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The prediction of the atmospheric and turbulence conditions are of great interest for the astronomical community
and free space optical telecommunications. With the advent of the next generation of extremely large telescopes
(ELT), the knowledge of atmospheric conditions several hours prior to observations has become essential. As
well, in the field of free space optical telecommunications, the propagation of optical signals in the atmosphere
is significantly influenced by weather conditions (clouds, fog, rain, etc.) and optical turbulence. A numerical
approach based on the Weather and Research Forecasting (WRF) model coupled with an optical turbulence
model for the predictions above the Calern site has been used. Results have shown good agreement between
predictions and observation. However, a difference persists in the first 500 m of the atmosphere. We present in
this paper two approaches to improve the predictions. The first one has been developed by Giordano et al. (2021)
using a ‘site learning' method. The second one consists of using data from an instrumented drone to improve
the initial conditions of the simulations. We will also present a new method for short-term prediction using two
statistical learning algorithms: ARIMA (Autoregressive Integrated Moving Average) and SARIMA (Seasonal
Autoregressive Integrated Moving Average).
