Recent Developments in Assimilation of Satellite Data in the MSC 4D-Var Analysis and Forecast System
Bibliographic record
Abstract
In May 2008, several important changes with respect to satellite data assimilation were made to the Meteorological Service of Canada (MSC) operational global 4D-Var Data Assimilation and Forecast System (DAFS). At the same time, a new version of the MSC DAFS has been developed which uses a new version of the MSC Global Environmental Multiscale (GEM) model called GEM-Strato. The GEM-Strato model incorporates several significant changes to the current version of the model, including a lid raised from 10 hPa (~30 km) to 0.1 hPa (~65 km). The new system, scheduled to replace the current system in 2009, allows for assimilation of additional radiance data from higher-peaking microwave and infrared instrument channels as well as GPS radio-occultation data up to 40 km (~3 hPa). This paper describes the recent changes in satellite data assimilation in the operational DAFS, and presents results of satellite data impact experiments with the new GEM-Strato system. In addition, the performance of the operational DAFS and the new GEM-Strato system are compared. Finally, future plans for satellite data assimilation are presented, which include assimilation of data from the
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".