May2024_TEC_satellite_data

Swarm, DMSP, CSES, and CSS satellite data used in this study are archived as per-orbit files in the subdirectories 'Swarm', 'DMSP', 'CSES', and 'CSS', respectively. For these per-orbit files, the key plasma density variables used in this study are:- DMSP: 'sum_Ion_density' (total ion density, cm⁻³)- Swarm: 'electron_density' (electron density, cm⁻³)- CSS: 's_denity_O' (O⁺ ion density, m⁻³)- CSES: 'A311' (electron density, m⁻³) Geographic and geomagnetic coordinates (e.g. 'geoLongitude', 'geoLatitude', 'magLatitude') follow directly from their column names in each file. The TEC data are provided at two spatial resolutions. The subdirectory 'TEC_01' contains gridded TEC maps with a spatial resolution of 0.1° × 0.1°, whereas the subdirectory 'TEC_2' contains gridded TEC maps with a spatial resolution of 2° × 2°. Both TEC datasets cover the storm interval with a temporal resolution of 5 minutes. Each file in these folders, for example, '2024-05-10 00-00-00_5min.parquet' and '2024-05-10 00-05-00_5min.parquet', corresponds to a single epoch and is stored in parquet format. For each epoch, the file provides one TEC map with the following columns: 'geo_Longitude', 'geo_Latitude', and 'tec'. The two TEC datasets use the same file-naming convention and variable names, differing only in spatial resolution. The subdirectory ionosonde contains ionosonde observations and related processed data for 9 and 11 May 2024. The MATLAB (.mat) files provide measurements from four stations—Dandong, Dulan, Wuhan, and Zhoushan—and contain three variables: UTh (universal time in hours), foF2 (F2-layer critical frequency, MHz), and hmF2 (F2-layer peak height, km). The directory also includes ionosonde electron-density profile data in Parquet format for Dandong, Wuhan, and Zhoushan, together with corresponding 2°-grid GNSS TEC data for these three locations. All datasets in this repository are provided in standard file formats, including '*.parquet', '*.pkl', and '*.mat', which can be easily read in Python using, for example, pandas ('read_parquet'), the standard 'pickle' module, and scipy.io ('loadmat').