Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model
Bibliographic record
Abstract
Abstract Atmospheric gravity waves (GWs) during the February 2018 sudden stratospheric warming (SSW) event are simulated using the T639L340 whole neutral atmosphere general circulation model. Their characteristic morphology around the drastically evolving polar vortex is revealed by three‐dimensional (3D) visualization and ray‐tracing analyses. The 3D morphology of simulated GWs is described for the three key days that represent the pre‐SSW conditions, the mature stage for the vortex splitting, and the late SSW. The combination of strong winds along the polar vortex edge and underneath the tropospheric winds with similar wind directions consist of the deep waveguide for the upward‐propagating GWs, forming GW hot spots in the middle atmosphere. The GW hot spots associated with the development of the SSW are limited to North America and Greenland, and they include the typical upward‐propagating orographic GWs with relatively long vertical wavelengths. Different types of characteristic GW signatures are also recognized around the Canadian sub‐vortex (CV). GWs having short vertical wavelengths form near the surface and obliquely propagate over long distances along the CV winds. The non‐orographic GWs with short vertical wavelengths form in the middle stratosphere through the spontaneous adjustment of flow imbalance around the CV. Those GWs cyclonically ascend into the mesosphere along CV winds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".