Paper
11 July 2018 Evaluation of filtering techniques to increase the reliability of weather forecasts for ground-based telescopes
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Abstract
In this contribution we evaluate the impact of filtering techniques in enhancing the accuracy of forecasts of optical turbulence and atmospheric parameters critical for ground-based telescopes. These techniques make use of the data continuously provided by the telescope sensors and instruments to improve the performances of real-time forecasts which have an impact on the telescope operation. In previous works we have already shown how a mesoscale high-frequency forecast (Meso-NH and Astro-Meso-Nh models can produce reliable predictions of different atmospheric parameters and the optical turbulence. The mesoscale forecast has an advantage on the global model in having a better implementation of the physical atmospheric processes, including turbulence, and produces an output with greater spatial resolution (up to 100m or beyond). Filtering techniques that make use of the real-time sensor data at the telescope may help in removing potential biases and trends which have an impact on short term mesoscale forecast and, as a consequence, may increase the accuracy of the final output. Given the complexity and cost of present and future top-class telescope installations, each improvement of forecasts of future observing conditions will definitely help in better allocating observing time, especially in queue-mode operation, and will definitely benefit the scientific community in medium-long term.
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Alessio Turchi, Gianluca Martelloni, and Elena Masciadri "Evaluation of filtering techniques to increase the reliability of weather forecasts for ground-based telescopes", Proc. SPIE 10703, Adaptive Optics Systems VI, 107036H (11 July 2018); https://doi.org/10.1117/12.2312480
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KEYWORDS
Telescopes

Calibration

Autoregressive models

Atmospheric modeling

Data modeling

Reliability

Performance modeling

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