Summer planetary-scale oscillations: aura MLS temperature compared with ground-based radar wind
Bibliographic record
Abstract
Abstract. The advent of satellite based sampling brings with it the opportunity to examine virtually any part of the globe. Aura MLS mesospheric temperature data are analysed in a wavelet format for easy identification of possible planetary waves (PW) and aliases masquerading as PW. A calendar year, 2005, of eastward, stationary, and westward waves at a selected latitude is shown in separate panels for wave number range −3 to +3 for period range 8 h to 30 days (d). Such a wavelet analysis is made possible by Aura's continuous sampling at all latitudes 82° S–82° N. The data presentation is suitable for examination of years of data. However this paper focuses on the striking feature of a "dish-shaped" upper limit to periods near 2 d in mid-summer, with longer periods appearing towards spring and fall, a feature also commonly seen in radar winds. The most probable cause is suggested to be filtering by the summer jet at 70–80 km, the latter being available from ground based medium frequency radar (MFR). Classically, the phase velocity of a wave must be greater than that of the jet in order to propagate through it. As an attempt to directly relate satellite and ground based sampling, a PW event of period 8d and wave number 2, which appears to be the original rather than an alias, is compared with ground based radar wind data. An appendix discusses characteristics of satellite data aliases with regard to their periods and amplitudes.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".