A First Attempt of Data Assimilation for Operational Sea Ice Monitoring in Canada
Bibliographic record
Abstract
A three-dimensional variational data assimilation (3D-Var) system is developed as a first attempt to explore the potential use of data assimilation to improve a coupled ice-ocean model (CIOM) forecast of sea ice near the east coast of Canada. The accuracy of the resulting analysis is largely dependent upon the forecast-error covariance matrix. This study focuses on the estimation of forecast-error statistics required in a 3D-Var system and their effect on the ocean part of the CIOM. This is accomplished by comparing CIOM output according to different specifications of forecast-error statistics used during the data assimilation. The results show no improvement in the ice forecast with respect to persistence. It has been concluded that the assimilation system still needs significant improvement including assimilation of many different types of observations such as sea surface temperature, ice drift, ocean current, etc. An improved forecast-error covariance matrix is needed for a more accurate description of the system's behaviour.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".