High-speed optical communications for satellite data have been under study for several years; they offer a good alternative to radio frequency (RF) links which are approaching the limit of their capacities in terms of data rate. Indeed, the motivation of optical space communication systems stems from the fact that higher data rates (10 times) than RF solutions could be achievable with similar on-board terminal loads.

The Observatoire de la Côte d'Azur (OCA), via its MéO laser station on the Calern plateau, is involved in several projects implementing high-performance ground-space optical links.

The limiting factor of this kind of links is the atmospheric turbulence. Indeed, fluctuations of the refractive index of air cause fluctuations in the phase and amplitude (scintillation) of the wavefront. These sometimes very high disturbances can cause the loss of the signal and generally make the link budget weak.

The OCA observation site, Calern, is equipped recently with the ANAtOLIA station (https://anatolia.oca.eu/) for atmospheric turbulence characterization. ANAtOLIA is equipped with the GMT instrument which measures the vertical distributions of turbulence during nighttime and daytime using the lunar limb and solar edge. This GMT monitor measures also in G-DIMM mode integrated wavefront coherence parameters such as seeing, outer scale, coherence time, and isoplanetic angle on bright stars.

Several studies have been carried out at Calern Observatory by correlation between the budget of lasers (T2L2, OPALS, OSIRIS,…) and turbulence measured simultaneously by CATS and ANAtOLIA, highlighting the impact of the turbulence on optical links.

The objective of the proposed thesis is to go beyond understanding the impact of turbulence on laser links and to make optimal prediction of the conditions of the atmospheric turbulence. Such work will thus make it possible to identify favorable periods for laser links and to move towards "Smart / flexible Scheduling" as in Astronomy. Indeed, in the context of astronomical observations, the prediction of seeing conditions has now become essential with the advent of the next generation of ELTs. Indeed, it is important to reduce the cost of observations and improve their scientific performance. Future optical communications and telemetry stations will be forced to meet the same requirements. As part of this thesis work, we propose to continue a work already started within our team consisting in developing a tool allowing prediction of atmospheric turbulence parameters. This is done using a Weather Research and Forecasting (WRF) model combined with a statistical optical turbulence model to predict optical sky conditions several hours in advance and throughout the day. This information would make it possible to improve the management of laser links programs, and thus reduce losses due to variations in atmospheric conditions. 

The results obtained by the WRF model were compared with those measured directly by the CATS station and we were able to demonstrate on median profiles that turbulence is better predicted in the free atmosphere but less well in the planetary layer (h <500m) because of ground/atmosphere exchanges. We were also able to demonstrate that prediction is much more sensitive to certain parameters such as the speed and direction of the wind responsible for the movements and mixing of air masses. It has also been found that better support in the WRF model of physicochemical processes at the surface layer level allows improved prediction results. We also launched a study with the support of CNES using a drone equipped with a weather station to measure the PTU profiles (pressure, temperature, humidity and wind) in the first 500 meters. These profiles compared to those obtained by the WRF prediction model to optimize the model. More work has to be performed for drone automatic flights in order to increase their frequency. We started injecting these drone data in the predictive model but only at 2 or 3 levels which need to be increased to optimize the model. In addition, specific work must be carried out to define, as a function of the weather (seasons, beginning / end of night, etc.), the height of the target, the necessary adjustments of the models to have a good agreement between measurements and forecasts. For short term prediction, more work is needed for optimal forecasts by use of  Maching Learning algorithms such as Neuronal Networks.  

The goal is to develop a reliable, efficient and easy-to-use tool to predict atmospheric turbulence conditions within a few hours for better use of ground optical communications stations. Beyond this objective, we can also use this tool in addition to the ANAtOLIA station (relaying this station during interruptions) but also for the qualification of the sites of future stations at lower cost. It can also be a way to improve the selection of astronomically interesting sites.

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For more Information about the topics and the co-financial partner (found by the lab!); contact Directeur de thèse - aziz.ziad@univ-cotedazur.fr

Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online before March 13th, 2026 Midnight Paris time!