A case study of blowing snow cooling effects on anticyclogenesis and cyclolysis
Bibliographic record
Abstract
[1] This paper focuses on blowing snow and its effect, through thermodynamic forcing, on anticyclogenesis and cyclolysis. A triple-moment blowing snow model (PIEKTUK-T) is coupled to an atmospheric model (MC2), and this system is used to simulate an anticyclogenesis event. For comparison, an uncoupled version of MC2 is used to model the same event. The coupled model (CPL) showed colder low-level temperatures in regions where blowing snow occurred. This cooling contributes to a rise in sea level pressure relative to the uncoupled simulation. A potential vorticity (PV) diagnostic is then applied to quantify how this microphysical cooling affects the geopotential height and balanced wind fields. Surface potential temperature differences between the coupled and uncoupled runs were used as lower boundary conditions for the inversion. The results showed that blowing snow has only a small cooling effect over the anticyclogenesis region in CPL and moderate cooling over Baffin Island, where a decaying cyclone was moving northward. The cooling induces positive geopotential height and anticyclonic flow perturbations extending up to 500 mbar over the cyclone region. The averaged inverted geopotential height anomaly at 1000 mbar level over the cooling region is up to 4.6 dam in 72 h. Surface cooling is demonstrated to play a role in the cyclolysis. The CPL run allows the relative humidity with respect to ice in the blowing snow module to remain supersaturated and includes the heat release from the supersaturated water vapor deposition. Another experiment was carried out, in which supersaturated vapor was not allowed in the blowing snow module. The sensitivity experiment results indicated that blowing snow cooling effects over Baffin Island will be much reduced.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".