Numerical studies on winter precipitation type formation
Bibliographic record
Abstract
Winter storms produce major problems for society and their varying types of precipitation are often the key factor responsible. The objective of this study is to better understand the formation of winter precipitation types (freezing rain, ice pellets, snow, slush, wet snow and refrozen wet snow) within the varying, and interacting, environmental conditions experienced within many winter storms. A one dimensional cloud model utilizing a double-moment microphysics scheme has been developed to address this issue. Temperature and moisture profiles favorable for the formation of different winter precipitation types were varied in a systematic manner in an environment in which snow is falling continuously from above a temperature inversion. The ensuing precipitation evolved due to atmospheric temperature and moisture variations arising from phase changes such as melting and freezing. Average soundings associated with various surface precipitation types were consequently defined as well as the atmospheric conditions leading to the longest duration of various precipitation events. In addition, the reduction of visibility by precipitation was calculated to determine the conditions leading to the greatest reduction and those leading to its most rapid changes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".