# Resonant Infrasonic Disturbances in Total-Electron-Content During a Severe Thunderstorm on 23 October 2021

R. H. Honda, E. A. Kherani, C. A. O. B. Figueiredo, E. Astafyeva, C. M.

Wrasse, K. P. Naccarato

► To cite this version:

R. H. Honda, E. A. Kherani, C. A. O. B. Figueiredo, E. Astafyeva, C. M. Wrasse, et al. Resonant Infrasonic Disturbances in Total-Electron-Content During a Severe Thunderstorm on 23 October 2021. Journal of Geophysical Research Space Physics, 2025, 130, pp.2024JA033679. 10.1029/2024JA033679. insu-05080922

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RESEARCH ARTICLE

10.1029/2024JA033679

**Key Points:**

* Detection of amplifications on the TEC disturbances in proximity to lightning clusters associated with deep convective cloud tops

* Detection time of 9 min, phase speed of 1,100 m/s, and spectral peak at 4.8 mHz linked to the resonant response of Acoustic wave energetics

* TEC detection time from peak LFR is tested for the first time to identify disturbance source and understand infrasonic coupling energetics

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

R. H. Honda,
hisashi.brase@gmail.com

**Citation:**

Honda, R. H., Kherani, E. A., Figueiredo, C. A. O. B., Astafyeva, E., Wrasse, C. M., & Naccarato, K. P. (2025). Resonant infrasonic disturbances in total-electron-content during a severe thunderstorm on 23 October 2021. *Journal of Geophysical Research: Space Physics*, 130, e2024JA033679. https://doi.org/10.1029/2024JA033679

Received 19 DEC 2024
Accepted 10 MAR 2025

©2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Resonant Infrasonic Disturbances in Total-Electron-Content During a Severe Thunderstorm on 23 October 2021

R. H. Honda¹,² ID, E. A. Kherani¹ ID, C. A. O. B. Figueiredo¹ ID, E. Astafyeva² ID, C. M. Wrasse¹ ID, and K. P. Naccarato¹ ID

¹Instituto Nacional de Pesquisas Espaciais, São José dos Campos, Brazil, ²Université Paris Cité, Institut de Physique du Globe de Paris (IPGP), CNRS UMR7154, Paris, France

**Abstract** Deep convective clouds and lightning activity during thunderstorms imprint Infrasonic (>3 mili Hertz) oscillations in the ionospheric density or Traveling Ionospheric Disturbances (TIDs). The wave characteristics of these oscillations and the coupling mechanisms remain a subject of investigation, noting that the coupling energetics may alter the spectral and propagation characteristics. Moreover, the availability of numerous convective dynamics time scales makes the oscillation detection time uncertain. To study these aspects, the present work examines the spatial-temporal lightning flash rate during a severe thunderstorm (cloud top temperature <-80°C) from the GOES16 infrared channel and the total electron content of the ionosphere from the GNSS network over the tropical Southern Hemisphere. The study finds TIDs amplification above the deep convective clouds. The strongest amplification occurs at the earliest, at 9 min, from the most intense lightning flash rate and propagates at the most probable speed of 400-1,100 m/s. In contrast to the spectral peak of the active storm, which is 1.2 mHz, the spectral peak of TIDs is 4.8 mHz. The results highlight the magnitude of coupling energetics to determine the wave propagation characteristics of infrasonic TIDs.

## 1. Introduction

The interaction between warm air masses and cold air masses creates atmospheric instabilities, leading to convective systems. Convective systems are characterized by rising air parcels, which are associated with CAPE (Convective Available Potential Energy). The larger CAPE, the faster the updraft velocities, allowing the parcel to reach higher altitudes, cooling down, and releasing latent heat, defining the cloud tops and setting severe weather hazards such as deep convection and thunderstorms (Rudlosky & Fuelberg, 2013).

Several studies have reported the detection of atmospheric and ionospheric disturbances in 3–10 mHz during deep convection, thunderstorms, and tornados (Astafyeva, 2019; Azeem et al., 2015; Figueiredo et al., 2018, 2023; Lay et al., 2015; Nishioka et al., 2013). These infrasonic disturbances have been associated with tropospheric thunderstorms since the 1960s (Baker & Davies, 1969; Davies & Jones, 1971; Georges, 1968). Davies and Jones (1973) employed a ray-tracing analysis and found the source of these oscillations near thunderstorms. Later studies associate the disturbances with the overshooting top, updraft, and downdraft regions of deep convective clouds within the thunderstorm (Lay et al., 2013, 2015; Marshall & Snively, 2014; Nishioka et al., 2013; Shao & Lay, 2016; Sindelarova et al., 2009). The simulation studies find the acoustic waves from latent heat and mechanical wind forcing associated with the thunderstorm can attain significant amplitudes at thermospheric heights and give rise to detectable infrasonic disturbances (Hickey et al., 2001; Walterscheid, 2003; Zettergren & Snively, 2013, 2015). The dynamics governing the generation and response of near-surface and low thermosphere wave propagation encompass a multitude of concurrent processes that occur within the atmosphere of the earth (Hines, 1960; Hooke, 1968; Baker & Davies, 1969; Hargreaves, 1992; Naugol'nykh & Ostrovsky, 1998; Zettergren & Snively, 2013; Zeld'ovich, 2002).

Total-Electron-Content (TEC) measurements from GNSS network had unfolded various aspects of infrasonic disturbances such as the concentric wavefronts emerging above the deep convective clouds, horizontal phase speeds close to the sound speed at ionospheric heights and spectral dominance of 3–5 mHz frequencies (Azeem et al., 2015; Lay et al., 2015; Nishioka et al., 2013). These studies have opened a new frontier to probe the infrasonic coupling energetics to understand the diverse nature of TIDs of thunderstorm origin. For example, the origin time of TIDs remains uncertain due to the uncertainty in the reference time definition of the thunderstorm. Though the TIDs spectrum is often reported, the source spectrum that drives the TIDs remains unattainded.
Independently, the thunderstorms themselves produce infrasonic thunder in 0.2–2 Hz caused by the conversion of the electrostatic field of thunderclouds into sound (Pasko, 2009).

On the 23rd of October 2021, the South American region experienced a Mesoscale Convective System (MCS) characterized by deep convection and Cloud Tops temperatures below –70 °C. The cloud tops are visible in satellite imagery by exhibiting enhanced brightness due to ice particles. The collision of ice particles leads to charge separation within the cloud, enhancing the electric field, and producing lightning. Thus, lightning activity has particular signatures that serve as indicators of the evolution of these convective systems, for example, the lightning jump, which is a rapid surge (less than 15 min) in the lightning flash rates (LFR) (Rudlosky & Fuelberg, 2013). This study will use LFR surge to identify strong updraft velocities, deep convection, and overshooting tops. The study investigates ionospheric response, based on TEC measurements over the MCS region. The objective is to examine the cause-and-effect relationship between LFR and TEC disturbances and to determine the detection time of TEC disturbances relative to the LFR surge. The innovative nature of work stems from the estimation of the detection time based on LFR surge that consistently explains the troposphere-ionosphere coupling energized by the Acoustic Gravity Waves (AGWs).

## 2. Data Processing Method

The sampling rate of the GOES-16 Infrared Channel, which is 10 min, constrains the analysis of waves with periods shorter than this resolution. This limitation is particularly relevant for analyzing Acoustic Gravity Waves (AGWs). As previously described, convective regions exhibit lightning activity because of the friction of ice particles, which is related to the release of latent heat of the convective dynamics. The occurrences of these lightning events are monitored by the EarthNetworks Total Lightning Network (ENTLN) (Zhu et al., 2022), which monitors those lightning strikes in terms of both timing and position. ENTLN can detect lightning with a resolution that is appropriate for our work. The region considered on the LFR encompasses latitudes ranging from –21° to –28° and longitudes spanning from –51° to –60°.

The total electron Content (TEC) estimation employs carrier-phase measurements from GNSS receivers with the following definition (Mannucci et al., 1998):

$$TEC = 40.3 \frac{(f_1^2 - f_2^2)}{f_1^2 f_2^2} (\phi_{f1} - \phi_{f2}) + \text{biases}, \quad (1)$$

Here, $\phi_{f1}$ and $\phi_{f2}$ are the carrier phases of the carrier frequencies $f_1$ and $f_2$ respectively. The phase measurements within 25° inclination angle of line-of-sight (LOS) are included. Moreover, the ionosphere is considered a thin layer at 350 km height. The absolute value of TEC is irrelevant when analyzing periodic disturbances, as it does not contribute any meaningful information to the discussion. Biases value are therefore disregarded, following the methodology of Astafyeva et al. (2022), where the initial value of the TEC time series is set to zero for each TEC curve. Subsequently, the TEC curves were detrended using a 15-min moving average to obtain the TEC-residual, referred to as dTEC (dTEC = TEC – TECav). These dTEC values were plotted along the IPP trajectories, enabling the estimation of the wavefront position and the horizontal phase velocity of the TIDs.

To generate the snapshots of dTEC map, the study follows a procedure similar to that used by Azeem et al. (2018): a grid of 0.04 by 0.04° was created and populated with the values of dTEC. This grid was then smoothed by averaging the values of all neighboring points within a 1-degree square distance. The position of the ionospheric piercing (IPP) point was considered to be the one that filled the grid. Note that the data used to populate the grid includes all available data from receivers and PRNs within the region.

The LFR data was organized at 15-s intervals, matching the sample rate of the TEC data. The LFR data aligned with the same epochs as the GNSS observations. A spectral analysis was performed on TEC along each IPP trajectory, to obtain temporal features, such as frequencies and the timing of the amplitude apex. The method applied is an adaptation of the approach proposed by (Torrence & Compo, 1998), and it is utilized for both lightning occurrence data and dTEC data.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Snapshots of the longitude-latitude map of Cloud Top Brightness Temperature (green-yellow contours), and dTEC (blue-red pixmap). The black clusters within the cloud top contours indicate where lightning flashes occurred. The black-dot curves represent the IPP trajectories of several satellite-receiver pairs (as indicated in the top legend box in b). The color box in the trajectories represents the approximate location of the IPP during 16:00UT–17:30UT. Each panel is composed of the time duration of ± 5 min from the time indicated in each frame: (a) 16:10 UT, (b) 16:20 UT, (c) 16:50 UT, and (d) 17:20 UT. The supplementary material in Supporting Information S1, labeled S1, presents more time frames.</figcaption></figure>

**3. Results**

Figure 1 shows a chronological sequence of the Cloud Top Brightness Temperature in °C and dTEC in TECU. The black clusters correspond to the position of lightning occurrences. The Video S1 in the support material presents the sequence in more time frames. The sequence shows the evolution of cloud top temperature from -60°C to -80°C during 16:00–17:20 UT. From 16:20 UT onwards, two regions of very cold brightness temperatures around 25°S, 58°W and 24°S, 54°W develop dense clusters of LFR. Although a wavefront cannot be clearly distinguished in the dTEC map, an increase in dTEC values is observed around the dense LFR clusters. This amplification becomes more intense at 17:20 UT over the cluster located at (25°S, 58°W). Additionally, a strong amplification of dTEC is also observed at 16:20 UT near the same cluster.

Figures 2a and 2b shows the TEC and dTEC variations along IPP trajectories of several receiver-satellite pairs. Figure 2c shows the LFR counted within the dense clusters. Total electron Content and dTEC develop the most intense oscillations during 17:00–17:15 UT. The LFR variation shows a most intense surge during 16:55–17:00 UT. Another set of dTEC oscillations is noted during 16:20–16:40 UT when considerable LFR activity is observed.

Figure 3 replots the dTEC curves for the receiver-satellite pairs MSBL-G29 and MSPM-G25 from Figure 2. These curves were selected since they show the earliest peak dTEC from the time of most intense LFR surge. We note that the time of peak LFR surge is 16:59:31UT and the time of most intensified dTEC is 17:08:15 UT from the MSBL-G29 pair that is, the earliest detection of dTEC-peak is about 9 min from the peak of LFR.

Figures 4a and 4b show the Travel-Time diagram of dTEC in longitude and latitude, respectively. Since the study monitors the most intensified dTEC, the corresponding peak dTEC value of the same phase from each trajectory is
<figure><img src="image_4.png" /><figcaption>**Figure 2.** The panels (a–b) depict the temporal variation of TEC-TEC₀ and dTEC in TECU (1 TECU = 10¹⁶ electrons/m²), along the IPP trajectories of numerous receiver-satellite pair as indicated in the legend box. The TEC₀ value is the initial TEC value of each time series. The dTEC curves are vertically offset by 0.3 units. Panel (c) shows the Lightning-Flash-Rate (LFR), counted at 15-s intervals within the lightning clusters.</figcaption></figure>

obtained. The lines are drawn from the linear regression of the coordinates of these iso-phase values. In Figure 4, it is evident that the best-fit lines pass through the iso-phase values of peak-dTEC. For example, in 4a, the lines mostly pass through the negative peak dTEC values, denoted by redvent. In 4b, the lines mostly pass through

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Red and yellow curves correspond to the dTEC variation for pairs MSBL-G29 and MSPM-G25, chosen from Figure 2b. The black curve corresponds to the LFR, which is the same as in Figure 2c. After the LFR jump at 16:59:31 UT, these two pairs register the earliest intensification in dTEC at 17:08:15UT and 17:10 UT.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** The Travel-Time diagram of dTEC in longitude in (a) and in latitude in (b). The blue and orange lines represent the linear regression of coordinates of the iso-phase values of the peak dTEC. The values in the legend box indicate the slope of the lines.</figcaption></figure>

the positive peak dTEC values, denoted by bluevent. The finite slopes of the lines reflect the phase shift of the
iso-phase oscillation in time with longitude and latitude, suggesting the phase propagation of the most intense
dTEC oscillations. The most probable phase speed is between 400 to 1,100 m/s, as determined from the slopes of
the lines. The coordinates of the intersecting point of two outgoing lines in the upper and lower panels are
(17:08UT, 58.1°W) and (17:08UT, 23.7°S), respectively. Therefore, the two outgoing lines intersect the longitude
of 58.1°W and the latitude of 23.7°S at 17:08 UT that is, the phase propagation traced back at 23.7°S, 58.1°W
about 9 min after the peak LFR. Thus, the origin coordinates of the most intense TIDs are at 23.7°S, 58.1°W.

Figures 5a and 5b show the spectra of dTEC from the pair MSJR-G29 and LFR, respectively. In contrast to the spectral peak of lightning occurrence at 1.2 mHz at 16:59 UT, the spectral peak of dTEC is around 4.8 mHz at 17:08:15 UT. The spectral analysis of dTEC oscillations from other IPP trajectories reveals a similar spectral behavior, showing a spectral peak at 4.7 mHz with a standard deviation of 0.2 mHz and the time of peak at 17:11 UT with a standard deviation of 3 min.

**4. Discussion**

It is commonly understood that the presence of cold cloud spots, observed by brightness temperature, is closely associated with the presence of an overshooting top (Nyassor, Wrasse, Paulino, Gobbi, et al., 2022; Nyassor, Wrasse, Paulino, São Sabbas et al., 2022; Wrasse et al., 2024). Even though they are small and short when compared to the whole storm activity, regions with Overshooting Tops often produce hazardous weather conditions such as aviation turbulence, high lightning activity, heavy rainfall, large hail, damaging wind, and tornadoes. In Figure 1, the strongest dTEC at 16:20 UT and 17:20 UT develops over the coldest brightness temperature and Overshooting Top surrounding the cluster at 25°S, 58°W. The time 16:20 UT is when significant amplification in the LFR first occurs in this cluster, and the time 17:20 UT is afterward the time of peak on LFR. The two sets of intensified dTEC oscillations in Figure 2 correspond to the two occasions of strongest dTEC development of Figure 1. The association of dTEC with lightning clusters and timing in the present study is consistent with the finding of Shao and Lay (2016) who reported a good coincidence between infrasonic TIDs and high lightning activity. They associate the infrasonic TIDs with the convectively quiet stratiform regions behind active cells.

The most intense and earliest dTEC oscillations occur at 17:08:15 UT, with a corresponding detection time of
9 min from the LFR jump. This interval is characteristic of an acoustic wave propagating upwards through the
<figure><img src="image_7.png" /><figcaption>**Figure 5.** The panels (a–b) depict the spectrograms of dTEC from pair MSPM-G25 and LFR, respectively. The spectral amplitudes in both panels are normalized to their respective peak values. The coordinates of these peaks are indicated by arrows.</figcaption></figure>

atmosphere. Based on the sound speed profile in the support material S2 in Supporting Information S1, the average sound speed on this day is about 450 m/s so the acoustic wave arrives in 9 min at the ionospheric height of 250 km, an altitude of considerable electron density.

The detection time provides insight into the coupling energetics and the kind of waves involved. More often, the detection time estimation is based on the ray-tracing analysis. The present study estimates the detection time from the time delay between the peak on the LFR and peak TEC residual. Therefore, the detection time of 9 min is in line with the sound ray travel time with the average speed and also in line with the AGWs travel time from the recent numerical simulation study by Sanchez et al. (2023).

The present study relies on detection time estimation from the time difference of peak oscillations, as considered in the case of TIDs of earthquake origin (Sanchez et al., 2023). The TIDs are generated from various lower atmospheric sources. The time difference based on emerging oscillations may lead to ambiguity in the presence of TIDs of different origins as their presence alters the reference line. However, the peak-to-peak difference ensures the cause-to-effect relationship with fewer ambiguities.

The most probable phase speed of 400–1,100 m/s in Figure 4 also associates the TIDs to the AGWs energetics. The estimated speed range is larger than the maximum sound speed in Figure S2 in Supporting Information S2. Recent studies by Azeem et al. (2018) and Nishioka et al. (2013) report the phase speed of TIDs higher than the sound speed during a tornado and a thunderstorm, as found in the present study. According to Azeem et al. (2018), the phase speed, not the group speed, may exceed the local sound speed. The study by Sanchez et al. (2023) finds the AGWs of earthquake origin propagating faster than the sound speed, responsible for early TIDs detections.

Figure 3 shows a distinct LFR jump occurring at 16:59 UT, resulting in a broader spectrum with increased energy at higher frequencies (Figure 5b). This serves as an energy driver, transferring energy from the lower atmosphere to the upper atmosphere. This process contributes to the upward propagation of AGWs, allowing them to penetrate the ionosphere. The spectral peak of the earliest detected TIDs is at 4.8 mHz, revealing the nearly monochromatic nature of TIDs. The monochromatic nature stems from the resonance energetics of AGWs producing TIDs with spectral peaks at 4.3–4.7 mHz as reported during thunderstorms (Azeem et al., 2018) and earthquakes (Rolland et al., 2011). A recent study by Sanchez and Kherani (2024) finds that the wave frequencies at the wavelengths longer than the size of the troposphere-mesosphere duct become shorter than the acoustic cutoff frequency around tropopause and mesopause. Such waves become evanescent and excite the resonance
mode and most intense AGWs in the 3.5–5 mHz range. The spectral peak at 4.8 mHz reflects the resonant response of AGWs and the ionosphere.

The presence of a spectral peak at 4.8 mHz ensures the intensified dTEC oscillation is caused by the forcing at the troposphere. Such spectral peaks are a feature of the troposphere-lower-thermosphere duct (Balachandran, 1968; Tahira, 1995). Moreover, the time lag between the LFR peak and the spectral peak is about 9 min, consistent with the sound ray travel time between the troposphere and the ionosphere. Therefore, spectral peak and detection time reinforce the cause-and-effect relationship between peak LFR and dTEC. Additionally, Figure 5a reveals a secondary spectral peak at 4.8 mHz between 16:20UT and 16:40 UT when another set of secondarily intensified dTEC oscillations is present in Figure 2. Interestingly, the LFR spectrum also shows its first intensification around this time at the frequency of around 1 mHz. Therefore, the first period of TIDs during 16:20UT–16:40 UT should be of convective origin. However, such a cause-and-effect relation is inconclusive for the weakly intensified TIDs in the presence of TIDs of non-convective origin. For this reason, the study focuses on the most intensified TIDs around 17:10 UT to ensure the cause-effect relation. The dynamics of the event marked by the LFR jump at 16:59 UT provide evidence that the enhanced TID amplitudes were indeed excited by energy transported through AGWs generated by the LFR event.

The infrasonic signal, for example, sound pressure from a thunderstorm possesses several time scales ranging from the thunderstorm duration to the infrasonic that is, between mHz to Hz. The lightning flash rate is confided into the 0.2–2 Hz residing in the main phase of about a Brunt-Vaisala period that is, of about minutes. The low-frequency-mHz infrasonic signal records the main phase, also found in the LFR spectrum at 1.2 mHz in Figure 5. Numerous studies reported lightning spectral peaks in infrasonic 0.2–2 Hz. However, spectral peaks at acoustic resonance modes are not known and the LFR spectrum in Figure 5 also does not find them. Therefore the magnitude of coupling energetics, energized by the Acoustic-Gravity Waves (AGWs) determines the wave propagation and spectral characteristics of infrasonic TIDs.

## 5. Summary

The severe thunderstorm occurred over tropical South America on 21 October 2021. During 16–18 UT, the active storm cells and lightning activities intensify, attaining the minimum brightness temperature during 16:50–17:20 UT with the peak on the LFR occurring at 16:59 UT. During this time, the TEC and TEC-residual register infrasonic oscillations in the region of active storm cells and attain the maximum TEC-residual over convective cloud 25°S, 58°W, characterized by the coldest brightness temperature of –80°C and an amplification on LFR. The time of most intensified oscillations varies from 17:08–17:12 UT that is, 9–12 min from peak on LFR, depending on the PRN and receivers. The earliest detection of TIDs in 9 min confirms the arrival time of Acoustic-Gravity Waves (AGWs) at ionospheric heights, as expected from ray tracing analysis and numerical simulation of AGWs from previous studies. The time lag in iso-phase oscillations with latitude and longitude reflects the phase propagation with the most probable speed of 400–1,000 m/s. Therefore, arrival time and phase speed associate the TIDs to the coupling energetics energized by the acoustic-gravity waves. At 9 min after 16:59:31 UT, the phase propagation of 400–1,100 m/s traces back to the 23.7°S, 58.1°W that is, above the lightning-producing clusters.

In contrast to the spectral peak of the active storm at 1.2 mHz at 16:59 UT, the spectral peak of the earliest detected TIDs is at 4.8 mHz, revealing the resonance response of the ionosphere during the occurrence of a high lightning flash rate.

The results highlight the magnitude of coupling energetics to determine the wave propagation characteristics of infrasonic TIDs. The earliest detection time is one of the products of the study, not commonly discussed in the literature. The limitation in detection time estimation from lower time resolution of cloud brightness temperature from GOES16 is mitigated by higher resolution lightning data. Monitoring the LFR with distinct intensification and overshooting signatures facilitated the detection time estimation of TIDs. This method is tested for the first time, potentially bringing out the infrasonic coupling energetics. The peak on LFR also yields the origin location of TIDs close to the coldest cloud top when the phase propagation is traced back to the peak time. Therefore, observations on peak of LFR of the lightning activity offers a consistent method to determine the origin location and detection time of TIDs. However, the consistency is subject to the spatial resolution of IPP trajectories, finding the peak LFR region and overshooting top within the deep convective clouds. The findings suggest the existence of a coupling between lightning cluster activity and infrasonic TIDs, with AGWs transporting energy
from the troposphere to the ionosphere. This provides new insights into the energetic interactions between atmospheric and ionospheric dynamics.

## Data Availability Statement

The TEC curves displayed in this research were calculated from RINEX files obtained from GNSS receivers around South America. RINEX files are available at different networks, including: IBGE (Instituto Brasileiro de Geografia e Estatística) under the network RBMC (Rede Brasileira de Monitoramento Continuo) via https://www.ibge.gov.br/en/geosciences/geodetic-positioning/geodetic-networks/20079-brazilian-network-for-continuous-monitoring-gnss-systems.html?lang=en-GB with open access for research and non-commercial use; RAMSAC (Red Argentina de Monitoreo Satelital Continuo) via https://www.ign.gob.ar/NuestrasActividades/Geodesia/Ramsac/DescargaRinex with open access for research and non-commercial use. A browser plugin can be used to view this page in English (Piñón et al., 2018); REGNA-ROU (Red Geodésica Nacional Activa de Uruguay) via https://igm.gub.uy/data is available with open access for research and non-commercial use; however, user registration is required. A browser plugin can be used to view this page in English. The Cloud Top Brightness Temperature data, used to analyze the overshooting top positions, are available at CPTEC (Centro de Previsão de Tempo e Estudos Climáticos) via https://ftp.cptec.inpe.br/goes/goes16/retangular/ch13/2021/10/ with open access for research and non-commercial use. AEM/EarthNetworks provided lightning data supporting this research under an NDA (Non-Disclosure Agreement). The raw data are not publicly accessible to the research community. Interested researchers may request access by contacting AEM/EarthNetworks through their website: https://aem.eco/our-brands/earth-networks/. Figures were made with Matplotlib version 3.5.1 (Caswell et al., 2021; Hunter, 2007). The publicly accessible data that supported this research can be accessed by [dataset] Honda (2025).

## Acknowledgments

We thank the Brazilian Ministry of Science, Technology and Innovation (MCTI) and the Brazilian Space Agency (AEB). Part of this work was carried out by R. H. Honda during the exchange program at IPGP/UPC, France, who acknowledges the financial support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Finance Code 001. under the process: 88887.877742/2023-00 and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) through grant 163874/2021-2 and The Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) under the process 2024/18564-2; E. A. Kherani acknowledges the financial support of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) through grant 307496/2015-5 and The Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) under the process 2022/03502-6; C. A. O. B. Figueiredo thanks to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (contract no.303871/2023-7) and Fundação de Apoio à Pesquisa do Estado da Paraíba (FAPESq) (contract no. 2417/2023) for supporting this research; E. Astafyeva acknowledges the support of the French National Research Agency (ANR, project "IONO-DIET", grant ANR-22-CE49-0011), and the French Space Agency (CNES), project "RealDetect". Also, we would like to thank AEM/EarthNetworks for providing the lightning data used in this study. The Article Processing Charge for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614).

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