Dynamical Consistency of Reanalysis Datasets in the Extratropical Stratosphere
Bibliographic record
Abstract
Abstract Reanalysis data provide a good estimate of global atmospheric temperature and wind fields. However, the available reanalysis datasets reveal nonnegligible discrepancies in their mean state and temporal variability. In this study, the quality of eight reanalysis datasets is evaluated by examining their dynamical consistency in the extratropical stratosphere. The dynamical consistency is quantified by computing the residual of the zonal-mean momentum equation. The residual is generally small in the lower stratosphere, especially at and below 30 hPa, but increases significantly aloft in both hemispheres poleward of 45°, where the effect of parameterized gravity wave drag becomes important. However, at most levels, a large difference in the residual is found among the datasets. This interdata difference is mainly caused by an uncertainty in the Coriolis torque. The non-quasigeostrophic terms, such as those associated with the vertical motion, also play a nonnegligible role when the polar vortex accelerates or decelerates. The latest reanalysis datasets exhibit smaller residuals than their earlier counterparts. For example, ERA-Interim is dynamically more consistent than ERA-40. This improvement over the generations is largely attributed to a better representation of the Coriolis torque. This is not likely achieved by the increase in satellite data observations over the past few decades. In fact, the dynamical consistency is only weakly sensitive to the analysis period. Instead, model-specific factors, such as data assimilation technique, model resolution, and physics, likely play a crucial role in improving the dynamical consistency.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".