Comment on egusphere-2024-2160
Bibliographic record
Abstract
Abstract. Atmospheric gravity waves (i.e., buoyancy waves) can occur within stable layers when vertical oscillations are triggered by localized heating, flow over terrain, or imbalances in upper level flow. Case studies of winter storms have associated gravity waves with heavier surface snowfall, but the representativeness of those findings for settings without orographic precipitation has not been previously addressed. To detect gravity waves, we deployed networks of high precision pressure sensors from January 2020 to April 2023 in and around Toronto, ON, Canada, and New York, NY, USA, two regions without strong topographic forcing. Pressure wave events were identified when at least 4 sensors in a network detected propagating pressure waves with wave periods ≤ 67 min, wavelengths ≤ 170 km, and amplitudes ≥ 0.45 hPa. We detected 33 pressure wave events across 40 months of data, of which 23 were gravity waves and the rest were frontal passages, outflow boundary passages, or a wake low. Reanalysis model output and operational weather observations provided environmental context for each gravity wave event. Consistent with previous work, most gravity wave events occurred with strong upper-level flow imbalance to the south or west of their location. Of the 79 winter storms with snow that occurred over our 40 months of observations, only 6 had detectable gravity waves. For New York City, the typical offshore cyclone low center track means the metro area is usually in a location where gravity waves are not expected to occur.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.473 | 0.311 |
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".