HF ground scatter from the polar cap: Ionospheric propagation and ground surface effects
Bibliographic record
Abstract
In addition to being scattered by the ionospheric field‐aligned irregularities, HF radar signals can be reflected by the ionosphere toward the Earth and then scattered back to the radar by the rugged ground surface. These ground scatter (GS) echoes are responsible for a substantial part of the returns observed by HF radars making up the Super Dual Auroral Radar Network (SuperDARN). While a GS component is conventionally used in studying ionosphere dynamics (e.g., traveling ionospheric disturbances, ULF waves), its potential in monitoring the state of the scattering surface remains largely unexploited. To fill this gap, we investigated diurnal and seasonal variation of the ground echo occurrence and location from a poleward‐looking SuperDARN radar at Rankin Inlet, Canada. Using colocated ionosonde information, we have shown that seasonal and diurnal changes in the high‐latitude ionosphere periodically modulate the overall echo occurrence rate and spatial coverage. In addition, characteristics of GS from a particular geographic location are strongly affected by the state of the underlying ground surface. We have shown that (1) ice sheets rarely produce detectable backscatter, (2) mountain ranges are the major source of GS as they can produce echoes at all seasons of the year, and (3) sea surface becomes a significant source of GS once the Arctic sea ice has melted away. Finally, we discuss how the obtained results can expand SuperDARN abilities in monitoring both the ionosphere and ground surface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".