Modeling the Phase Transition Associated with Melting Snow in a 1D Kinematic Framework: Sensitivity to the Microphysics
Bibliographic record
Abstract
A simple 1D kinematic cloud model coupled to a two-moment bulk microphysics scheme is used to perform quasi-idealized simulations of snow, with a prescribed upper boundary snow field based on observed radar reflectivity and temperature, falling into a low-level melting layer. The model realistically simulates the formation of a nearly isothermal layer below the melting level, the surface precipitation rate, and the phase transition from liquid to solid, consistent with observations for this case. A series of test runs is performed to examine the sensitivity of modeling the timing and duration of the phase transition period to details of specific parameterization aspects related to snow in the microphysics scheme. The sensitivity tests include varying the number of prognostic moments, the mass–diameter relation, the fall velocity–diameter relation, the treatment of aggregation, and the lower limit for the slope of the size distribution. It is shown that the simulated transition period, for such a case with the initial melting level being close to the surface, can be quite sensitive to model parameters specified within realistic ranges and/or ranges within our physical understanding.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".