Multiinstrument observations of a geomagnetic storm and its effects on the Arctic ionosphere: A case study of the 19 February 2014 storm
Bibliographic record
Abstract
Abstract We present a multiinstrumented approach for the analysis of the Arctic ionosphere during the 19 February 2014 highly complex, multiphase geomagnetic storm, which had the largest impact on the disturbance storm‐time index that year. The geomagnetic storm was the result of two powerful Earth‐directed coronal mass ejections (CMEs). It produced a strong long lasting negative storm phase over Greenland with a dominant energy input in the polar cap. We employed global navigation satellite system (GNSS) networks, geomagnetic observatories, and a specific ionosonde station in Greenland. We complemented the approach with spaceborne measurements in order to map the state and variability of the Arctic ionosphere. In situ observations from the Canadian CASSIOPE (CAScade, Smallsat and IOnospheric Polar Explorer) satellite's ion mass spectrometer were used to derive ion flow data from the polar cap topside ionosphere during the event. Our research specifically found that (1) thermospheric O/N2 measurements demonstrated significantly lower values over the Greenland sector than prior to the storm time. (2) An increased ion flow in the topside ionosphere was observed during the negative storm phase. (3) Negative storm phase was a direct consequence of energy input into the polar cap. (4) Polar patch formation was significantly decreased during the negative storm phase. This paper addresses the physical processes that can be responsible for this ionospheric storm development in the northern high latitudes. We conclude that ionospheric heating due to the CME's energy input caused changes in the polar atmosphere resulting in Ne upwelling, which was the major factor in high‐latitude ionosphere dynamics for this storm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".