Characteristics of Extratropical Cyclones That Cause Tornadoes in Italy: A Preliminary Study
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Bibliographic record
Abstract
Characteristics of extratropical cyclones that cause tornadoes in Italy are investigated. Tornadoes between 2007 and 2016 are analyzed, and statistical analysis of the associated cyclone structures and environments is performed using the JRA-55 reanalysis. Tornadoes are distributed sporadically around the cyclone location within a window of 10° × 10°. The difference in the cyclone tracks partially explains the seasonal variability in the distribution of tornadoes. The highest number of tornadoes occur south of the cyclone centers, mainly in the warm sector, while a few are observed along the cold front. Composite mesoscale parameters are examined to identify the environmental conditions associated with tornadoes in different seasons. Potential instability is favorable to tornado development in autumn. The highest convective available potential energy (CAPE) in this season is associated with relatively high-temperature and humidity at low-levels, mainly due to the strong evaporation over the warm Mediterranean Sea. Upper-level potential vorticity (PV) anomalies and the associated cold air reduce the static stability above the cyclone center, mainly in spring and winter. On average, the values of CAPE are lower than for US tornadoes and comparable with those occurring in Japan, while storm relative helicity (SREH) is comparable with US tornadoes and higher than Japanese tornadoes, indicating that the environmental conditions for Italian tornadoes have peculiar characteristics. Overall, the conditions emerging in this study are close to the high-shear, low-CAPE environments typical of cool-season tornadoes in the Southeastern US.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it