Precipitable water vapour measurement using GNSS data in the Atacama Desert for millimetre and submillimetre astronomical observations
Bibliographic record
Abstract
ABSTRACT Precipitable water vapour (PWV) strongly affects the quality of data obtained from millimetre- and submillimetre-wave astronomical observations, such as those for cosmic microwave background measurements. Some of these observatories have used radiometers to monitor PWV. In this study, PWV was measured from 2021 April to 2022 April using Global Navigation Satellite System (GNSS) instruments in the Atacama Desert, Chile, where several millimetre- and submillimetre-wave telescopes are located. We evaluated the accuracy of these measurements by comparing them to radiometer measurements. We calculated the PWV from GNSS data using csrs-ppp (Canadian Spatial Reference System Precise Point Positioning), an online software package. When using GNSS data alone, the estimated PWV showed a systematic offset of $+1.08$ mm. When combining GNSS data with data from a barometer, which was co-located with the GNSS receiver, the estimated PWV showed a lower systematic offset of −0.05 mm. The GNSS PWV showed a statistical uncertainty of 0.52 mm with an averaging time of an hour. Compared to other PWV measurement methods, GNSS instruments are robust in bad weather conditions, have sufficient time resolution, and are less expensive. By demonstrating good accuracy and precision in low-PWV conditions, this paper shows that GNSS instruments are valuable tools for PWV measurements for observing site evaluation and data analysis for ground-based telescopes.
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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 | 0.000 | 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".