## 2 Materials and Methods  
As mentioned above, solar activity plays a significant role in the ionization process of the upper atmosphere. We worked during the nighttime to reduce these effects on geomagnetically quiet days, with planetary index \(K_{p}<4\) and Disturbance Storm Time (DST) index \(-50\)nT\(<\)DST. \(K_{p}\) describes the average level of irregularities in the horizontal components of the Earth's magnetic field [1], while the DST index describes the intensity of the geomagnetic storm. Both indices were obtained from [26]. Thus, during the analysis period, 00:00 UTC-08:00 UTC (21:00 LT-05:00 LT), two days in 2018 with \(K_{p}\leq 2\) and \(-32\)nt \(\leq\) DST \(\leq\) 11 nT were selected: 10 November, with some electrical activity detected, and 9 September, without lightning. We worked in the region defined by latitudes 64\({}^{\circ}\) S-21\({}^{\circ}\) S and longitudes 80\({}^{\circ}\) W-53\({}^{\circ}\) W, as can be seen in Figure 1.  
The data used in this paper are as follows:  
* Atmospheric Electrical Activity (AEA) data detected by the Earth Networks Total Lightning Network (ENTLN) [27]. ENTLN uses broadband sensors with frequencies between 1 Hz and 12 MHz. ENTLN can distinguish between the type of discharge, i.e., Cloud-to-Ground (CG) and In-Cloud (IC) lightning. This network consists of more than 1800 sensors deployed in more than 100 countries [28]. There are 50 sensors installed in Argentina. The sensors record the electric field signals produced by lightning, which are sent to the central processor. Using the time-of-arrival (TOA) technique, the lightning is geolocated in real time [28]. The data obtained from this network are the type of discharge (CG or IC), the time of occurrence, the geolocation, the estimated peak current, the height of the detected IC discharge, the multiplicity  
Figure 1: Study region outlined in orange. of the discharges, and the number of sensors that detected the discharge. More information can be found in [29];
* Advanced Baseline Imaging (ABI) data from the GOES-R 16 series of Geostationary Operational Environmental Satellites (GOES), corresponding to channel 13, wavelength 10.3 \(\upmu\)m. The data are free and available at [30]. Detailed information on GOES-16 and the ABI sensor can be found in [31];
* GNSS observation data from the Crustal Dynamics Data Information System (CDDIS), [32], and the Argentine Continuous Satellite Monitoring Network (RAMSAC), [33]. Figure 2 shows the RAMSAC Stations used in this study. In order to analyze the ionospheric variations generated by thunderstorms, the following procedure was carried out. First, we used the cloud-top Brightness Temperature (BT), corresponding to channel 13, wavelength 10.3 \(\upmu\)m from the GOES-R 16 satellite, as a proxy for the optically thick cloud-top temperature [34]. Radiation at this wavelength (10.3 \(\upmu\)m) is not affected by absorption by atmospheric gases [35]. Using cloud-top Brightness Temperature (BT) and thunderstorm discharge data, the temporal evolution of thunderstorms on 10 November 2018 was studied. Statistical and descriptive analyses of the lightning detected were then performed. The analyzed parameters were the peak current and the lightning count--\"The lightning count is the number of lightning events that occurred\" [36]. We employed lightning counts as a proxy for convective storm activity, considering electrical activity as a proxy for dynamic and microphysical storm parameters. In addition, we classified the lightning strokes according to their type (i.e., CG and IC) and polarity (i.e., negative or positive). The GNSS data were then preprocessed with Bernese GNSS software version 5.2 [37]. This software uses the models recommended by the International Earth Rotation and  
Figure 2: RAMSAC stations [33]. Reference Systems Service (IERSS) [38]. These models incorporate corrections for oceanic and atmospheric tidal loading [39; 40] and absolute phase-center corrections for satellites and receivers from IGS.