The Canadian Regional Data Assimilation and Forecasting System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper describes the recent changes to the regional data assimilation and forecasting system at the Canadian Meteorological Center. A major aspect is the replacement of the currently operational global variable resolution forecasting approach by a limited-area nested approach. In addition, the variational analysis code has been upgraded to allow limited-area three- and four-dimensional variational data assimilation (3D- and 4DVAR) analysis approaches. As a first implementation step, the constraints were to impose similar background error correlation modeling assumptions, equal computer resources, and the use of the same assimilated data. Both bi-Fourier and spherical-harmonics spectral representations of background error correlations were extensively tested for the large horizontal domain considered for the Canadian regional system. Under such conditions, it is shown that the new regional data assimilation and forecasting system performs as well as the current operational system and it produces slightly better 24-h accumulated precipitation scores as judged from an ensemble of winter and summer cases. Because of the large horizontal extent of the regional domain considered, a spherical-harmonics spectral representation of background error correlations was shown to perform better than the bi-Fourier representation, considering all evaluation scores examined in this study. The latter is more suitable for smaller domains and will be kept for the upcoming use in the kilometric-scale local analysis domains in order to support the Canadian Meteorological Center’s (CMC’s) operations using multiple domains over Canada. The CMC’s new regional system [i.e., a regional limited-area 3DVAR data assimilation system coupled to a limited-area model (REG-LAM3D)] is now undergoing its final evaluations before operational transfer. Important model and data assimilation upgrades are currently under development to fully exploit this new system and are briefly presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it