Use of Canadian Quick covariances in the Met Office data assimilation system
Bibliographic record
Abstract
Abstract In this paper, we describe the use of Canadian Quick (CQ) covariances in the Met Office assimilation system. These covariances have two particular advantages over other methods (such as the National Meteorological Center (NMC) method). First, they are calculated from a single long forecast run, rather than a series of short forecasts, and thus are much quicker to produce. Second, in cases where the vertical range of the assimilation system increases, they can be used immediately. Here, we compare the performance of CQ and NMC covariances in a troposphere/stratosphere configuration of the Met Office assimilation system. The forecast model used has 50 levels from the surface to 63 km. In general, the performance of the two covariances is similar. However, it is clear that a consequence of the bootstrapping approach that was used to develop the NMC covariances is noisy patterns in the error covariances which adversely affects mean errors, particularly in the winter middle and high latitudes above the 10 hPa level. In addition, the NMC covariances show evidence of gravity waves, which appear to have been generated spuriously by the 3D‐Var assimilation due to lack of dynamical balance in the 3D‐Var analyses. Such signals are absent in the trials where CQ covariances are used. The CQ method was also used to generate covariances for expanded versions of the model which spans the surface to around 80–84 km. Trial results are generally in good agreement with Earth Observing System Microwave Limb Sounder (EOS MLS) correlative measurements. Through this work, the CQ approach is proving to be a very effective tool for developing and testing new models which will be used to provide operational weather forecasts at the Met Office. The results show that the CQ approach is a quick and effective alternative to the NMC method, and can produce similar or sometimes better results. It is a particularly useful tool in the development of new assimilation systems. © Crown Copyright 2008 Reproduced with the permission of Her Majesty's St ationery Office. Published by J ohn Wiley & Sons, Ltd
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".