Characteristics of Generating Cells in Wintertime Orographic Clouds
Bibliographic record
Abstract
Abstract During the Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE) field campaign, cloud-top generating cells were frequently observed in the very high-resolution W-band airborne cloud radar data. This study examines multiple flight segments from three SNOWIE cases that exhibited cloud-top generating cells structures, focusing on the in situ measurements inside and outside these cells to characterize the microphysics of these cells. The observed generating cells in these three cases occurred in cloud tops of −15° to −30°C, with and without overlying cloud layers, but always with shallow layers of atmospheric instability observed at cloud top. The results also indicate that liquid water content, vertical velocity, and drizzle and ice crystal concentrations are greater inside the generating cells compared to the adjacent portions of the cloud. The generating cells were predominantly <500 m in horizontal width and frequently exhibited drizzle drops coexisting with ice. The particle imagery indicates that ice particle habits included plates, columns, and rimed and irregular crystals, likely formed via primary ice nucleation mechanisms. Understanding the sources of natural ice formation is important to understanding precipitation formation in winter orographic clouds, and is especially relevant for clouds that may be targeted for glaciogenic cloud seeding as well as to improve model representation of these clouds. Significance Statement This study presents the characteristics of cloud-top generating cells in winter orographic clouds, and documents that fine-scale generating cells are ubiquitous in clouds over complex terrain in addition to having been observed in other types of clouds. The generating cells exhibited enhanced concentrations of larger drizzle and ice particles, which suggests the environments of these fine-scale features promote ice formation and growth. The source of ice formation in winter clouds is critical to understanding and modeling the precipitation formation process. Given the ubiquity of cloud-top generating cells in many types of clouds around the world, this study further motivates the need to investigate methods for representing subgrid-scale environments to improve ice formation in numerical models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".