A new method of assessing filtering schemes in data assimilation systems
Bibliographic record
Abstract
Abstract A new statistical diagnostic is proposed to assess the impact of externally applied filters in data assimilation systems with high‐lid atmospheric models. The diagnostic involves comparing the variances of 6 h differences of 6 h forecasts with those of a free model simulation. It was applied to the Canadian Middle Atmosphere Model Data Assimilation System (CMAM‐DAS) to choose among various filtering options. The variances of 6 h difference fields are shown to suppress long time‐scales and highlight short ones. This explains their sensitivity to a variety of filters considered and their relative insensitivity to the choice of initial conditions used for the time series. The method was used to quantify the extent to which a digital filter applied to the full state reduced the desired level of variability, as well as to determine objectively the most appropriate filter for our system from among several incremental analysis updating schemes. The method should be especially useful for models extending to the stratosphere and mesosphere, where short time‐scales represent significant contributions to the energy spectrum. Copyright © 2009 Royal Meteorological Society and Crown in the Right of Canada
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".