Long term-extension of ICMEs and SIRs catalogs with deep learning and geomagnetic indices

Space weather event catalogs are essential tools for characterizing the near-Earth space environment. From a scientific standpoint, these catalogs provide extensive statistical insights into the physical properties of such events. Operationally, they support forecasting scenarios by offering a basis to assess the diverse impacts these events may have on the near-Earth space environment.

Interplanetary Coronal Mass Ejections (ICMEs) and Stream Interaction Regions (SIRs) are two of the most significant drivers of space weather disturbances. Traditional catalogs of these large-scale solar wind structures are primarily built using in-situ measurements from L1 monitors like WIND, ACE, and DSCOVR. However, these datasets primarily cover the period after 1995, limiting the temporal scope of current catalogs.

Conversely, geomagnetic indices have recorded Earth’s geomagnetic activity for several decades before the advent of the space era. These indices have been shown to respond differently to ICMEs and SIRs (e.g., Benacquista et al., 2017; Bernoux and Maget, 2020), making them a valuable resource for identifying these events in earlier periods.

In this study, we adapt an existing deep learning-based method—originally developed for detecting ICMEs and SIRs using L1 solar wind data—to analyze geomagnetic index measurements. While the geomagnetic-based approach is inherently less precise than its solar wind counterpart, it successfully identifies time intervals likely associated with ICMEs or SIRs

This method is used to extend existing ICME and SIR catalogs back in time to cover the period from 1870 to 1995. Although the resulting extension is not exhaustive, it captures the most geoeffective events, offering a valuable dataset for long-term climatological studies of space weather. This work lays the groundwork for future research aimed at understanding historical space weather trends and their implications for Earth's near-space environment.

This work was supported by both the FARBES (Forecast of Actionable Radiation Belts Scenarios) project, funded by the European Union's Horizon Europe research and innovation programme under grant agreement No 101081772 and ONERA internal fundings, through the federated research project PRF-FIRSTS.