Simulation and Interpretation of the Phase Data Used by the Radar Refractivity Retrieval Algorithm
Bibliographic record
Abstract
Abstract The radar refractivity retrieval algorithm applied to radar phase measurements from ground targets can provide high-resolution, near-surface moisture estimates in time and space. The reliability of the retrieval depends on the quality of the returned phase measurements, which are affected by factors such as 1) the vertical variation of the refractive index along the ray path and 2) the properties of illuminated ground targets (e.g., the height and shape of the targets intercepted by radar rays over complex terrain). These factors introduce ambiguities in the phase measurement that have not yet been considered in the refractivity algorithm and that hamper its performance. A phase measurement simulator was designed to better understand the effect of these factors. The results from the simulation were compared with observed phase measurements for selected atmospheric propagation conditions estimated from low-level radio sounding profiles. Changes in the vertical gradient of refractivity coupled with the varying heights of targets are shown to have some influence on the variability of phase fields. However, they do not fully explain the noisiness of the real phase observations because other factors that are not included in the simulation, such as moving ground targets, affect the noisiness of phase measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".