Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the Environment Canada Regional Ice Prediction System
Bibliographic record
Abstract
Abstract This study describes the impact from three major modifications to an existing ice‐analysis system developed at Environment Canada. The analysis component of the Regional Ice Prediction System currently provides near real‐time gridded estimates of ice concentration for all ice‐affected areas around North America and Greenland, and is primarily aimed to satisfy the operational requirements of the Canadian Ice Service. The first modification is the assimilation of Special Sensor Microwave Imager/Sounder data from three satellite platforms to complement the already assimilated Special Sensor Microwave Imager data from one platform. The second change is the assimilation of ice‐concentration information derived from Advanced Scatterometer data. The third modification is to replace the ice concentration in the analysis with spatially interpolated values for all grid points where an estimated measure of uncertainty is above a specified threshold. Objective verification scores were computed from 1 year experiments spanning all of 2010 using independent verification data to evaluate the accuracy of the analyses. The incremental impact of adding each of the three modifications is examined along with the combined impact from the three modifications. It is demonstrated that the new version of the system produces consistently more accurate ice‐concentration analyses than the previous version, especially during the summer period and when the ice is refreezing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".