Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure network and radar observations
Bibliographic record
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 accumulations, but the representativeness of those findings for settings without orographic precipitation has not been previously addressed. 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 four sensors in a network detected propagating pressure waves with wave periods ≤67 min, wavelengths ≤170 km, and amplitudes ≥0.45 hPa. Reanalysis model output and operational weather observations provided environmental context for each gravity wave event. We detected 33 pressure wave events across 40 months of data; of these events, 23 were gravity waves, whereas the rest were frontal passages, outflow boundary passages, or a wake low. We found a strong linear relationship between amplitude and event duration for the 23 atmospheric gravity wave events. Gravity wave events are rare in non-orographic snow storms in our study region. Of the 594 h with ≥0.1 mm h −1 (liquid equivalent) of snow sampled, only 19 h was during a gravity wave event. When gravity waves and enhanced reflectivity bands within snow co-occurred, the bands did not move in a direction or at a velocity consistent with the pressure waves. In agreement with previous work, most of our gravity wave events are associated with strong upper-level flow imbalances to the south or west of their location.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".