Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Bibliographic record
Abstract
Abstract. The Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) scheme was modified by incorporating predicted graupel density. Explicitly predicted graupel density, in turn, modifies graupel characteristics such as the fall velocity–diameter and mass–diameter relationships of graupel. The modified WDM6 has been evaluated based on a two-dimensional (2D) idealized squall line simulation and winter snowfall events that occurred during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign over the Korean Peninsula. From the 2D simulation, we confirmed that the modified WDM6 can simulate varying graupel densities, ranging from low values in an anvil cloud region to high values in the convective region at the mature stage of a squall line. Simulations with the modified WDM6 increased graupel amounts at the surface and decreased graupel aloft because of the faster sedimentation of graupel for two winter snowfall cases during the ICE-POP 2018 campaign, as simulated in the 2D idealized model. The altered graupel sedimentation in the modified WDM6 influenced the magnitude of the major microphysical processes of graupel and snow, subsequently reducing the surface snow amount and precipitation over the mountainous region. The reduced surface precipitation over the mountainous region mitigates the surface precipitation bias observed in the original WDM6, resulting in better statistical skill scores for the root mean square errors. Notably, the modified WDM6 reasonably captures the relationship between graupel density and its fall velocity, as retrieved from 2D video disdrometer measurements, thus emphasizing the necessity of including predicted graupel density to realistically represent the microphysical properties of graupel in models.
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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 | 0.004 | 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 | 0.001 | 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".