Decomposition of Atmospheric Disturbances into Standing and Traveling Components, with Application to Northern Hemisphere Planetary Waves and Stratosphere–Troposphere Coupling
Bibliographic record
Abstract
Abstract This study updates a body of literature that aims to separate atmospheric disturbances into standing and traveling zonal wave components. Classical wavenumber–frequency analysis decomposes longitude- and time-dependent signals into contributions from distinct spatial and temporal scales. Here, an additional decomposition of the spectrum into standing and traveling components is described. Previous methods decompose the power spectrum into standing and traveling parts with no explicit allowance for covariance between the two. This study provides a simple method to calculate the variance of each of these components and the covariance between them. It is shown that this covariance is typically a significant portion of the variance of the total signal. The approach also preserves phase information and allows for the reconstruction of the real-space standing and traveling components. The technique is applied to reanalysis wintertime geopotential height anomalies in the Northern Hemisphere in order to investigate planetary wave interference effects in stratosphere–troposphere coupling. The results show that for planetary waves 1–3, standing waves explain the largest portion of the variance at low frequencies. An exception is for wave 1 in the high-latitude troposphere, where there is a strong westward-traveling wave. Furthermore, the antinodes of the standing waves have preferred longitudes that tend to align with the extremes of the climatological wave, suggesting that standing waves contribute to a linear interference effect that has been shown to be an important part of stratosphere–troposphere interactions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".