Recent trends and climatic perspectives of hailstorms frequency and intensity in Tuscany and Central Italy
Bibliographic record
Abstract
Abstract. The damages from climatic extremes have dramatically increased in the last decades in Europe, as likely outcomes of climate change: floods, droughts, heat waves and hailstorms have brought local as well as widespread damages to farmers, industry, infrastructures and society, to insurance and reinsurance companies; in this work we deal with the hailstorm hazard. The NCEP-NCAR Reanalysis (2.5 by 2.5° lat-lon) over the Italian area and the hailstorm reports at several sites are used to identify few forcings for hailstorms; statistical relationships linking forcings and hailstorm frequencies are derived. Such relationships are applied to the same forcings derived from the CGCM2-A2 climate scenario provided by the Canadian Centre for Climate modeling and analysis (CCCma; resolution approximately 3.75 by 3.75° lat-lon), to evaluate the expected changes of the frequency of hailstorms. The time series of the forcings from the NCEP-NCAR Reanalysis and the CCCma climate scenario in the past decades are compared in order to assess the reliability and accuracy of the predictions of the future hailstorm hazard. It is shown that the climate scenario provides a fairly faithful representation of the past trends of the forcings relevant to the hailstorms frequency and that such quantity, hence the hailstorm hazard, is growing and will likely grow in the future over the limited area taken into consideration in this study.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".