Development of a coupled blowing snow-atmospheric model and its applications
Why this work is in the frame
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Bibliographic record
Abstract
Blowing snow can occur over snow-covered surface in association with strong winds.Snow particles are lifted into the boundary layer where they are subject to sublimation and horizontal transport over long distances.The snow transport and in-transit sublimation processes affect the moisture and the snow mass budgets.The cooling effect of sublimation also affects the temperature in the boundary layer and thus may play a role in the dynamics of both the boundary layer and the larger scale synoptic flow.In this thesis, a coupled blowing snow-atmospheric model is developed to study the effects of blowing snow on the winter season Northern Hemisphere snow mass budget and anticyclogenesis.We first extended a one-dimensional double-moment blowing snow model (PIEKTUK-D) to a triple-moment version (PIEKTUK-T).The procedure is to formulate predictive equations for three moments of an assumed Gamma particle size distribution for blowing snow.The three moments are the total number concentration, the total mass mixing ratio, and the total radar reflectivity.The results of idealized experiments and real case simulations indicated that PIEKTUK-T predicts well the number concentration, mixing ratio, the shape parameter, and visibility in blowing snow.The model also simulated a power law relationship between the radar reflectivity factor and the particle extinction coefficient consistent with observations in snow storms.However, PIEKTUK-T cannot treat horizontal advection, lateral entrainment, and the interaction between blowing snow and the atmospheric boundary layer.To allow for the consideration of these effects, we next coupled PIEKTUK-T to an atmospheric mesoscale model (MC2).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it