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Register a new userAll-sky homogeneity of precipitable water vapour over Paranal
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A Low Humidity and Temperature Profiling (LHATPRO) microwave radiometer, manufactured by Radiometer Physics GmbH (RPG), is used to monitor sky conditions over ESOs Paranal observatory in support of VLT science operations. The unit measures several channels across the strong water vapour emission line at 183 GHz, necessary for resolving the low levels of precipitable water vapour (PWV) that are prevalent on Paranal (median ~2.4 mm). The instrument consists of a humidity profiler (183-191 GHz), a temperature profiler (51-58 GHz), and an infrared camera (~10 {mu}m) for cloud detection. We present, for the first time, a statistical analysis of the homogeneity of all-sky PWV using 21 months of periodic (every 6 hours) all-sky scans from the radiometer. These data provide unique insight into the spatial and temporal variation of atmospheric conditions relevant for astronomical observations, particularly in the infrared. We find the PWV over Paranal to be remarkably homogeneous across the sky down to 27.5{deg} elevation with a median variation of 0.32 mm (peak to valley) or 0.07 mm (rms). The homogeneity is a function of the absolute PWV but the relative variation is fairly constant at 10-15% (peak to valley) and 3% (rms). Such variations will not be a significant issue for analysis of astronomical data. Users at ESO can specify PWV - measured at zenith - as an ambient constraint in service mode to enable, for instance, very demanding observations in the infrared that can only be conducted during periods of very good atmospheric transmission and hence low PWV. We conclude that in general it will not be necessary to add another observing constraint for PWV homogeneity to ensure integrity of observations.
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Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R = 0.951 and R = 0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.
In the search for small exoplanets orbiting cool stars whose spectral energy distributions peak in the near infrared, the strong absorption of radiation in this region due to water vapour in the atmosphere is a particularly adverse effect for the ground-based observations of cool stars. To achieve the photometric precision required to detect exoplanets in the near infrared, it is necessary to mitigate the impact of variable precipitable water vapour (PWV) on radial-velocity and photometric measurements. The aim is to enable global PWV correction by monitoring the amount of precipitable water vapour at zenith and along the line of sight of any visible target. We developed an open source Python package that uses Geostationary Operational Environmental Satellites (GOES) imagery data, which provides temperature and relative humidity at different pressure levels to compute near real-time PWV above any ground-based observatory covered by GOES every 5 minutes or 10 minutes depending on the location. We computed PWV values on selected days above Cerro Paranal (Chile) and San Pedro Martir (Mexico) to benchmark the procedure. We also simulated different pointing at test targets as observed from the sites to compute the PWV along the line of sight. To asses the accuracy of our method, we compared our results with the on-site radiometer measurements obtained from Cerro Paranal. Our results show that our publicly-available code proves to be a good supporting tool for measuring the local PWV for any ground-based facility within the GOES coverage, which will help in reducing correlated noise contributions in near-infrared ground-based observations that do not benefit from on-site PWV measurements.
At Paranal Observatory, the least predictable parameter affecting the short-term scheduling of astronomical observations is the optical turbulence, especially the seeing, coherence time and ground layer fraction. These are critical variables driving the performance of the instruments of the Very Large Telescope (VLT), especially those fed with adaptive optics systems. Currently, the night astronomer does not have a predictive tool to support him/her in decision-making at night. As most service-mode observations at the VLT last less than two hours, it is critical to be able to predict what will happen in this time frame, to avoid time losses due to sudden changes in the turbulence conditions, and also to enable more aggressive scheduling. We therefore investigate here the possibility to forecast the turbulence conditions over the next two hours. We call this turbulence nowcasting, analogously with weather nowcasting, a term already used in meteorology coming from the contraction of now and forecasting. We present here the results of a study based on historical data of the Paranal Astronomical Site Monitoring combined with ancillary data, in a machine learning framework. We show the strengths and shortcomings of such an approach, and present some perspectives in the context of the Extremely Large Telescope.
This article aims at proving the feasibility of the forecast of all the most relevant classical atmospherical parameters for astronomical applications (wind speed and direction, temperature) above the ESO ground-base site of Cerro Paranal with a mesoscale atmospherical model called Meso-Nh. In a precedent paper we have preliminarily treated the model performances obtained in reconstructing some key atmospherical parameters in the surface layer 0-30~m studying the bias and the RMSE on a statistical sample of 20 nights. Results were very encouraging and it appeared therefore mandatory to confirm such a good result on a much richer statistical sample. In this paper, the study was extended to a total sample of 129 nights between 2007 and 2011 distributed in different parts of the solar year. This large sample made our analysis more robust and definitive in terms of the model performances and permitted us to confirm the excellent performances of the model. Besides, we present an independent analysis of the model performances using the method of the contingency tables. Such a method permitted us to provide complementary key informations with respect to the bias and the RMSE particularly useful for an operational implementation of a forecast system.
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