning with a major duration occurred during the early morning hours. They also observed that as lightning frequency increased, lightning length tended to decrease.
Despite numerous studies on the diurnal cycle of lightning, few have simultaneously assessed multiple lightning characteristics, including quantity, electrical properties (polarity, peak current), and physical attributes (size, duration, and number of VHF sources), which was the aim of this study. Additionally, this study focused on lightning activity over the MASP, a region of great economic significance and a major electricity consumer. The MASP is the largest metropolitan area in Brazil, with approximately 21.5 million inhabitants, ranking among the ten most populous metropolitan regions worldwide. It consists of 39 municipalities in the state of São Paulo [28]. Advancing the understanding of lightning properties in this region could be valuable for improving short-term weather forecasting models. Therefore, the primary objective of this study was to analyze the temporal distribution of lightning properties—including peak current, polarity, type, size, and duration—over the MASP, based on data collected during the CHUVA-Vale campaign.

2. Methodology

2.1. Study Area

The study area comprises the MASP, which is the largest metropolitan area in Brazil and is in the southeastern region of Brazil, between latitudes 23°8′24″ S and 24°21′14″ S and longitudes 45°39′0″ W and 47°16′12″ W, with a territorial extension of 7963 km2 [29] (see Figure 1).
The MASP represents a major Brazilian economic hub, with approximately 21.5 million inhabitants distributed among 39 municipalities in the São Paulo state, and is one of the ten most populous metropolitan regions in the world [28]. The main climate feature of The MASP is its monsoon climate [30,31,32], meaning the region receives more than 50% of its annual rainfall (1500 mm) during the austral summer, while the austral winter is dry. The MASP is also affected by frontal systems, coastal extratropical cyclones, and sea breeze circulation [32,33].

2.2. Data

The data used in this study were obtained through the CHUVA-Vale Field Campaign (data available at: https://ftp.cptec.inpe.br/chuva/glm_vale_paraiba/, accessed on 10 March 2022). The CHUVA campaign project took place between November 2011 and March 2012 in the Vale do Paraíba region, the MASP, and neighboring cities (Figure 1). The observation systems in operation were an X-band polarimetric radar; lightning mapping systems operating at Very Low Frequency (VLF, 3–30 KHz), Low Frequency (LF, 30–300 KHz), High Frequency (HF, 3–30 KHz), and Very High Frequency (VHF, 30–300 MHz); electric field mills; a fast camera; disdrometers; a vertical aiming radar; and a microwave radiometer. From the campaign data, VHF sources from the São Paulo Lightning Mapper Array (SPLMA) and return strokes provided by the Brazilian Lightning Detection Network (BrasilDAT) were used. It is important to highlight that the return strokes detected by the BrasilDAT are referred to in this text as a “lightning event”. In contrast, for the SPLMA network, the term “lightning event” refers to VHF sources grouped into a flash. Figure 1 shows the location of the sensors of the SPLMA (white circles) and BrasilDAT (blue cross) networks. The analysis will focus on the red circle region, which defines the area that the SPLMA network reaches with the best detection efficiency (<100 km distance from the center of the SPLMA network), as discussed by Bailey et al. [20].
SPLMA lightning network: The SPLMA locates the radiation sources emitted in VHF frequency from electrical discharges in three dimensions up to 100–150 km from the network center and locates the radiation emissions emitted by lightning in the frequency range of 30–300 MHz [26]. A total of 12 stations operating on TV channels 8 (180–186 MHz) and 10 (192–198 MHz) were installed in the MASP and neighboring cities in October 2011 [20] (Figure 1, circles in white color). The SPLMA was operational from November 2011 through March 2012. Sensor spacing was on the order of 15–30 km, with a network diameter on the order of 40–50 km. The date and location (latitude, longitude, and height) of the sources of each lightning event registered by the SPLMA was used. The following quality control procedures were applied to the SPLMA data [20]: (i) VHF source data correspond to processing Level 2, which consists of VHF sources grouped into flashes with noise filtering. (ii) In order to minimize noise effects, VHF sources detected by the SPLMA were restricted to those with a maximum reduced chi-square (χ2) of 5 that were detected by at least six stations. During the CHUVA-Vale campaign, the mean χ2 and mean number of stations per solution were 1.3 and 7, respectively. (iii) To minimize the impact of noisy detections and misclassified lightning events in the SPLMA network (which are typically associated with flashes containing very few VHF sources), only lightning events with more than 10 VHF sources were used. (iv) The VHF sources used in this study were limited to those occurring within 100 km of the SPLMA center, ensuring a region with higher detection efficiency and capturing the majority of lightning activity from thunderstorms.
BrasilDAT lightning network: The BrasilDAT is a lightning network for the detection of IC and CG return strokes that use technology developed by Earth Networks. The lightning sensors use the time-of-arrival (TOA) for the detection of return discharges and operate in the frequency range between 1 Hz and 12 MHz. For the CHUVA-Vale campaign, it was composed of 56 sensors in 11 states in the southeast, south, midwest, and part of the northeast regions of Brazil [34]. The information used in this study consists of the date, location, polarity (positive and negative), and peak current of the IC and CG return strokes. Williams et al. [35] observed excellent agreement between peak current estimates from the RINDAT (another Brazilian LLS) and the BrasilDAT during the CHUVA campaign. In addition, Mattos et al. [36] evaluated 46 compact isolated thunderstorms during the CHUVA-Vale campaign with the BrasilDAT and SPLMA datasets. The authors concluded that both LLSs captured the electrical activity of thunderstorms.

2.3. Analysis Method

The focus of this study was on the diurnal cycle of lightning properties (Table 1) between November 2011 and March 2012. For this, return strokes provided by the BrasilDAT and VHF sources of lightning detected by the SPLMA network were used. The properties studied using the information from the BrasilDAT were the frequencies of +CG, −CG, and IC lightning and their electrical properties (peak current and polarity). Lightning frequency was analyzed according to the temporal distribution of lightning occurrence, corresponding to the total sum of return strokes for each hour of the day over the studied period. The analysis was conducted on a monthly basis. In contrast, for the diurnal cycle of peak current, the average value was calculated for each local time by month.
The diurnal cycle of the physical properties of lightning (duration, height, and length) and the frequency of VHF sources in the SPLMA data were evaluated. The duration of each lightning detected by the SPLMA network was calculated by determining the time difference between the occurrence of the last and the first source of the lightning. The analysis of lightning height considered the height of all VHF sources associated with a specific lightning event. In contrast, the determination of lightning length was first calculated by computing the area (in km2) of the lightning. For this, the convex hull method proposed by Bruning and Macgorman [37] was employed. In this methodology, the lightning area is determined as the area of the polygon that connects the outermost VHF sources (convex hull). Then, the lightning length (in km) is calculated as the square root of the polygon area.

3. Results and Discussion

3.1. Diurnal Cycle of Lightning Frequency and Peak Current from BrasilDAT

Table 2 presents the number of lightning events that occurred during the CHUVA-Vale campaign measured by the two detection networks (SPLMA and BrasilDAT). Only lightning events that occurred within 100 km of the SPLMA network center were considered. This spatial limitation aims to select a region for which the SPLMA network showed reasonable detection efficiency [20].
The data analysis reveals that January exhibits the highest incidence of lightning events according to the BrasilDAT, accounting for 28% of the total recorded events, while the peak activity for the SPLMA network occurs in February, representing 32% of its detections. The BrasilDAT system identified a total of 1,158,535 lightning events, whereas the SPLMA network recorded 467,160. This difference arises from the distinct detection methods and operational frequencies of each lightning network. The BrasilDAT captures return strokes, while the SPLMA detects flashes. Return strokes correspond to discrete pulses of electrical discharge that constitute a lightning flash, whereas flashes represent the entire lightning event, encompassing all associated strokes. Consequently, networks that detect strokes tend to report a higher number of events than those that register only flashes.
Figure 2 shows the diurnal cycle of three types of lightning, IC, +CG, and −CG, from November 2011 to March 2012 based on the BrasilDAT data. The higher lightning frequency occurs in the afternoon, from approximately 2:00 pm to 5:00 pm local time (1500 to 2000 UTC), agreeing with Pinto Jr et al. [18], who found a peak between 4:00 pm and 6:00 pm local time in Minas Gerais state, also in southeastern Brazil. Naccarato [19] also found a maximum frequency of lightning around 3:00 pm to 4:00 pm local time through a lightning climatology study in the southeast of Brazil. Lightning’s preference for the afternoon period is associated with diurnal heating, which strengthens convection in the afternoon, facilitating cloud and thunderstorm development. Convective clouds also impact the lightning polarity since negative −CG is predominant over +CG lightning, and we found this for the studied region. Positive polarity lightning is typically formed in the stratiform regions of mesoscale convective systems (MCSs) due to a significant reduction in cloud depth [22]. In contrast, regions with intense convection, which results in deep cloud formations, produce more lightning with negative polarity [5].
The most frequent type of lightning is IC (Figure 2), and January (austral summer) records the highest number of lightning events (322,598 occurrences; Figure 2c), including 294,259 IC occurrences with a peak at 2:00 pm local time. Figure 2f depicts the total diurnal lightning cycle for the BrasilDAT, showing an increasing trend after 11:00 am local time and reaching an hourly peak around 2:00 to 3:00 pm local time. Most of the lightning occurs during afternoon’s peak convection. The higher incidence of lightning in January and February, compared to November and December, is directly related to the intensification of convective processes typical of summer in the Southern Hemisphere. November and December mark the transition between spring and summer when heating and humidity have not yet reached their peak, resulting in lower atmospheric instability and, consequently, a lower frequency of lightning events.
Figure 3 shows diurnal temporal evolution for peak current from November 2011 to March 2012. December was the month with the highest peak current values (Figure 3b). Figure 3 shows that average hourly variations in the peak current between both polarities show similar patterns during daytime hours. In the morning, there is a positive polarity, with ups and downs during the months and a greater constancy of both positive and negative in the afternoon.
Figure 3f shows a decrease in the average over a day, peaking at 10:00 am local time. It is important to highlight that the period of the day with the highest occurrence of lightning events does not coincide with the period of the highest peak current, which suggests that thunderstorms caused by late afternoon thermodynamic instability involve less intense discharges in terms of electrical current, although greater frequency of occurrence [22]. According to Naccarato [19], from 10:00 pm to 11:00 am local time, the atmosphere does not present the thermodynamic characteristics necessary for the formation of thunderstorms, and lightning formation during this period is more related to the passage of frontal systems. In the afternoon, local thunderstorms are mostly caused by atmospheric instability caused by the increase in temperature and heat exchange on the surface.

3.2. Diurnal Cycle of Physical Properties of Lightning from SPLMA

Figure 4 illustrates the diurnal cycle of five physical properties of lightning events from November 2011 to March 2012. The VHF source frequency, height, length, and duration of lightning events are all greater during the early morning hours. The frequency of lightning events is higher in the afternoon, as was also observed with data from the BrasilDAT previously (Figure 2). That is, when the amount of lightning is low, the length of lightning is greater. This result agrees with the study carried out by Mecikalski et al. [10], who also observed this relationship. Chronis et al. [21] also documented that most lightning events that occurred during afternoon convection in CHUVA-Vale campaign are smaller compared to those that occur during the morning period.
Note that the frequency is greater in the afternoon and that the greatest amount of lightning occurs in February and March (Figure 4d,e). Bailey et al. [20], who also used the SPLMA network data from CHUVA-Vale campaign, found that February and March are the most active months, with daily peak hours in the afternoon around 4:00 pm local time. Chronis et al. [21] examined the diurnal variations in lightning properties observed by the SPLMA, where they also found that the frequency of lightning events is higher at the typical afternoon convective maximum.
Figure 4f presents the average diurnal cycle of lightning properties, except for lightning frequency, which represents the total number of lightning events from November 2011 to March 2012. During the night period (0–6:00 am local time), lightning events tend to be greater in length, whereas during the daytime (12–14:00 local time), they are shorter. In addition, the results show that the length and frequency of lightning exhibit opposite trends, i.e., maximum lightning frequency (green curve) occurs between 15:00 and 16:00, while minimum length values occur in this period. This probably occurs because the regions of opposite polarity charge are closer together due to vigorous mixing within the storm cloud [21]. Consequently, the stronger afternoon precipitation mechanism, combined with latent heat release and intense turbulent updrafts, promotes a more heterogeneous charge distribution [38]. This process occurs across smaller eddies, leading to spatially smaller lightning [37].
However, according to Chronis et al. [21], it is important to highlight the fact that because the dataset is temporally limited, it can be argued that the results may not capture the entire meteorological variability of the region nor explain all types of thunderstorms.

4. Conclusions

This work examined the properties of lightning in the Metropolitan Region of São Paulo (MASP) during the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign between November 2011 and March 2012. The diurnal cycle of lightning was analyzed through data from the BrasilDAT and SPLMA networks. This study represents one of the first to evaluate, through an LMA network, the diurnal cycle of lightning properties in the MASP. Analyzing the diurnal variability of convective behavior through the characterization of the diurnal cycle of total lightning occurrence is of critical importance. Given the high incidence of lightning in Brazil, conducting further in-depth studies is essential to enhance the understanding of this phenomenon.
Analysis of the BrasilDAT data revealed that the most prevalent type of lightning is intracloud (91%). January (summer) exhibited the highest frequency of lightning events (322,598 occurrences), of which 294,259 were IC lightning, occurring most often around 2:00 pm local time. Most of the lightning events occurred during the afternoon peak in convective activity. The observed peak current values predominantly consist of higher positive polarity currents, with December showing the highest peak current values.
Regarding the SPLMA data, it is noted that the duration, number of sources, altitude, and length of lightning events are all greater during the early morning hours. The frequency of lightning events is highest in the afternoon, indicating that when the number of lightning events is lower, their duration and length tends to be longer. In addition, there is a predominance of −CG lightning over +CG lightning for all months analyzed. Regions such as the MASP typically experience the formation of deep convective clouds, which generate a higher occurrence of negatively polarized lightning.
The use of three-dimensional networks, such as LMA networks, has enabled a better understanding of the microphysical processes of thunderstorms over the Metropolitan Region of São Paulo and contributes to short-term forecasting (nowcasting) to mitigate the adverse effects of thunderstorms. For future studies, it is recommended to analyze additional instruments available during the CHUVA-Vale campaign, such as other lightning location systems and total lightning measured by satellites (Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measurement Mission satellite (TRMM)), disdrometers, radiosondes, and satellite data.

Author Contributions

Conceptualization, R.G.P., E.V.M. and T.S.B.; methodology, R.G.P., E.V.M. and T.S.B.; software, R.G.P.; formal analysis, R.G.P., E.V.M. and T.S.B.; writing—original draft preparation, R.G.P., E.V.M., T.S.B. and M.S.R.; writing—review and editing, R.G.P., E.V.M., T.S.B. and M.S.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Council for Scientific and Technological Development (CNPq) (grant 427673/2018-6). The CHUVA Project was supported by the São Paulo State Research Foundation (FAPESP) (grant 2009/15235-8). The authors thank the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001) for granting scholarships to the first author via master’s scholarships. We also thank the Minas Gerais State Research Foundation (FAPEMIG).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All datasets are freely available, including the campaign experiment (https://ftp.cptec.inpe.br/chuva/glm_vale_paraiba/, accessed on 10 March 2022).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wallace, J.M.; Hobbs, P.V. Atmospheric Science: An Introductory Survey; Elsevier Academic Press: Cambridge, MA, USA, 2006; 483p. [Google Scholar]
  2. Reynolds, S.E.; Brook, M.; Gourley, M.F. Thunderstorm charge separation. J. Meteorol. 1957, 14, 426–436. [Google Scholar] [CrossRef]
  3. Ogawa, T. Lightning currents. In Handbook of Atmospheric Electrodynamics; Volland, H., Ed.; CRC Press: Boca Raton, FL, USA, 1995; pp. 93–136. [Google Scholar]
  4. Pinto, J.R.O.; Pinto, I.R.C.A. Tempestades e Relâmpagos no Brasil; Instituto Nacional de Pesquisas Espaciais (INPE): São José dos Campos, Brazil, 2000; 185p. [Google Scholar]
  5. Rakov, V.A.; Uman, M.A. Lightning: Physics and Effects; Cambridge University Press: Cambridge, UK, 2003; 850p. [Google Scholar]
  6. Cecil, D.J.; Buechler, D.E.; Blakeslee, R.J. Gridded lightning climatology from TRMM-LIS and OTD: Dataset description. Atmos. Res. 2014, 135–136, 404–414. [Google Scholar] [CrossRef]
  7. Albrecht, R.I.; Goodman, S.J.; Buechler, D.E.; Blakeslee, R.J.; Christian, H.J. Where are the lightning hotspots on earth? Bull. Am. Meteorol. Soc. 2016, 97, 2051–2068. [Google Scholar] [CrossRef]
  8. Oda, P.S.S.; Enoré, D.P.; Mattos, E.V.; Weber, A.G.; Albrecht, R.I. An initial assessment of the distribution of total Flash Rate Density (FRD) in Brazil from GOES-16 Geostationary Lightning Mapper (GLM) observations. Atmos. Res. 2022, 270, 106081. [Google Scholar] [CrossRef]
  9. Cardoso, I.; Pinto, J.R.O.; Pinto, I.R.C.A.; Holle, R. Lightning casualty demographics in Brazil and their implications for safety rules. Atmos. Res. 2014, 135–136, 374–379. [Google Scholar] [CrossRef]
  10. Mecikalski, R.M.; Bain, A.L.; Carey, L.D. Radar and lightning observations of deep moist convection across Northern Alabama during DC3: 21 May 2012. Mon. Weather. Rev. 2015, 143, 2774–2794. [Google Scholar] [CrossRef]
  11. Wallace, J.M. Diurnal variations in precipitation and thunderstorm frequency over the conterminous United States. Mon. Weather Rev. 1975, 103, 406–419. [Google Scholar] [CrossRef]
  12. Orville, R.E.; Huffines, G.; Gammon, J.N.; Zhang, R.; Ely, B.; Steige, S.; Phillips, S.; Allen, S.; Read, W. Enhancement of cloud-to-ground lightning over Houston, Texas. Geophys. Res. Lett. 2000, 28, 2597–2600. [Google Scholar]
  13. Nascimento, M.G. Análise dos Impactos Dos Jatos de Baixos Níveis sobre a Bacia do Prata. Master’s Thesis, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, Brazil, 2008. [Google Scholar]
  14. Paegle, J.; Zhang, C.D.; Baumhefner, D.P. Atmospheric response to tropical thermal forcing in real-data integrations. Mon. Weather. Rev. 1987, 115, 2975–2995. [Google Scholar]
  15. Garreaud, R.D.; Wallace, J.M. The diurnal march of convective cloudiness over the Americas. Mon. Weather. Rev. 1997, 125, 3157–3171. [Google Scholar] [CrossRef]
  16. Williams, E.R.; Heckman, S.J. The local diurnal variation of cloud electrification and the global diurnal variation of negative charge on the Earth. J. Geophys. Res. 1993, 98, 5221–5234. [Google Scholar] [CrossRef]
  17. Williams, E.; Rothkin, K.; Stevenson, D.; Boccipio, D. Global lightning variations caused by changes in thunderstorm flash rate and by changes in the number of thunderstorms. J. Appl. Meteorol. 2000, 39, 1965–1982. [Google Scholar] [CrossRef]
  18. Pinto, J.R.O.; Pinto, I.R.C.A.; Diniz, J.H.; Cazetta Filho, B.; Cherchiglia, L.C.L.; Carvalho, B.M. A seven-year study about the negative cloud-to-ground lightning flash characteristics in southeastern Brazil. J. Atmos. Sol. -Terr. Phys. 2003, 65, 739–748. [Google Scholar] [CrossRef]
  19. Naccarato, K.P. Análise das Características dos Relâmpagos na Região Sudeste do Brasil. Ph.D. Thesis, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, Brazil, 2005. [Google Scholar]
  20. Bailey, J.C.; Blakeslee, R.J.; Carey, L.D.; Goodman, S.J.; Rudlosky, S.D.; Albrecht, R.; Morales, C.A.; Anselmo, E.M.; Neves, J.R.; Buechler, D.E. São Paulo Lightning Mapping Array (SP-LMA): Network assessment and analyses for intercomparison studies and GOES-R proxy activities. In Proceedings of the International Conference on Atmospheric Electricity (ICAE 2014), Norman, OK, USA, 15–20 June 2014. [Google Scholar]
  21. Chronis, T.; Lang, T.; Koshak, W.; Blakeslee, R.; Christian, H.; Maccaul, E.; Bailey, J. Diurnal characteristics of lightning flashes detected over the São Paulo Lightning Mapping Array. J. Geophys. Res. Atmos. 2015, 120, 11799–11808. [Google Scholar] [CrossRef]
  22. Paulucci, T.B.; França, G.B.; Libonati, R.; Ramos, A.M. Long-term spatial–temporal characterization of cloud-to-ground lightning in the Metropolitan Region of Rio de Janeiro. Pure Appl. Geophys. 2019, 176, 5161–5175. [Google Scholar] [CrossRef]
  23. Kousky, V. Frontal influences on Northeast Brazil. Mon. Weather. Rev. 1979, 107, 1140–1153. [Google Scholar] [CrossRef]
  24. Natan, C.O.N.; Machado, G.P.H.; Reboita, M.S.; Reis, A.L. Assessment of the Intertropical Convergence Zone over the Atlantic Ocean through an Algorithm Based on Precipitation. J. Atmos. Sci. Res. 2024, 7, 59–73. [Google Scholar] [CrossRef]
  25. Cohen, J.; Silva Dias, M.; Nobre, C. Environmental conditions associated with Amazonian squall lines: A case study. Mon. Weather. Rev. 1995, 123, 3163–3174. [Google Scholar] [CrossRef]
  26. Rison, W.; Thomas, R.J.; Krehbiel, P.R.; Hamlin, T.; Harlin, J. A GPS-based three-dimensional lightning mapping system: Initial observations in central New Mexico. Geophys. Res. Lett. 1999, 26, 3573–3576. [Google Scholar] [CrossRef]
  27. Machado, L.A.T.; Silva Dias, M.A.F.; Morales, C.; Fisch, G.; Vila, D.; Albrecht, R.; Goodman, S.J.; Calheiros, A.J.P.; Biscaro, T.; Kummerow, C.; et al. The CHUVA Project: How does convection vary across Brazil? Bull. Am. Meteorol. Soc. 2014, 95, 1365–1380. [Google Scholar] [CrossRef]
  28. Instituto Brasileiro de Geografia e Estatística (IBGE). Available online: https://www.ibge.gov.br (accessed on 15 September 2021).
  29. Centro de Estudos da Metrópole (CEM). Base de Dados. Contornos Perimétricos e Dados dos 162 Distritos da RMSP, Obtidos Conforme Quadro Político-Administrativo de 01 jan. 2001. Available online: https://centrodametropole.fflch.usp.br/pt-br (accessed on 20 September 2021).
  30. Zhou, J.; Lau, K.M. Does a monsoon climate exist over South America? J. Clim. 1998, 11, 1020–1040. [Google Scholar] [CrossRef]
  31. Marengo, J.A.; Liebmann, B.; Grimm, A.M.; Misra, V.; Silva Dias, P.L.; Cavalcanti, I.F.A.; Carvalho, L.M.V.; Berbery, E.H.; Ambrizzi, T.; Vera, C.S.; et al. Recent developments on the South American monsoon system. Int. J. Climatol. 2012, 32, 1–21. [Google Scholar] [CrossRef]
  32. Ferreira, G.W.S.; Reboita, M.S. A new look into the South America precipitation regimes: Observation and forecast. Atmosphere 2022, 13, 873. [Google Scholar] [CrossRef]
  33. Reboita, M.S.; Krusche, N.; Ambrizzi, T.; da Rocha, R.P. Entendendo o tempo e o clima na América do Sul. Terrae Didat. 2012, 8, 34–50. [Google Scholar] [CrossRef]
  34. Naccarato, K.P.; Saraiva, A.C.V.; Saba, M.M.F.; Schumann, D.C. First performance analysis of BrasilDAT total lightning network in southeastern Brazil. In Proceedings of the International Conference on Grounding and Earthing & International Conference on Lightning Physics and Effects, Bonito, Brazil, 25–29 November 2012. [Google Scholar]
  35. Williams, E.R.; Mattos, E.V.; Machado, L.A.T. Stroke multiplicity and horizontal scale of negative charge regions in thunderclouds. Geophys. Res. Lett. 2016, 43, 5460–5466. [Google Scholar] [CrossRef]
  36. Mattos, E.V.; Machado, L.A.T.; Williams, E.R.; Goodman, S.J.; Blakeslee, R.J.; Bailey, J.C. Electrification life cycle of incipient thunderstorms. J. Geophys. Res. Atmos. 2017, 122, 4670–4697. [Google Scholar] [CrossRef]
  37. Bruning, E.C.; Macgorman, D.R. Theory and observations of controls on lightning flash size spectra. J. Atmos. Sci. 2013, 70, 4012–4029. [Google Scholar] [CrossRef]
  38. Colgate, S.A. Planetary Electrodynamics; Coroniti, S.C., Hughes, J., Eds.; CRC Press: New York, NY, USA, 1969; pp. 144–145. [Google Scholar]
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Figure 1. (a) Location of the study region (red circle) in relation to São Paulo state and Brazil, and (b) zoomed-in view of the study region showing the location of the SPLMA (white circle) and BrasilDAT (blue cross) lightning sensors. The big red circle encompasses the region that the LMA network covers with the best detection efficiency (<100 km) and the black line represents the limits of the MASP region.
Figure 1. (a) Location of the study region (red circle) in relation to São Paulo state and Brazil, and (b) zoomed-in view of the study region showing the location of the SPLMA (white circle) and BrasilDAT (blue cross) lightning sensors. The big red circle encompasses the region that the LMA network covers with the best detection efficiency (<100 km) and the black line represents the limits of the MASP region.
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Figure 2. Diurnal cycle of intracloud lightning (IC, curve in black) and negative (−CG, curve in blue) and positive (+CG, curve in red) cloud-to-ground lightning from the BrasilDAT in number of events per month per hour for: (a) November and (b) December of 2011; and (c) January, (d) February, and (e) March of 2012. (f) Total number of lightning events recorded between November 2011 and March 2012. The gray dashed vertical lines represent the average time of sunrise and sunset in each month.
Figure 2. Diurnal cycle of intracloud lightning (IC, curve in black) and negative (−CG, curve in blue) and positive (+CG, curve in red) cloud-to-ground lightning from the BrasilDAT in number of events per month per hour for: (a) November and (b) December of 2011; and (c) January, (d) February, and (e) March of 2012. (f) Total number of lightning events recorded between November 2011 and March 2012. The gray dashed vertical lines represent the average time of sunrise and sunset in each month.
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Figure 3. Diurnal cycle of peak current (kA) of negative (−CG, curve in blue) and positive (+CG, red curve) cloud-to-ground from the BrasilDAT for: (a) November and (b) December of 2011; and (c) January, (d) February, and (e) March of 2012. (f) Average peak current of lightning events recorded between November 2011 and March 2012. The gray dashed vertical lines represent the average time of sunrise and sunset in each month.
Figure 3. Diurnal cycle of peak current (kA) of negative (−CG, curve in blue) and positive (+CG, red curve) cloud-to-ground from the BrasilDAT for: (a) November and (b) December of 2011; and (c) January, (d) February, and (e) March of 2012. (f) Average peak current of lightning events recorded between November 2011 and March 2012. The gray dashed vertical lines represent the average time of sunrise and sunset in each month.
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Figure 4. Diurnal cycle of lightning frequency (curve in green, in events month−1 hour−1), height of VHF sources (curve in gray, in kilometer), VHF source frequency (curve in red, in events month−1 hour−1), length (curve in black, in kilometer), and duration (curve in blue, in seconds) of lightning from the SPLMA network for (a) November and (b) December 2011; and (c) January, (d) February, and (e) March 2012. (f) Average of lightning properties recorded between November 2011 and March 2012. In Figure 4f, the lightning frequency represents the total number of lightning events from November 2011 to March 2012. The gray dashed vertical lines represent the average time of sunrise and sunset in each month.
Figure 4. Diurnal cycle of lightning frequency (curve in green, in events month−1 hour−1), height of VHF sources (curve in gray, in kilometer), VHF source frequency (curve in red, in events month−1 hour−1), length (curve in black, in kilometer), and duration (curve in blue, in seconds) of lightning from the SPLMA network for (a) November and (b) December 2011; and (c) January, (d) February, and (e) March 2012. (f) Average of lightning properties recorded between November 2011 and March 2012. In Figure 4f, the lightning frequency represents the total number of lightning events from November 2011 to March 2012. The gray dashed vertical lines represent the average time of sunrise and sunset in each month.
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Table 1. Synthesis of the lightning properties analyzed in this study.
Table 1. Synthesis of the lightning properties analyzed in this study.
SPLMA Network
Physical PropertiesDefinition [Unit]
VHF source frequency Number of VHF sources per hour [events/month.hour]
Flash frequencyNumber of flashes per hour [events/month.hour]
Flash duration Time difference between the occurrence of
the last and the first source of the flash [s]
Flash height Mean height of VHF sources of flash [km]
Flash lengthSquare root of the polygon area calculated
with the polygon that connects the outermost
VHF sources (convex hull) [km]
BrasilDAT Network
Physical PropertiesDefinition [Unit]
Intracloud lightning frequencyNumber of lightning events per hour
[events/month.hour]
Negative cloud-to-ground lightning frequencyNumber of lightning events per hour
[events/month.hour]
Positive cloud-to-ground lightning frequencyNumber of lightning events per hour
[events/month.hour]
Peak current of negative cloud-to-ground
lightning 		Lightning intensity [kA]
Peak current of positive cloud-to-ground
lightning 		Lightning intensity [kA]
Table 2. Number and percentage (%) of lightning events detected by the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) during the 5 months (November 2011 to March 2012) of the CHUVA-Vale campaign.
Table 2. Number and percentage (%) of lightning events detected by the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) during the 5 months (November 2011 to March 2012) of the CHUVA-Vale campaign.
November%December%January%February%March%Total
BrasilDAT72,6036241,26321322,59828303,80726218,264191,158,535
SPLMA38,412861,08113110,38224150,56632106,71923467,160
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MDPI and ACS Style

Pereira, R.G.; Mattos, E.V.; Biscaro, T.S.; Reboita, M.S. Temporal Evolution of Lightning Properties in the Metropolitan Area of São Paulo (MASP) During the CHUVA-Vale Campaign. Atmosphere 2025, 16, 426. https://doi.org/10.3390/atmos16040426

AMA Style

Pereira RG, Mattos EV, Biscaro TS, Reboita MS. Temporal Evolution of Lightning Properties in the Metropolitan Area of São Paulo (MASP) During the CHUVA-Vale Campaign. Atmosphere. 2025; 16(4):426. https://doi.org/10.3390/atmos16040426

Chicago/Turabian Style

Pereira, Raquel Gonçalves, Enrique Vieira Mattos, Thiago Souza Biscaro, and Michelle Simões Reboita. 2025. "Temporal Evolution of Lightning Properties in the Metropolitan Area of São Paulo (MASP) During the CHUVA-Vale Campaign" Atmosphere 16, no. 4: 426. https://doi.org/10.3390/atmos16040426

APA Style

Pereira, R. G., Mattos, E. V., Biscaro, T. S., & Reboita, M. S. (2025). Temporal Evolution of Lightning Properties in the Metropolitan Area of São Paulo (MASP) During the CHUVA-Vale Campaign. Atmosphere, 16(4), 426. https://doi.org/10.3390/atmos16040426

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