Simulation of an Orographic Precipitation Event during IMPROVE-2. Part II: Sensitivity to the Number of Moments in the Bulk Microphysics Scheme
Bibliographic record
Abstract
Abstract This is the second in a series of papers examining the behavior of the Milbrandt–Yau multimoment bulk microphysics scheme for the simulation of the 13–14 December 2001 case of orographically enhanced precipitation observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) experiment. The sensitivity to the number of predicted moments of the hydrometeor size spectra in the bulk scheme was investigated. The triple-moment control simulations presented in Part I were rerun using double- and single-moment configurations of the multimoment scheme as well the single-moment Kong–Yau scheme. Comparisons of total precipitation and in-cloud hydrometeor mass contents were made between the simulations and observations, with the focus on a 2-h quasi-steady period of heavy stratiform precipitation. The double- and triple-moment simulations were similar; both had realistic precipitation fields, though generally overpredicted in quantity, and had overprediction of snow mass and an underprediction of cloud water aloft. Switching from the triple- to single-moment configuration resulted in a simulation with a precipitation pattern shifted upwind and with a larger positive bias, but with hydrometeor mass fields that corresponded more closely to the observations. Changing the particular single-moment scheme used had a greater impact than changing the number of moments predicted in the same scheme, with the Kong–Yau simulations greatly overpredicting the total precipitation in the lee side of the mountain crest and producing too much snow aloft. Further sensitivity tests indicated that the leeside overprediction in the Kong–Yau runs was most likely due to the combination of the absence of the latent heat effect term in the diffusional growth rate for snow combined with the assumption of instantaneous snow melting in the scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".