A Case Study of the Solar and Lunar Semidiurnal Tide Response to the 2013 Sudden Stratospheric Warming
Bibliographic record
Abstract
Abstract This study investigates the response of the semidiurnal tide (SDT) to the 2013 major sudden stratospheric warming (SSW) event using meteor radar wind observations and mechanistic tidal model simulations. In the model, the background atmosphere is constrained to meteorological fields from the Navy Global Environmental Model—High Altitude analysis system. The solar (thermal) and lunar (gravitational) SDT components are forced by incorporating hourly temperature tendency fields from the ERA5 forecast model, and by specifying the M 2 and N 2 lunar gravitational potentials, respectively. The simulated SDT response is compared against meteor wind observations from the CMOR (43.3°N, 80.8°W), Collm (51.3°N, 13.0°E), and Kiruna (67.5°N, 20.1°E) radars, showing close agreement with the observed amplitude and phase variability. Numerical experiments investigate the individual roles of the solar and lunar SDT components in shaping the net SDT response. Further experiments isolate the impact of changing propagation conditions through the zonal mean background atmosphere, non‐linear wave‐wave interactions, and the SSW‐induced stratospheric ozone redistribution. Results indicate that between 80 and 97 km altitude in the northern hemisphere mid‐to‐high latitudes the net SDT response is driven by the solar SDT component, which itself is shaped by changing propagation conditions through the zonal mean background atmosphere and by non‐linear wave‐wave interactions. In addition, it is demonstrated that as a result of the rapidly varying solar SDT during the SSW the contribution of the lunar SDT to the total measured tidal field can be significantly overestimated.
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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) | 0.000 | 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 | 0.001 | 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".