Evaluation of an operational ice–ocean analysis and forecasting system for the Gulf of St Lawrence
Bibliographic record
Abstract
Abstract Our ability to simulate and understand oceanic conditions of the Gulf of St Lawrence (GSL) has significantly increased in the last decade with the development of regional ocean models combining scientific knowledge, oceanographic data, and computing techniques. This has created opportunities for their integration with meteorological forecast models where the importance of sea ice–ocean dynamics in air–sea exchanges was recently demonstrated. In order to produce accurate initial conditions for coupled weather forecasts, a GSL ice–ocean ‘pseudo’ analysis (IOPA) system has been developed and implemented operationally at the Canadian Meteorological Centre (CMC). Based on prognostic ice–ocean model solutions the IOPA system has sufficient skill to produce realistic daily fields without flux correction or data assimilation of temperature and salinity. Using only a simple assimilation method for sea‐ice data, daily oceanic fields are obtained and can be used to initialize ice–ocean or coupled atmospheric forecasting systems. A 3‐year retrospective evaluation of this operational method is presented focusing on sea‐surface temperature (SST) and sea‐ice conditions. Model estimates are compared with in situ and satellite‐derived data. Results show that the IOPA system has an accuracy equivalent to or better than the current operational SST and ice analyses produced at the CMC. Together with preceding modelling efforts, this work lays the foundation for the first state‐of‐the‐art GSL ocean analysis and forecast systems combining ocean modelling and real‐time three‐dimensional data assimilation. Coupled atmosphere–ice–ocean forecasts produced with the same ice–ocean model and initialized with the IOPA system are presented in a companion paper. Copyright © 2012 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".