Detection of a 14-d atmospheric perturbation peak at Paranal associated with lunar cycles
Bibliographic record
Abstract
Abstract In this paper we investigate the correlation between the atmospheric perturbations at Paranal Observatory and the Chilean coast tides, which are mostly modulated by the 14-d syzygy solar–lunar tidal cycle. To this aim, we downloaded 15 yr (2003–2017) of cloud coverage data from the Aqua satellite, in a matrix that includes also Armazones, the site of the European Extremely Large Telescope. By applying the Fast Fourier transform to these data we detected a periodicity peak of about 14 d. We studied the tide cycle at Chanaral De Las Animas, on the ocean coast, for the year 2017, and we correlated it with the atmospheric perturbations at Paranal and the lunar phases. We found a significant correlation ($96{\rm {per\, cent}}$) between the phenomena of short duration and intensity (1–3 d) and the tidal cycle at Chanaral. We then show that an atmospheric perturbation occurs at Paranal in concomitance with the low tide, which anticipates the full (or the new) moon by 3–4 d. This result allows to improve current weather forecasting models for astronomical observatories by introducing a lunar variable.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".