Measuring and forecasting of PWV above La Silla, APEX and Paranal Observatories
Bibliographic record
Abstract
The content of precipitable water vapor (PWV) in the atmosphere is very important for astronomy in the infrared and radio (sub-millimeter) spectral regions. Therefore, the astrometeorology group has developed different methods to derive this value from measurements and making forecasts using a meteorological model. The goal is use that model to predict the atmospheric conditions and support the scheduling of astronomical observations. At ESO, several means to determine PWV over the observatories have been used, such as IR-radiometers (IRMA), optical and infrared spectrographs as well as estimates using data from GOES-12 satellite. Using all of these remote sensing methods a study undertaken to compare the accuracy of these PWV measurements to the simultaneous in-situ measurements provided by radiosondes. Four dedicated campaigns were conducted during the months of May, July, August and November of 2009 at the La Silla, APEX and Paranal observatory sites. In addition, the astrometeorological group employs the WRF meteorological model with the goal of simulating the state of the atmosphere (every 6 hours) and forecasting the PWV. With these simulations, plus satellite images, radiosonde campaign data can be classified synoptically and at the same time the model can be validated with respect to PWV.
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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 | 0.000 | 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) | 0.000 | 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".