Effects of atmospheric conditions on the formation of winter precipitation types
Bibliographic record
Abstract
Winter storms produce major problems for society and their precipitation is often the key factor responsible. The objective of this thesis is to better understand the formation of this precipitation. A multi-moment bulk microphysics scheme coupled with a cloud model has been developed to address this issue. It predicts the mass mixing ratio and total number concentration for many hydrometeor categories including rain, snow, freezing rain, wet snow, slush and ice pellets. Semi-melted particles have been incorporated into the bulk scheme since they are commonly formed at temperatures near 0ºC and they influence the formation of other types of precipitation within the atmosphere and reaching the surface. Considering a vertical profile in the atmosphere, the precipitation type characteristics during the 1998 Ice Storm in the Montreal area have been compared with aircraft measurements. Many of the observed characteristics were reproduced by the model. Also, sensitivity tests on the precipitation types formed during the Ice Storm were performed. The results show that small variations (±0.5ºC) in the temperature profiles as well as the precipitation rate can have major impacts on the types of precipitation formed at the surface in such a catastrophic event. Using a two-dimensional cloud model, the effects of the background wind on the precipitation type evolution within the atmosphere and at the surface have likewise been investigated. These results were compared with observations taken during a field project held at the Centre for Atmospheric Research Experiments (CARE) in the Toronto area during the winter 2006-2007. The results reproduced many of the precipitation types observed. The background wind field and snowfield aloft influence the type and the amount of precipitation reaching the surface. Overall, the environmental factors such as the temperature, the degree of saturation and the background wind critically affect the type of winter precipitation formed.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".