Impact of May 2024 Geomagnetic Superstorm on the Submarine Cables
Bibliographic record
Abstract
During the first half of May 2024, solar active regions (ARs) 3663 and 3664 produced numerous solar flares, SEPs, and associated CMEs, which created major geospace disturbances. Specifically, the CME associated with the aforementioned ARs caused G5 geomagnetic storms, pushing Kp beyond 9 and Dst below -400 nT, creating a superstorm. During the storm, we observed large fluctuations in the magnetospheric and ionospheric current systems, recorded by space-borne and ground-based instruments such as magnetometers, coherent radars, and ISRs. Previous studies reported possible hazards of storm induced ground electric fields (GEF) and associated geomagnetically induced currents (GICs). While magnetic superstorms are known to be known to permanently damage ground-based transformers and lead to blackouts [1], we have not had a clear vision of the impact of these superstorms on underwater electrical equipment such as submarine cables, due to the low accessibility of underwater measurements. The March 1989 storm, the largest superstorm of the last century, caused widespread effects on power systems, including a blackout of the Hydro-Quebec system, and created rapid fluctuations on submarine cables [2, and references therein]. Due to the lack of underwater measurements, the general understanding of submarine cable risk and vulnerability during superstorms is limited. We have recently developed a capability to model (SCUBAS) the impact of storm-driven GICs on underwater cables [3]. In this study, we will leverage this capability to estimate the voltage fluctuations during the May 2024 superstorm and compare them with the March 1989 superstorm. We will also include the variability in the model parameters and uncertainties in the inputs to create ensemble outputs from SCUBAS to quantify the uncertainties. This study will be the steppingstone towards validating the SCUBAS estimates during extreme space weather events and will provide insight into the health of the underwater electronics during these types of space weather hazards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".