在ALTA项目范围内LBT场地近地面大气参数的预报
Forecasts of the atmospherical parameters close to the ground at the LBT site in the context of the ALTA project
摘要：
在本文中，我们研究的能力，大气中尺度模式预测的经典大气参数天文应用在地面层（风速，风向，温度，相对湿度）的大型双目望远镜（LBT）网站-格雷厄姆山，亚利桑那州。该研究是在ALTA项目的框架内进行的，旨在实现一个自动化系统，用于LBT服务模式操作的大气参数（Meso-Nh代码）和光学湍流（Astro-Meso-Nh代码）的预测。这种操作工具的最终目标是提供大气和光学参数的高时间频率的预测，用于望远镜操作的优化规划（圆顶热化、风依赖圆顶定向、基于预测视宁度的观测规划、自适应光学优化等）。数值模拟进行了Meso-Nh和Astro-Meso-Nh代码，这被证明是给优秀的结果，在以前的研究集中在两个ESO网站的Cerro Paranal和Cerro Armazones（MOSE项目）。在本文中，我们将把我们的注意力集中在比较的大气参数预测的模式接近地面的观测仪器和存储在LBT遥测，以验证数值预报的测量。如先前对Cerro Paranal所做的（Lascaux等人，2015），我们还将提出基于列联表方法的模型性能分析，这使我们能够提供关于偏差和RMSE（系统和统计误差）的补充关键信息，例如正确检测的百分比和在定义的值区间内获得正确检测的概率。
英文摘要：
In this paper we study the abilities of an atmospherical mesoscale model in forecasting the classical atmospherical parameters relevant for astronomical applications at the surface layer (wind speed, wind direction, temperature, relative humidity) on the Large Binocular Telescope (LBT) site - Mount Graham, Arizona. The study is carried out in the framework of the ALTA project aiming at implementing an automated system for the forecasts of atmospherical parameters (Meso-Nh code) and the optical turbulence (Astro-Meso-Nh code) for the service-mode operation of the LBT. The final goal of such an operational tool is to provide predictions with high time frequency of atmospheric and optical parameters for an optimized planning of the telescope operation (dome thermalization, wind-dependent dome orientation, observation planning based on predicted seeing, adaptive optics optimization, etc...). Numerical simulations are carried out with the Meso-Nh and Astro-Meso-Nh codes, which were proven to give excellent results in previous studies focused on the two ESO sites of Cerro Paranal and Cerro Armazones (MOSE Project). In this paper we will focus our attention on the comparison of atmospherical parameters forescasted by the model close to the ground with measurements taken by the observatory instrumentations and stored in the LBT telemetry in order to validate the numerical predictions. As previously done for Cerro Paranal (Lascaux et al., 2015), we will also present an analysis of the model performances based on the method of the contingency tables, that allows us to provide complementary key information with the respect to the bias and RMSE (systematic and statistical errors), such as the percentage of correct detection and the probability to obtain a correct detection inside a defined interval of values.
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