The role of Mediterranean cyclone structure in modulating convective activity
Bibliographic record
Abstract
Deep convection in the Mediterranean is favoured by warm sea surface temperatures and complex topography, but its occurrence is further modulated by synoptic-scale systems such as Mediterranean cyclones (MEDCs). In this study we investigate how MEDCs influence the frequency and intensity of convective environments and associated hazards. The analysis combines ERA5 reanalysis data, modelled hail and lightning probabilities, and lightning detections from the ATDNet network. A recent classification of MEDCs into nine clusters based on upper-level dynamical structure (Givon et al., 2024) provides a framework for linking cyclone types to convective activity.For each MEDC cluster, we examine the evolution of convective environments, highlighting key differences in their spatial distribution and timing relative to the cyclone centre. In general, convective activity is most frequent northeast of the cyclone centre and within the warm sector, typically peaking before the time when the minimum central pressure is reached. Among the clusters, small and deep cyclones in the northern Mediterranean during autumn show the highest potential for severe convection, followed by weaker systems occurring in the southern Mediterranean during autumn, spring and summer.We further identify mesoscale features within MEDCs and show that regions of warm conveyor belt ascent are more strongly linked to deep convection than cold frontal zones. This pattern is consistent across all cyclone types. Our findings advance the understanding of convective processes associated with MEDCs and offer valuable insights for improving weather forecasting and risk communication in the Mediterranean region.Givon, Y., Hess, O., Flaounas, E., Catto, J. L., Sprenger, M., and Raveh-Rubin, S.: Process-based classification of Mediterranean cyclones using potential vorticity, Weather Clim. Dynam., 5, 133–162, https://doi.org/10.5194/wcd-5-133-2024, 2024.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".