High-accuracy short-term precipitable water-vapour operational forecast at the Very Large Telescope and perspectives for sky background forecast
Bibliographic record
Abstract
ABSTRACT In this article, we present the first results ever obtained by applying the autoregressive (AR) technique to precipitable water vapour (PWV). The study is performed at the Very Large Telescope (VLT). The AR technique was proposed recently to provide forecasts of atmospheric and astroclimatic parameters on short time-scales (up to a few hours) by achieving much better performance with respect to the ‘standard forecasts’ provided in early afternoon for the coming night. The AR method uses real-time measurements of the parameter of interest to improve the forecasts performed with atmospherical models. Here, we used measurements provided by the Low Humidity And Temperature PROfiling microwave radiometer (LHATPRO), a radiometer measuring the PWV at the VLT continuously. When comparing the AR forecast at 1h with the standard forecast, we observe a gain factor of ∼8 (i.e. ∼800 per cent) in terms of forecast accuracy. In the PWV ≤ 1 mm range, which is extremely critical for infrared astronomical applications, the RMSE of the predictions is of the order of just a few hundredth of millimetres (0.04 mm). We therefore proved that the AR technique provides an important benefit to VLT science operations for all instruments sensitive to PWV. Also, we show how such an ability to predict PWV can also be useful to predict the sky background in the infrared range [extremely appealing for Mid-infrared ELT Imager and Spectrograph (METIS)]. We quantify such an ability by applying this method to the New Earth in the Alpha Cen region (NEAR) project supported by the European Southern Observatory (ESO) and Breakthrough Initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".