SuperDARN Radar‐Derived HF Radio Attenuation During the September 2017 Solar Proton Events
Bibliographic record
Abstract
Abstract Two solar proton events in September 2017 had a significant impact on the operation of the Super Dual Auroral Radar Network (SuperDARN), a global network of high‐frequency (HF) radars designed for observing F region ionospheric plasma convection. Strong polar cap absorption caused near‐total loss of radar backscatter, which prevented the primary SuperDARN data products from being determined for a period of several days. During this interval, the high‐latitude and polar cap radars measured unusually low levels of background atmospheric radio noise. We demonstrate that these background noise measurements can be used to observe the spatial and temporal evolution of the polar cap absorption region, using an approach similar to riometry. We find that the temporal evolution of the SuperDARN radar‐derived HF attenuation closely follows that of the cosmic noise absorption measured by a riometer. Attenuation of the atmospheric noise up to 10 dB at 12 MHz is measured within the northern polar cap, and up to 14 dB in the southern polar cap, which is consistent with the observed backscatter loss. Additionally, periods of enhanced attenuation lasting 2–4 hr are detected by the midlatitude radars in response to M‐ and X‐class solar flares. Our results demonstrate that SuperDARN's routine measurements of atmospheric radio noise can be used to monitor 8‐ to 20‐MHz radio attenuation from middle to polar latitudes, which may be used to supplement riometer data and also to investigate the causes of SuperDARN backscatter loss during space weather events.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".