Improving Radar Refractivity Retrieval by Considering the Change in the Refractivity Profile and the Varying Altitudes of Ground Targets
Bibliographic record
Abstract
Abstract Accurate radar refractivity retrievals are critical for quantitative applications, such as assimilating refractivity into numerical models or studying boundary layer and convection processes. However, the technique as originally developed makes some simplistic assumptions about the heights of ground targets ( ) and the vertical gradient of refractivity ( ). In reality, the field of target phases used for refractivity retrieval is noisy because of varying terrain and introduces estimation biases. To obtain a refractivity map at a constant height above terrain, a 2D horizontal refractivity field at the radar height must be computed and corrected for altitude using an average . This is achieved by theoretically clarifying the interpretation of the measured phase considering the varying and the temporal change of . Evolving causes systematic refractivity biases, as it affects the beam trajectory, the associated target range, and the refractivity field sampled between selected targets of different heights. To determine and changes, a twofold approach is proposed: first, can be reasonably inferred based on terrain height; then, a new method of estimation is devised by using the property of the returned powers of a pointlike target at successive antenna elevations. The obtained shows skill based on in situ tower observation. As a result, the data quality of the retrieved refractivity may be improved with the newly added information of and .
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".