Assimilation of Infrared Radiance Observations with Sensitivity to Land Surfaces in the Canadian Ensemble–Variational System
Bibliographic record
Abstract
Abstract This study highlights recent progress at the Canadian Meteorological Centre in the assimilation of Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) radiance observations that are sensitive to land surfaces. The assimilation is carried out using the Canadian global ensemble–variational system. That system benefits from flow-dependent background-error statistics that include covariances between surface skin temperature and atmospheric variables. Up to 142 channels from both AIRS and IASI are assimilated. A detailed database of spectral surface emissivity is used. Forecasts are evaluated against ERA-Interim analyses, own-cycle analyses, and radiosonde observations. A conservative approach is taken, with restrictive conditions on topography and surface emissivity. From 2-month assimilation experiments, a significant positive impact is obtained in the Northern Hemisphere extratropics beyond day 2 from the surface to the upper troposphere. The impact is mixed in other regions, depending on level or forecast lead time. Causes for these mixed results are examined in view of future experiments and eventual operational implementation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".