The assimilation of AMSU and SSM/I brightness temperatures in clear skies at the Meteorological Service of Canada
Bibliographic record
Abstract
In recent years, the assimilation of satellite data has become a vital component of the global and regional assimilation systems at the Canadian Meteorological Centre (CMC). Moreover, the direct assimilation of satellite radiance measurements from AMSU-A, AMSU-B, and the GOES water vapor channel has resulted in notable improvements in the short and medium range CMC forecasts. This has been demonstrated in Observation System Experiments conducted by CMC. In preparation for the operational assimilation of Special Sensor Microwave Imager (SSM/I) data in the 4D-Var global analysis system at CMC, two 3D-Var experiments are conducted. In the first experiment, the assimilation of clear-sky, open-ocean brightness temperatures from the 7 SSM/I microwave channels is added to the operational configuration of the global analysis system. In the second experiment, stricter filtering of AMSU data is applied together with the addition of the SSM/I data. More specifically, AMSU-A CH3 (50.3 GHz) is removed due to its non-negligible sensitivity to clouds, and more aggressive filtering of AMSU-B CH2 (150.0 GHz), CH3 (183.3±1 H GHz), CH4 (183.3±3 H GHz), and CH5 (183.3±7 H GHz) is invoked using CH2 to identify cloudy pixels. In the current quality control procedures for AMSU-B, an effective precipitation screen is present, however, there is no method of detecting and removing cloudy observations. In both experiments, improvements are evident in the analysed integrated water vapour, surface wind speed, and daily precipitation rate fields when compared against independent observations. Furthermore, for the second experiment gains are realized in the forecasts when validated against radiosonde data. Other indicators such as anomaly correlation, RMSE, and QPF scores show a net positive effect. Overall, the second experiment shows better results than the first. In particular, the additional filtering of AMSU-B CH2-5 is identified as an important modification to the current operational configuration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".