Retrieval of vertical air motion in stratiform snow: «a case study»
Bibliographic record
Abstract
This thesis is an attempt to estimate the vertical air motion in stratiform snow region of a case study. The motivation is to search for a better understanding of the interesting precipitation and vertical velocity structure observed for this case by the X-band vertically-pointing radar of McGill University, where particles are decelerating in their fall at several height levels while the reflectivity field suggests an increase of snow mass. Three methods of retrieval of vertical air motion are explored: the adiabatic method that assumes conservation of potential energy and gives an estimation of the synoptic scale air motion; subtraction of fall speed of snow from Doppler vertical velocity measurements, the fall speed estimated from its relationship with two moments of the particle size distribution: radar reflectivity and Doppler velocity; and finally the kinematic method that gives the vertical wind by integration of horizontal divergence assuming air mass continuity. Each method of retrieval brought new elements to the analysis of vertical wind field, in space and in time, and all seem to agree on the observed fluctuations in vertical velocities to be mainly caused by air motion. Results of vertical wind at the large scale, obtained when necessary from retrieved fields by taking the time or spatial average, seem consistent with each other. They could be used as additional information derived from observations in data assimilation analysis systems, and provide a better initial guess of the atmospheric state to numerical models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".