Improvement of SuperDARN velocity measurements by estimating the index of refraction in the scattering region using interferometry
Bibliographic record
Abstract
In past calculations of convective velocities from Super Dual Auroral Radar Network (SuperDARN) HF radar observations, the refractive index in the scattering region has not been taken into account, and therefore the inferred ionospheric velocities may be underestimated. In light of the significant contribution by SuperDARN to ionospheric and magnetospheric research, it is important to refine the velocity determination. The refractive index in the ionosphere at SuperDARN observation F region altitudes has typical values between 0.8 and close to unity. In the scattering region, where conditions are more extreme, the index of refraction may be much lower. A simple application of Snell's law in spherical coordinates (Bouguer's law) suggests that a proxy for the index of refraction at the scattering location can be determined by measuring the elevation angle of the returned ionospheric radar signal. Using this approximation for refractive index, the Doppler velocity calculation can be refined for each SuperDARN ionospheric echo, using the elevation angles obtained from the SuperDARN interferometer data. A velocity comparison of DMSP and SuperDARN observations has revealed that the SuperDARN speeds were systematically lower than the DMSP speeds. A linear regression analysis of the velocity comparisons found a best fit slope of 0.74. When the elevation angle data were used to estimate refractive index, the best fit slope rose 12% to 0.83. As most SuperDARN radars employ an interferometer antenna array for elevation angle measurements, the improvement in velocity estimates can be done routinely using the method outlined in this paper.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".