The Regional Ice Prediction System (RIPS): verification of forecast sea ice concentration
Bibliographic record
Abstract
In recent years, the demand for improved environmental forecasts in the Arctic has intensified as maritime transport and offshore exploration increase. As a result, Canada has accepted responsibility for the preparation and issuing services for the new Arctic MET/NAV Areas XVII and XVIII. Environmental forecasts are being developed based on a new integrated Arctic marine prediction system. Here, we present the first phase of this initiative, a short‐term pan‐Arctic 1/12° resolution Regional Ice Prediction System (RIPS). RIPS is currently set to perform four 48 h forecasts per day. The RIPS forecast model (CICE 4.0) is forced by atmospheric forecasts from the Environment Canada regional deterministic prediction system. It is initialized with a 3D‐Var analysis of sea ice concentration and the ice velocity field and thickness distribution from the previous forecast. The other forcing (surface current) and initialization fields (mixed‐layer depth, sea surface temperature and salinity) come from the 1/4° resolution Global Ice Ocean Prediction System. Three verification methods for sea ice concentration are presented. Overall, verifications over a complete seasonal cycle (2011) against the Ice Mapping System ice extent product show that RIPS 48 h forecasts are better than persistence during the growth season while they have a lower skill than persistence during the melt period. A better representation of landfast ice, oceanic processes (wave–ice interactions, upwelling events, etc.) in the marginal ice zone and better initializing fields should lead to improved forecasts.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".