Assessments of multiple metocean forecasts in the North Atlantic Ocean
Why this work is in the frame
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Bibliographic record
Abstract
Accurate wind and wave forecasts are crucial for a variety of marine operations, including ship design, navigation safety, and engineering. This study evaluates the predictability of wind and wave conditions in the North Atlantic Ocean using both deterministic and ensemble forecast models. A dataset of 29 deep water buoys was used to validate deterministic and ensemble forecasts from the National Centres for Environmental Prediction (NCEP), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the German Weather Service (DWD). A comparison of deterministic and ensemble forecast performance was conducted using eight statistical metrics focusing on the significant wave heights associated with 10-m wind speed. The results indicate that ensemble models exhibit higher correlation coefficients and lower scatter root mean square error especially beyond the 7th forecast day, outperforming deterministic models. The findings suggest that ensemble forecasts offer a more reliable estimation of forecast, enhancing predictability for marine operations. • Buoy data in deep waters is used to validate deterministic and ensemble forecasts. • A comparison between the performance of deterministic forecast and ensemble forecast is conducted. • The errors of significant wave heights associated with 10-m wind speed are analysed and discussed. • The results indicate that ensemble models exhibit higher correlation coefficients and lower scatter root mean square error. • Ensemble forecasts offer a more reliable estimation of forecast, enhancing predictability for marine operations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it