Related papers: OTProf: estimating high-resolution profiles of opt…
This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence strength ($C_n^2$) using gradient boosting machines. OTCliM…
Images captured from a long distance suffer from dynamic image distortion due to turbulent flow of air cells with random temperatures, and thus refractive indices. This phenomenon, known as image dancing, is commonly characterized by its…
Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried…
The mesoscale model Meso-NH is used to simulate the optical turbulence at Mt Graham (Arizona, USA), site of the Large Binocular Telescope. Measurements of the CN2-profiles obtained with a generalized scidar from 41 nights are used to…
Atmospheric turbulence degrades the performance of free-space optical (FSO) communication and remote sensing systems by introducing phase and intensity distortions. While a majority of research focuses on mitigating these effects to ensure…
Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ($C_n^2$) is highly relevant for the…
COupled SLope and scIntillation Detection And Ranging (CO-SLIDAR) is a recent profiling method of the vertical distribution of atmospheric turbulence strength ($C_n^2$ profile). It takes advantage of correlations of slopes and of…
We use spatio-temporal cross-correlations of slopes from five Shack-Hartmann wavefront sensors to analyse the temporal evolution of the atmospheric turbulence layers at different altitudes. The focus is on the verification of the frozen…
Atmospheric turbulence strength (Cn2 parameter) sensing based on processing of intensity scintillation patterns with deep neural network (DNN) is considered. It is shown that DNN re-training with propagation distance change can be avoided…
CO-SLIDAR is a very promising technique for the metrology of near ground $C_n^2$ profiles. It exploits both phase and scintillation measurements obtained with a dedicated wavefront sensor and allows profiling on the full line of sight…
Knowledge of the optical refractive index structure parameter $C_n^2$ is of interest for Free Space Optics (FSO) and ground-based optical astronomy, as it depicts the strength of the expected scintillation on the received optical waves.…
Optical turbulence, driven by fluctuations of the atmospheric refractive index, poses a significant challenge to ground-based optical systems, as it distorts the propagation of light. This degradation affects both astronomical observations…
Advanced adaptive optics (AO) instruments on ground-based telescopes require accurate knowledge of the atmospheric turbulence strength as a function of altitude. This information assists point spread function reconstruction, AO temporal…
A characterization of the optical turbulence vertical distribution (Cn2 profiles) and all the main integrated astroclimatic parameters derived from the Cn2 and the wind speed profiles above the site of the Large Binocular Telescope (Mt.…
Prior statistical knowledge of the atmospheric turbulence is essential for designing, optimizing and evaluating tomographic adaptive optics systems. We present the statistics of the vertical profiles of $C_N^2$ and the outer scale at…
These last years ground-based astronomy has been looking towards Antarctica, especially its summits and the internal continental plateau where the optical turbulence (OT) appears to be confined in a shallow layer close to the surface.…
Deep learning is revolutionizing weather forecasting, with new data-driven models achieving accuracy on par with operational physical models for medium-term predictions. However, these models often lack interpretability, making their…
Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-resolution weather models, which typically consume many hours…
Characterization of atmospheric turbulence is essential to understanding image quality of astronomical telescopes and applying adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol…
For extremely large telescopes, adaptive optics will be required to correct the Earth's turbulent atmosphere. The performance of tomographic adaptive optics is strongly dependent on the vertical distribution (profile) of this turbulence. An…