Evaluation of new estimates of background‐ and observation‐error covariances for variational assimilation
Bibliographic record
Abstract
Abstract In this study new estimates for the observation‐ and background‐error covariances in a three‐dimensional variational analysis system at the Canadian Meteorological Centre are evaluated. In the current system, the observation errors are assumed uncorrelated with the variances estimated following a more or less ad hoc approach for each instrument type. The background‐error covariances are computed using the so‐called NMC method. The new estimate for the observation error variances is obtained using a practical approach developed at Météo‐France that computes the maximum likelihood estimate of the variances. Two new estimates for the stationary background‐error covariances are evaluated. The first simply involves tuning the variances in the operational background‐error covariances. For the second, the NMC method is replaced by a Monte Carlo approach applied to the existing analysis system. Both the variances and spatial covariances of the innovations are computed and compared with the corresponding quantities derived from each set of error covariances used by the analysis system. This comparison shows a significantly improved consistency for radiosonde data when using the new error covariances, especially with the Monte Carlo approach. In contrast, the interchannel innovation covariances for ATOVS AMSU‐A observations are more consistent when using background‐error covariances obtained with the NMC method. In addition, modest forecast improvements are obtained by using the new observation‐ and background‐error covariance estimates, most notably for the tropics. © Crown copyright, 2005
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".