Sensitivity of convective cell dynamics and microphysics to model resolution for lake-effect shallow convection
Bibliographic record
Abstract
Shallow convection over an unfrozen lake (Lake Ontario) during a cold-air outbreak is simulated using the Weather Research and Forecasting model (WRF) with two horizontal grid spacings, 148 m and 1.33 km. The dynamics and microphysics of the simulated convective snow band are compared to radar and aircraft observations. The dynamical and microphysical changes that occur when going from 1.33-km to 148-m grid spacing are explored. Improved representation of the convective dynamics at higher resolution leads to a better representation of the microphysics of the snowband compared to radar and aircraft observations. Stronger updrafts in the high-resolution grid lead to larger ice nucleation rates and produce ice particles that are more heavily rimed and thus faster falling. These changes to the ice particle properties in the high resolution grid limit aggregation rates and result in more realistic radar reflectivity patterns. Graupel, observed at the surface, is produced in the strongest convective updrafts, but only at the higher resolution. Ultimately, the quantitative precipitation forecast is improved at a higher grid resolution. Additionally, the duration of heavy precipitation just onshore, where convection collapses, is better predicted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".