Exploring the convective grey zone with regional simulations of a cold air outbreak
Bibliographic record
Abstract
Cold air outbreaks can bring snow to populated areas and can affect aviation safety. Shortcomings in the representation of these phenomena in global and regional models are thought to be associated with large systematic cloud‐related radiative flux errors across many models. In this study, nine regional models have been used to simulate a cold air outbreak case at a range of grid spacings (1–16 km) with convection represented explicitly or by a parametrization. Overall, there is more spread between model results for the simulations in which convection is parametrized when compared to simulations in which convection is represented explicitly. The quality of the simulations of both the stratocumulus and the convective regions of the domain are assessed with observational comparisons 24 h into the simulation. The stratocumulus region is not well reproduced by the models, which tend to predict open cell convection with increasing resolution rather than stratocumulus. For the convective region the model spread reduces with increased resolution and there is some improvement in comparison to observations. Comparing models that have the same physical parametrizations or dynamical core suggest that both are important for accurately reproducing this case.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".