A comparison of KNMI quality control and JPL rain flag for SeaWinds
Bibliographic record
Abstract
In the past few years, scatterometer winds have been successfully assimilated in weather analysis. A good assessment of the information content of these winds is particularly important for such activities. Besides retrieval problems in cases of a confused sea state, a particularly acute problem of Ku-band scatterometry is the sensitivity to rain. Elimination of poor-quality data is therefore a prerequisite for the successful use of the new National Aeronautics and Space Administration (NASA) scatterometer, QuikSCAT. This issue has been the topic of recent work. On the one hand, the Royal Dutch Meteorological Institute (KNMI) has developed a quality-control (QC) procedure that detects and rejects the poor-quality QuikSCAT data (including rain contamination). On the other hand, the Jet Propulsion Laboratory (JPL) has developed a "rain flag" for QuikSCAT. In this paper, we test the KNMI QC against the JPL rain flag to improve QC for QuikSCAT. Collocations with the European Centre for Medium-range Weather Forecasts (ECMWF) winds and special sensor microwave imager (SSM/I) rain data are used for validation purposes. The results show that the KNMI QC is more efficient in rejecting poor-quality data than the JPL rain flag, whereas the latter is more efficient in rejecting rain-contaminated data than the former. The JPL rain flag, however, rejects too much of the consistent wind data in dynamically active areas. The KNMI QC is a good QC procedure in the parts of the swath where the wind retrieval ability of QuikSCAT is high. In the nadir region, however, the KNMI QC efficiency and the wind retrieval skill are relatively low. In the nadir region, the KNMI QC needs additional information from the JPL rain flag to reject rain-contaminated data.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".