Simulations of Selective Seeding of Hailstorms—A Summertime Case Study over Switzerland
Bibliographic record
Abstract
Abstract Hailstorms can cause a lot of damage to agriculture and property. Therefore, efforts exist to mitigate hail damage by means of seeding a developing hailstorm with ice nucleating particles. Motivated by the Swiss hail mitigation campaign, we examined the impact of silver iodide (AgI) perturbations on a convective storm observed over northern Switzerland on 6 July 2019. We evaluated the effectiveness of an early seeding strategy and investigated the concept of beneficial competition, where an increased number of ice nucleating particles (INPs) leads to the formation of smaller, less damaging hailstones. We used the Consortium for Small-Scale Modeling (COSMO) Regional Weather and Climate Model to simulate this case. AgI particles were added as a prognostic variable to the hailstorm during its cumulus stage and were released in the updraft region near the cloud base with concentrations ranging from 0.2 to 2000 cm −3 in ensemble simulations. While seeding delayed the onset of precipitation, increased the graupel concentration, and reduced supercooled liquid water, especially in the upper part of the convective cloud, no systematic change in the overall hail size has been found. We did, however, observe fewer grid points with mean hail diameters larger than 30 mm in all seeding concentrations across all altitudes, corresponding to a decrease of the largest hail sizes (>30 mm) across all atmospheric levels by up to 11%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".