Comment on acp-2022-429
Bibliographic record
Abstract
Intense dual-polarization Doppler signatures in conjunction with strong vertical wind shear were observed by an X-band weather radar during a winter high precipitation event over the Swiss Alps. An enhancement of differential phase shift (Kdp > 1° km−1) around −15 °C suggested that a large population of oblate ice particles was present in the atmosphere. Here, we will show that ice-graupel collisions are a likely origin of this population. We perform sensitivity simulations that include ice-graupel collisions of a cold frontal passage to investigate whether these simulations can capture the event better and whether the vertical wind-shear had an impact on the secondary ice production (SIP) rate. The simulations are conducted with the Consortium for Small scale Modeling (COSMO), at a 1 km horizontal grid spacing in the Davos region in Switzerland. The rime-splintering simulations could not reproduce the high ice number concentrations, produced too large ice particles and therefore overestimated the radar reflectivity. The collisional-breakup simulations reproduced both the measured horizontal reflectivity and the ground-based observations of hydrometeor number concentration more accurately (∼ 20 L−1). During 14:30–15:45 UTC the vertical wind shear strengthened by 60 % within the region favorable for SIP. Calculation of the mutual information between the SIP rate and vertical wind shear and updraft velocity suggested that the SIP rate is best predicted by the vertical wind shear rather than the updraft velocity. The ice-graupel simulations were insensitive to the conversion rate size restriction from ice to graupel and snow to graupel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.033 | 0.016 |
| Insufficient payload (model declined to judge) | 0.110 | 0.114 |
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".