Examining the Potential of the Super Dual Auroral Radar Network for Monitoring the Space Weather Impact of Solar X‐Ray Flares
Bibliographic record
Abstract
Abstract Increased electron density in the ionosphere due to photoionization by radiation emitted during a solar X‐ray flare impacts high‐frequency (HF) radio wave propagation. Shortwave fadeout (SWF) due to the enhanced D region absorption that results is characterized by the level of cosmic radio noise attenuation derived from riometer measurements. SWF impacts HF radio propagation and has been identified in the Super Dual Auroral Radar Network (SuperDARN) data. An X2.1 solar X‐ray flare that erupted on 11 March 2015 is examined to determine its effects on HF radio propagation. Riometer data indicate a sharp enhancement in absorption, which falls off with increasing solar zenith angle. SuperDARN radars observed a suppression of both ground scatter and ionospheric echoes. Ground scatter data indicated a rapid weakening of signal from far to near ranges followed by a ~20‐min interval of complete signal loss. Recovery lasted ~30 min and proceeded from near to far ranges. Prior to the complete signal loss, an apparent sharp velocity impulse (Doppler flash) lasting 1–2 min was observed in the ground scatter data. The peak of this flash preceded the onset of enhanced absorption. The onset of signal loss by SuperDARN preceded the onset of enhanced absorption observed by riometers. Both data sets observed a positive correlation between increasing delay in onset and increasing solar zenith angle with onset progressing at an average rate of 16.7°/min (0.060 min/°). Agreement between riometer and SuperDARN indicates the possibility of using a joint data set for improved monitoring of the space weather impact of solar X‐ray flares.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".