Abstract
The prediction of atmospheric and turbulence conditions are important for astronomical observations and free space optical communications. In previous papers, we have used the Weather Research and Forecasting (WRF) model combined with optical turbulence models as forecasting tools. Results have shown that the atmospheric ground layer properties are not well predicted, in particular for the wind, which is used in turbulence models. Then, we have developed a statistical site learning to constrain our model by in-situ measurements to improve the optical turbulence prediction. An improvement of the prediction has been observed but some dispersion remains in the first 500 m of the atmosphere. In the continuity of this study, we have used an unmanned aircraft system (UAS) to measure the meteorological parameters (pressure, temperature, humidity, wind) in the first 500 m above ground. This kind of in-situ measurements of vertical profiles in this part of the atmosphere will allow us to improve the turbulence model. A first measurement campaign was conducted during the fall of 2020 on the Calern observatory, France. This first campaign has provided good data quality with high temporal and spatial resolution. Comparisons between the UAS data, the Moon Limb Profiler of the Calern Atmospheric Turbulence Station (CATS) and the predictions of the WRF model show promising results. Direct measurements of the temperature fluctuation structure constant <i>C</i><sup>2</sup><sub><i>T</i></sub> using precise probes on the instrumented UAS would be worth considering in future campaigns.
Published Version
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