Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework

Basu, Sukanta

doi:10.1117/1.oe.56.7.071505

Cited by 3 publications

(2 citation statements)

References 23 publications

(32 reference statements)

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“…However, at propagation sites with complicated terrain features, the refractive index structure parameter could be inhomogeneous along all three geographical coordinates (elevation, longitude and altitude), which makes generation of SIM datasets a challenging problem. Some progress in the modeling of refraction index and C 2 n fields dependent on 3D geographical coordinates and time has recently been made using numerical weather prediction (NWP) simulations [35][36][37]. The NWP approach could potentially be applied for generation of more accurate SIM datasets for DNN training.…”

Section: Cnˆ2net Scintillometer Training With Sim Datasets: Machine Learning-based Turbulence Profilingmentioning

confidence: 99%

Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns

et al. 2020

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A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric turbulence theoretical models and to evaluate them against the experimentally measured data.

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Section: Cnˆ2net Scintillometer Training With Sim Datasets: Machine Learning-based Turbulence Profilingmentioning

confidence: 99%

Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns

et al. 2020

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“…However, an extension of the established models is required to provide higher spatial resolution along our paths of interest. 11,12 In this section, we describe our approach for using refractive index gradient data generated by NWP and the application of ray tracing to determine corresponding image shifts. The results of the approach are compared with time-lapse measurements in Sec.…”

Section: Numerical Weather Prediction and Ray Tracingmentioning

confidence: 99%

Image shift due to atmospheric refraction: prediction by numerical weather modeling and machine learning

et al. 2020

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We develop and study two approaches for the prediction of optical refraction effects in the lower atmosphere. Refraction can cause apparent displacement or distortion of targets when viewed by imaging systems or produce steering when propagating laser beams. Low-cost, time-lapse camera systems were deployed at two locations in New Mexico to measure image displacements of mountain ridge targets due to atmospheric refraction as a function of time. Measurements for selected days were compared with image displacement predictions provided by (1) a ray-tracing evaluation of numerical weather prediction data and (2) a machine learning algorithm with measured meteorological values as inputs. The model approaches are described and the target displacement prediction results for both were found to be consistent with the field imagery in overall amplitude and phase. However, short time variations in the experimental results were not captured by the predictions where sampling limitations and uncaptured localized events were factors. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Beam trajectory studies of diurnal refraction effects for a near-ground path involving numerical weather prediction and time-lapse imaging measurements

Abdullah-Al-Mamun

Al-Younis

Wijerathna

et al. 2021

Laser Communication and Propagation Through the Atmosphere and Oceans X

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Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework

Cited by 3 publications

References 23 publications

Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns

Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns

Image shift due to atmospheric refraction: prediction by numerical weather modeling and machine learning

Beam trajectory studies of diurnal refraction effects for a near-ground path involving numerical weather prediction and time-lapse imaging measurements

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