REFRACTT 2006
Bibliographic record
Abstract
The Refractivity Experiment for H2O Research and Collaborative Operational Technology Transfer (REFRACTT), conducted in northeast Colorado during the summer of 2006, provided a unique opportunity to obtain high-resolution gridded moisture fields from the operational Denver Next Generation Weather Radar (NEXRAD) and three research radars using a radar-based index of refraction (refractivity) technique. Until now, it has not been possible to observe and monitor moisture variability in the near-surface boundary layer to such high spatial (4-km horizontal gridpoint spacing) and temporal (4–10-min update rates) resolutions using operational NEXRAD and provide these moisture fields to researchers and the National Weather Service (NWS) forecasters in real time. The overarching goals of REFRACTT were to 1) access and mosaic the refractivity data from the operational NEXRAD and research radars together over a large domain for use by NWS forecasters in real time for short-term forecasting, 2) improve our understanding of near-surface water vapor variability and the role it plays in the initiation of convection and thunderstorms, and 3) improve the accuracy of quantitative precipitation forecasts (QPF) through improved observations and assimilation of low-level moisture fields. This paper presents examples of refractivity-derived moisture fields from REFRACTT in 2006 and the moisture variability observed in the near-surface boundary layer, in association with thunderstorm initiation, and with a cold frontal passage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.046 | 0.023 |
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".