# Effects of the October 2024 Storm over the Global Ionosphere
## 2 Data and Methods
The Swarm mission consists of three identical satellites (Swarm A, B, and C) in near-polar orbits, with Swarm A and C initially orbiting at -465 km and Swarm B at -520 km [19]. These high-altitude, high-resolution observations are essential for capturing storm-induced changes in Ne at F-region altitudes. To support a comprehensive global-scale analysis of ionospheric variability, we employed TEC data derived from the Madrigal database ([[http://www.haystacks.mit.edu](http://www.haystacks.mit.edu)]([http://www.haystacks.mit.edu](http://www.haystacks.mit.edu)) accessed on 24 April 2025)--a widely recognized open-source platform developed by the MIT Haystack Observatory. Madrigal (Millstone Analysis and Data Reduction Interactive Graphical Analysis Language) is a distributed data system that hosts and serves a wide range of geospace science data, including ionospheric and upper atmospheric measurements. It enables users to perform advanced data searches and supports access to standardized data formats that are compatible with systems such as the NCAR CEDAR data archive [6]. Specifically, we utilized global GPS-derived TEC maps available through Madrigal's \"Distributed Ground-Based Satellite Receivers\" instrument category, and more precisely from the \"Worldwide GNSS Receiver Network\" dataset. This dataset provides near-real-time, globally distributed TEC maps generated from ground-based GNSS observations. The TEC values are mapped into a regular spatial grid of 1\({}^{\circ}\) latitude \(\times\) 1\({}^{\circ}\) longitude, with a temporal resolution of 5 min, offering high-resolution coverage suitable for monitoring large-scale ionospheric structures and their temporal evolution. For this study, we extracted global TEC maps spanning the interval from 18:00 UT on 10 October to 18:00 UT on 11 October. These maps represent processed and calibrated TEC data, which are derived through the integration of dual-frequency GNSS signal phase delays measured at a global network of ground-based receivers. The integration time for each global TEC map is approximately 20 min, providing a temporally smoothed view of ionospheric electron density distribution across the globe. The selected dataset spans more than two decades (1998 to present), making it one of the most comprehensive and accessible sources for long-term ionospheric research. To investigate the activity of LSTIDs during the main phase of the geomagnetic storm, we employed global detrended TEC (dTEC) maps spanning from 15:00 UT on 10 October to 03:00 UT on 11 October. These maps were obtained from the GPS-TEC database maintained by Nagoya University ([[https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/GLOBAL/MAP/index.html#Oct%202024](https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/GLOBAL/MAP/index.html#Oct%202024)]([https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/GLOBAL/MAP/index.html#Oct%202024](https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/GLOBAL/MAP/index.html#Oct%202024)) accessed on 26 April 2025), which offers multiple two-dimensional ionospheric data products, including the absolute TEC, the TEC difference ratio (rTEC), the detrended TEC (dTEC), and the ROTI. All data products are provided in geographic coordinates with a temporal resolution of 10 min. The dTEC maps used in this study are generated using data from over 9300 GNSS receivers distributed globally. The global RINEX files used for generating these maps were collected through international collaboration with the DRAWING-TEC project managed by the National Institute of Information and Communications Technology (NICT), Japan ([[https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/](https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/)]([https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/](https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/)) accessed on 26 April 2025). To derive dTEC values, the bias estimation technique proposed by [PERSON] et al. (2002) [20] was applied, ensuring accurate baseline corrections. The dTEC product specifically isolates ionospheric anomalies by removing long-term trends from the TEC data.