A new high-resolution Coastal Ice-Ocean Prediction System for the East Coast of Canada
Bibliographic record
Abstract
Abstract. This paper describes the Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) running operationally at Environment and Climate Change Canada (ECCC). CIOPS-E uses a one-way downscaling technique on a 1/36° horizontal grid (~2 km) to simulate high-resolution ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). CIOPS-E is forced at its lateral boundaries with ECCC’s Regional Ice-Ocean Prediction System (RIOPS) and tidal conditions from the Oregon State University TPXO model. The three-dimensional temperature and salinity fields are spectrally nudged towards the RIOPS solution offshore of the 1500 m isobath to, effectively constrain mesoscale features in the Gulf Stream area. Over the continental shelf and the GSL, the CIOPS-E solution is free to develop fully according to model dynamics. CIOPS-E is evaluated over one year from March 2019 to February 2020. Overall, the CIOPS-E improves the representation of tides compared to ECCC’s lower resolution systems: RIOPS (1/12°) and the Regional Marine Prediction System – Gulf of St. Lawrence (RMPS-GSL, 5 km). The accuracy of the tides are comparable to the TPXO at most coastal tide gauges. Sub-tidal water levels from CIOPS-E agree well with the observed seasonal variability and show improved errors statistics at all stations compared to RIOPS and RMPS-GSL. Improvements are especially noted for the GSL. Sea surface temperatures (SSTs) from CIOPS-E are lower (higher) in spring (fall) over most of the GSL compared to satellite-derived analyses and RIOPS. Comparison with in-situ observations of SST show significant improvement in CIOPS-E with respect to the RMPS-GSL. Lastly, sea ice conditions in the GSL are compared with the Canadian Ice Service (CIS) charts and the RMPS-GSL model. The sea ice cover and thickness from the pseudo-analysis component (without data assimilation) shows an overestimation compared to the CIS estimates, which is subsequently corrected in the forecast phase through the direct insertion of a Radarsat image analysis product.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".