Wind Synthesis and Quality Control of Multiple-Doppler-Derived Horizontal Wind Fields
Bibliographic record
Abstract
Horizontal wind vector fields can be measured in real time by a bistatic Doppler radar network and can be applied directly for hazard warnings and weather surveillance. Most applications, however, especially for meteorological research and operational meteorology, require quality-controlled wind fields. Therefore, a qualitycontrol scheme is developed that includes algorithms to determine the data quality. The algorithms are applied through a decision criterion, and the quality of wind measurement is weighted with values ranging from 1 to 0. The results of each weighting algorithm are merged to an average quality index field, which represents the confidence of each horizontal wind measurement. This averaged field is available together with the measured horizontal wind vector field for further applications. This idea is applicable for all kinds of spatial wind field measurements and is applied in the paper for horizontal wind fields measured for monostatic dual-and bistatic dual-and/or multiple-Doppler radar measurements. Wind synthesis and quality control of three-dimensional wind fields are presented for two frontal passages with stratiform precipitation and for a convective situation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".