Rapid-Scan, Polarimetric Observations of Central Oklahoma Severe Storms on 31 May 2013
Bibliographic record
Abstract
ABSTRACT On 31 May 2013, a polarimetric WSR-88D located in Norman, Oklahoma (KOUN), was used to collect sectorized volumetric observations in a tornadic supercell. Because only a fraction of the full azimuthal volume was observed, rapid volume update times of ~1–2 min were achieved. In addition, the number of pulses used in each radial was larger than is conventional, increasing the statistical robustness of the calculated polarimetric variables. These rapid observations serve as a proxy for those of a future dual-polarized phased-array radar. Through comparison with contemporaneous observations from two nearby dual-polarized WSR-88Ds [Twin Lakes, Oklahoma (KTLX), and near University of Oklahoma Westheimer Airport in Norman (KCRI)], a number of instances in which the rapidly scanned KOUN radar detected or better resolved (in a temporal sense) features of severe convective storms are highlighted. In particular, the polarimetric signatures of merging updrafts, a rapidly descending giant hail core, an anticyclonic tornado, and a dissipating storm cell are examined. These observations provided insights into the rapid evolution of severe convective storms that could not be made (or would have been made with much lower confidence) with current, operational WSR-88D scanning strategies. Possible implications of these rapid updates for the warning decision process are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".