Advection of Coupled Hydrometeor Quantities in Bulk Cloud Microphysics Schemes
Bibliographic record
Abstract
Abstract This paper discusses the advection of coupled hydrometeor quantities by air motion in atmospheric models. It is shown that any bulk property derived from a set of advected microphysical variables must meet certain conditions in order to be preserved during transport using linear or semilinear advection schemes when the property is initially uniform, with implications for physical consistency of the property. A new, efficient flux-based method for calculating hydrometeor advection, similar to vector transport applied previously in aerosol modeling, is also presented. In this method, called scaled flux vector transport (SFVT), lead scalars (the mass mixing ratios) are advected using the host model’s unmodified advection scheme and secondary scalars (e.g., number mixing ratios) are advected by appropriately scaling the lead scalar fluxes. By design, SFVT retains linear relationships between the advected scalars. Analytic tests reveal that mean errors using SFVT are similar to those incurred using the traditional approach of separately advecting each variable. SFVT is applied to the multimoment predicted particle properties bulk microphysics scheme in idealized two-dimensional squall-line simulations using the Weather Research and Forecasting Model. The computational cost in total wall clock run time is reduced by 10%–15% while producing solutions similar to the traditional approach. Thus, SFVT can reduce the overall cost of using multimoment bulk microphysics schemes, making them competitive with simpler schemes having fewer prognostic variables.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".