Calibrating HF Radar Elevation Angle Measurements Using <i>E</i> Layer Backscatter Echoes
Bibliographic record
Abstract
Abstract One of the most important observed parameters of the ionospheric radar returns in the high‐frequency range is the vertical arrival angle (elevation angle), which provides information about ionospheric conditions along the propagation path, as well as characterizes the propagation mode of the radio signals. The most advanced network of ionospheric high‐frequency (HF) radars, SuperDARN (Super Dual Auroral Radar Network) utilizes two‐array interferometry to estimate this parameter. However, due to the intrinsic technical difficulties with direct (instrumental) calibration of a time offset between measurements made by the two arrays, td, the SuperDARN elevation angle data have been rarely used in the past. The present work is a further development of the calibration technique recently proposed by Ponomarenko et al. (2015, https://doi.org/10.1186/s40623-015-0310-3 ), which was based on the well‐defined propagation properties of the F layer ground scatter echoes. The main disadvantage of the original technique is that the F layer propagation conditions may vary considerably, even on a day‐to‐day basis, so its application requires visual analysis of data records. In order to address this issue, the new method utilizes the E layer ionospheric scatter whose range and altitude coverage are much more constrained. This allowed to quantify calibration criteria and to develop an automated procedure for td estimation. The improved technique has been applied to 1 year of data records from the Rankin Inlet SuperDARN radar and has demonstrated the capability for reliable td monitoring on a daily basis. Importantly, the proposed technique does not require access to the radar hardware and allows for calibrating historic data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".