Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPrecipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory
57
0
0.0
(
0
)
Added by
Gabriel Perez Jordan
Publication date
2018
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
The atmospheric water vapor content above the Roque de los Muchachos Observatory (ORM) obtained from Global Positioning Systems (GPS) is presented. GPS measurements have been evaluated by comparison with 940nm-radiometer observations. Statistical analysis of GPS measurements points to ORM as an observing site with suitable conditions for infrared (IR) observations, with a median column of precipitable water vapor (PWV) of 3.8 mm. PWV presents a clear seasonal behavior, being Winter and Spring the best seasons for IR observations. The percentage of nighttime showing PWV values smaller than 3 mm is over 60% in February, March and April. We have also estimated the temporal variability of water vapor content at the ORM. A summary of PWV statistical results at different astronomical sites is presented, recalling that these values are not directly comparable as a result of the differences in the techniques used to recorded the data.
We present the largest database so far of atmospheric optical-turbulence profiles (197035 individual CN2(h)) for an astronomical site, the Roque de los Muchachos Observatory (La Palma, Spain). This C2 (h) database was obtained through generalized-SCIDAR observations at the 1 meter Jacobus Kapteyn telescope from Febrary 2004 to August 2009, obtaining useful data for 211 nights. The overestimation of the turbulence strength induced during the generalized SCIDAR data processing has been analyzed for the different observational configurations. All the individual C2 (h) have been recalibrated to compensate the introduced errors during data treatment following (Avila & Cuevas 2009). Comparing results from profiles before and after the recalibration, we analyze its impact on the calculation of relevant parameters for adaptive optics.
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.
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.
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.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
