Stratospheric Influence on the Development of the 2018 Late Winter European Cold Air Outbreak
Bibliographic record
Abstract
Abstract Using ensemble forecasts from the Global Ensemble Forecast System (GEFS) and controlled integrations with the Weather Research and Forecasting (WRF) model, we explore the stratospheric influence on the development of the 2018 European severe cold air outbreak (CAO). The 2018 CAO set in on 22 February, and its evolution was closely related to a growing tropospheric blocking ridge over the North Atlantic in late February that brought cold air mass into Europe. The event was also preceded by a split of the stratospheric polar vortex on 12 February, after which one child vortex persisted over northeastern Canada. GEFS forecasts initialized 1 week prior to this CAO substantially underestimated its severity, exhibiting large spread correlated with the strength and location of the Atlantic ridge, while forecasts initialized closer to the event captured its severity with higher fidelity. Based on a set of nudged simulations, we show that capturing the evolution of the stratospheric child vortex over northeastern Canada, particularly in the lowermost stratosphere, is of great importance for predicting this CAO, since it substantially affects the strength and location of the Atlantic ridge that in turn determines the evolution and severity of this CAO. When the stratosphere is nudged toward the observed evolution, the northward flow between the child vortex and the Atlantic ridge advects more low potential vorticity air into the Atlantic ridge, and therefore amplifies it. Further experiments demonstrate that the impact of the stratosphere on the development of the ridge is particularly strong over the North Atlantic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".