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تسجيل مستخدم جديدMOSE: zooming on the Meso-NH mesoscale model performances at the surface layer at ESO sites (Paranal and Armazones)
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In the context of the MOSE project, in this contribution we present a detailed analysis of the Meso-NH mesoscale model performances and their dependency on the model and orography horizontal resolutions in proximity of the ground. The investigated sites are Cerro Paranal (site of the ESO Very Large Telescope - VLT) and Cerro Armazones (site of the ESO European Extremely Large Telescope - E-ELT), in Chile. At both sites, data from a rich statistical sample of different nights are available - from AWS (Automated Weather Stations) and masts - giving access to wind speed, wind direction and temperature at different levels near the ground (from 2 m to 30 m above the ground). In this study we discuss the use of a very high horizontal resolution (dX=0.1 km) numerical configuration that overcomes some specific limitations put in evidence with a standard configuration with dX=0.5 km. In both sites results are very promising. The study is co-funded by ESO and INAF.
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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 meso
scale 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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Franck Lascaux
, Elena Masciadri
, Luca Fini (INAF / Osservatorion Astrofisico di Arcetri
2013
This article is the second of a series of articles aiming at proving the feasibility of the forecast of all the most relevant classical atmospherical parameters for astronomical applications (wind speed and direction, temperature, relative humidity)
and the optical turbulence (Cn2 and the derived astro-climatic parameters like seeing, isoplanatic angle, wavefront coherence time...). This study is done in the framework of the MOSE project, and focused above the two ESO ground-bases sites of Cerro Paranal and Cerro Armazones. In this paper we present the results related to the Meso-Nh model ability in reconstructing the surface layer atmospherical parameters (wind speed intensity, wind direction and absolute temperature, [0-30] m a.g.l.). The model reconstruction of all the atmospherical parameters in the surface layer is very satisfactory. For the temperature, at all levels, the RMSE (Root Mean Square Error) is inferior to 1{deg}C. For the wind speed, it is ~2 m/s, and for the wind direction, it is in the range [38-46{deg}], at all levels, that corresponds to a RMSE_relative in a range [21-26{deg}]. If a filter is applied for the wind direction (the winds inferior to 3 m/s are discarded from the computations), the wind direction RMSE is in the range [30-41{deg}], i.e. a RMSE_relative in the range [17-23{deg}]. The model operational forecast of the surface layer atmospherical parameters is suitable for different applications, among others: thermalization of the dome using the reconstructed temperature, hours in advance, of the beginning the night; knowing in advance the main direction which the strong winds will come from during the night could allow the astronomer to anticipate the occurrence of a good/bad seeing night, and plan the observations accordingly; preventing adaptive secondary mirrors shake generated by the wind speed.
We present the overview of the MOSE project (MOdeling ESO Sites) aiming at proving the feasibility of the forecast of the classical atmospherical parameters (wind speed intensity and direction, temperature, relative humidity) and the optical turbulen
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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.
In a recent paper the authors presented an extended study aiming at simulating the classical meteorological parameters and the optical turbulence at Dome C during the winter with the atmospherical mesoscale model Meso-NH. A statistical analysis has b
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