  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.
