Estimation of Near-Ground Propagation Conditions Using Radar Ground Echo Coverage
Bibliographic record
Abstract
Abstract The vertical gradient of refractivity (dN/dh) determines the path of the radar beam; namely, the larger the negative values of the refractivity gradient, the more the beam bends toward the ground. The variability of the propagation conditions significantly affects the coverage of the ground echoes and, thus, the quality of the scanning radar measurements. The information about the vertical gradient of refractivity is usually obtained from radiosonde soundings whose use, however, is limited by their coarse temporal and spatial resolution. Because radar ground echo coverage provides clues about how severe the beam bending can be, we have investigated a method that uses radar observations to infer propagation conditions with better temporal resolution than the usual soundings. Using the data collected during the International H2O Project (IHOP_2002), this simple method has shown some skill in capturing the propagation conditions similar to these estimated from soundings. However, the evaluation of the method has been challenging because of 1) the limited resolution of the conventional soundings in time and space, 2) the lack of other sources of data with which to compare the results, and 3) the ambiguity in the separation of ground from weather echoes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".