Comparisons of Single- and Double-Moment Microphysics Schemes in the Simulation of a Synoptic-Scale Snowfall Event
Bibliographic record
Abstract
Abstract The Canadian CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Validation Project (C3VP) provided aircraft, surface, and remotely sensed observations of cloud and precipitation characteristics to support improved simulation of cold-season precipitation within weather forecast models and new developments in satellite and radar precipitation retrievals. On 22 January 2007, the C3VP campaign executed an intensive observation period to sample widespread snowfall that occurred as a midlatitude cyclone tracked along the U.S.–Canadian border. Surface air temperature and precipitation measurements, combined with aircraft measurement of hydrometeor content and size distribution, are used to examine various assumptions and parameterizations included within four bulk water microphysics schemes available within the Weather Research and Forecasting Model (WRF). In a simulation of the 22 January 2007 event, WRF forecasts reproduced the overall precipitation pattern observed during aircraft sampling, allowing for a comparison between C3VP measurements and microphysics scheme assumptions. Single-moment schemes that provide flexibility in size distribution parameters as functions of temperature can represent much of the vertical variability observed in aircraft data, but variability is reduced in an environment where the simulated temperature profile is nearly isothermal. Double-moment prediction of total number concentration may improve the representation of ice crystal aggregation. Inclusion of both temperature dependence on distribution parameters and variability in mass–diameter or diameter–fall speed relationships suggest a means of improving upon single-moment predictions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".