Comparison of MM5 and Meteorological Buoy Winds from British Columbia to Northern California
Bibliographic record
Abstract
Numerical ocean modelling requires reliable marine wind fields for accurate simulation of ocean circulation. This study compares winds from the University of Washington operational Pennsylvania State/National Center for Atmospheric Research Mesoscale Model 5 (MM5) atmospheric model to winds observed at coastal meteorological buoys from British Columbia to northern California, in order to assess their suitability for use in regional ocean modelling for the ECology and Oceanography of Harmful Algal Blooms (ECOHAB) program. Two three‐month study periods from 2003 were chosen: summer (July to September), which is most important for the growth of toxic Pseudo‐nitzschia off the Washington State coast, and fall (October to December) when down‐welling favourable winds can force the onshore movement of potentially contaminated shelf water. MM5 12‐km resolution model wind speeds ranged from 81 to 101% of observed wind speeds. Mean winds were well modelled in the summer, but showed a 35° (average) clockwise direction bias in the fall compared to buoy winds. Winds were strongest in the diurnal and 2–5 day period weather bands in both seasons; spectral coherences between the model and observed winds in both frequency bands were highest (0.66–0.93) off the Washington State coast and northern Vancouver Island. In isolated near‐shore cases, model wind characteristics were significantly different from those observed due to near‐shore processes that were not accurately captured by the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".