Variability and predictability of precipitation type and accumulation during near-freezing surface conditions during WINTRE-MIX
Bibliographic record
Abstract
<!--!introduction!--> While near-freezing precipitation events have large impacts on human and natural systems, fundamental challenges remain in our ability to adequately observe, diagnose, simulate, and predict these events. The Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was designed to study the multiscale processes influencing the variability and predictability of p-type (rain, drizzle, freezing rain, freezing drizzle, wet snow, ice pellets, and snow) as well as the precipitation accumulation under near-freezing surface conditions. The experiment was conducted in February-March 2022 in the vicinity of Montreal. The field campaign utilized operational networks (New York State Mesonet, Canada Foundation of Innovation Climate Sentinels) and research instruments like the NRC Convair-580 research aircraft with a suite of in-situ and remote sensors, one C-band on Wheels (COW), two X-band Doppler on Wheels (DOWs) radars, mobile sounding systems, and manual p-type observation stations. We will analyze the variability of mesoscale cloud and precipitation structures and mesoscale flow during near-freezing surface conditions. Small-scale vertical motions within clouds (convective generating cells, coherent wave motions, and shear-driven turbulence) are shown to enhance the formation of ice in supercooled clouds, leading to the enhancement of surface snow or rain. Mesoscale precipitation bands, produced by either convergence of mesoscale terrain-channeled flows or by embedded disturbances within synoptic storms, locally enhance vertical motion, increase cloud depth, and intensify precipitation rates.
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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 | 0.000 | 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 | 0.000 |
| 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".