Refractive index effects on the scatter volume location and Doppler velocity estimates of ionospheric HF backscatter echoes
Bibliographic record
Abstract
Abstract. Ionospheric E×B plasma drift velocities derived from the Super Dual Auroral Radar Network (SuperDARN) Doppler data exhibit systematically smaller (by 20–30%) magnitudes than those measured by the Defence Meteorological Satellites Program (DMSP) satellites. A part of the disagreement was previously attributed to the change in the E/B ratio due to the altitude difference between the satellite orbit and the location of the effective scatter volume for the radar signals. Another important factor arises from the free-space propagation assumption used in converting the measured Doppler frequency shift into the line-of-sight velocity. In this work, we have applied numerical ray-tracing to identify the location of the effective scattering volume of the ionosphere and to estimate the ionospheric refractive index. The simulations show that the major contribution to the radar echoes should be provided by the Pedersen and/or escaping rays that are scattered in the vicinity of the F-layer maximum. This conclusion is supported by a statistical analysis of the experimental elevation angle data, which have a signature consistent with scattering from the F-region peak. A detailed analysis of the simulations has allowed us to propose a simple velocity correction procedure, which we have successfully tested against the SuperDARN/DMSP comparison data set.
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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 | 0.003 |
| 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".