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In this contribution we evaluate the impact of filtering techniques inenhancing the accuracy of forecasts of optical turbulence and atmosphericparameters critical for ground-based telescopes. These techniques make use ofthe data continuously provided by the telescope sensors and instruments toimprove the performances of real-time forecasts which have an impact on thetelescope operation. In previous works we have already shown how a mesoscalehigh-frequency forecast (Meso-NH and Astro-Meso-Nh models) can produce reliablepredictions of different atmospheric parameters and the optical turbulence. Themesoscale forecast has an advantage on the global model in having a betterimplementation of the physical atmospheric processes, including turbulence, andproduces an output with greater spatial resolution (up to 100m or beyond).Filtering techniques that make use of the real-time sensor data at thetelescope may help in removing potential biases and trends which have an impacton short term mesoscale forecast and, as a consequence, may increase theaccuracy of the final output. Given the complexity and cost of present andfuture top-class telescope installations, each improvement of forecasts offuture observing conditions will definitely help in better allocating observingtime, especially in queue-mode operation, and will definitely benefit thescientific community in medium-long term.

Evaluation of filtering techniques to increase the reliability of meteo forecasts for ground-based telescopes