Towards ensemble data assimilation for the Environment Canada Regional Ice Prediction System
Bibliographic record
Abstract
A short‐range high‐resolution sea ice prediction system has been developed at Environment Canada. This study describes the first steps towards transitioning this system from a simple deterministic data assimilation system based on the three‐dimensional variational (3D‐Var) approach into a data assimilation system based on an ensemble of ensemble‐variational (EnVar) analyses. First, an ensemble of 3D‐Var analyses using static background‐error covariances is implemented and used to evaluate different strategies for simulating model uncertainties during the ensemble forecast step; these range from perturbing parameters within the sea ice model to completely disabling the sea ice dynamics or thermodynamics in some of the ensemble members. The experiments show a good ensemble spread–error relationship in areas with low or high ice concentration, though more work is needed to better simulate uncertainties in areas with intermediate ice concentration. Second, results from idealized experiments with EnVar analyses using ensemble covariances are presented. They demonstrate the potential improvement of sea ice analyses from using state‐dependent multivariate ensemble covariances when assimilating ice concentration observations to correct both ice concentration and unobserved variables such as ice thickness and ocean temperature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".