Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> This study examines the impact of inclusion of secondary ice production (SIP) parameterizations on the cloud microphysics and dynamics in numerical weather prediction (NWP) simulations under mid-latitude winter conditions. Hindcast mesoscale model simulations were performed for two flights from the 2019 In-Cloud ICing and Large-drop Experiment (ICICLE) field campaign. The simulations used a horizontal grid spacing of 250 meters and employed a detailed triple-moment bulk microphysics scheme capable of predicting the liquid fraction of hydrometeors. SIP processes, including the Hallett-Mossop (HM) and fragmentation of freezing drops (FFD), are parameterized in this study. The NWP simulation results are compared with observational data collected during the ICICLE campaign. Sensitivity tests were conducted to highlight the importance of better quantifying SIP production rate in the NWP models. The findings indicate that SIP significantly enhances the simulated cloud ice number concentration and ice water content, particularly under strong convective conditions during winter. Additionally, the results reveal that the simulations are highly sensitive to the parameterization of HM and FFD processes due to the interaction between these two SIP mechanisms. High ice water content (HIWC) production is closely associated with SIP in strong convective conditions, whereas in stratiform conditions, HIWC can occur without a significant impact from SIP.
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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 | 0.001 |
| 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".