Sensitivity of snowfall forecast over North China to ice crystal deposition/sublimation parameterizations in the <scp>WSM6</scp> cloud microphysics scheme
Bibliographic record
Abstract
Abstract Ice‐phase cloud microphysical processes are very complicated, and there are still many uncertainties in current microphysics parameterization schemes. In this study, two alternative ice crystal deposition/sublimation (ICDS) parameterizations, following the Harrington et al. , Journal of the Atmospheric Sciences, 1995, 52, 4344–4366 and Koenig, Monthly Weather Review, 1972, 100, 417–423 methods, were implemented into the Weather Research and Forecast (WRF) Single‐Moment 6‐class Microphysics (WSM6) scheme in the Global/Regional Assimilation and Prediction System (GRAPES) regional operational model to investigate their impact on overestimation of snowfall over North China. The results show that the snowfall amount and cloud particle composition are very sensitive to the ICDS parameterization. Sensitivity tests with WSM6 using the Harrington and Koenig ICDSs, referred to as WSM6_H and WSM6_K, respectively, notably reduced overestimation of the snowfall amount and simulated more realistic supercooled cloud water content compared with ERA5 reanalysis data. The vertical distributions, intensities, and duration of radar echoes simulated by WSM6_H are the most consistent with millimeter‐wavelength cloud radar observations. There is competition for water vapor in WSM6 due to the sequential order in which processes are updated. This tends to weaken the deposition of snow and graupel and the condensation of cloud water when the deposition of ice crystals is too strong in a cold region. In both WSM6_H and WSM6_K, the deposition processes of ice crystals are reduced and the other microphysical transformation processes become more active compared with WSM6. Overall, the WSM6_H configuration improves the forecast performance as evaluated by Taylor diagrams for eight snowfall days over North China. The ICDS parameterizations must therefore be handled carefully due to their large uncertainties in the development of the cloud microphysics schemes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".