This work presents an extended analysis of atmospheric refraction effects captured by time-lapse imagery for near-ground and near-horizontal paths. Monthly trends and multipath analysis of image shift caused by refraction during daytime are studied. Nighttime shift measurements during moonlit nights are also presented. Advanced nonlinear machine learning approaches for image shift prediction are implemented and the performance of the models is evaluated.
The performance of free space optical applications depends on accurate estimation of the trajectory of optical beams through the atmosphere. In situations where a signal at the wavelength of interest is not available at the target, the propagation path of an optical beam may be predicted based on the refractive index gradient profile of the atmosphere, typically using standard models such as the 1976 US Standard Atmosphere. However, the actual refractive conditions evolve with time and the formation of features, such as inverse temperature layers and ducts, can introduce strong refractive index gradients. We present ray tracing studies involving modeling and measurements of the effects of near-ground atmospheric refraction on near-horizontal beam propagation during daytime along a desert path at the Jornada Experimental Range in Las Cruces, NM. The amplitude of the diurnal deviation of the ray trajectory of a 1550-nm source is observed in simulation results where the refractivity profile was generated from numerical weather prediction. Visible time-lapse camera measurements of diurnal differential image effects (compression/stretch) are also compared with results predicted by numerical weather modeling. Additionally, a duct-like refractivity profile occurring in the morning at the site and whose parameters are estimated from time-lapse imagery, is imposed on the US standard atmosphere and the resulting differential trajectory effects are demonstrated.
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.
This work details the analysis of time-lapse images with a point-tracking image processing approach along with the use of an extensive numerical weather model to investigate image displacement due to refraction. The model is applied to create refractive profile estimates along the optical path for the days of interest. Ray trace analysis through the model profiles is performed and comparisons are made with the measured displacement results. Additionally, a supervised machine learning algorithm is used to build a predictive model to estimate the apparent displacement of an object, based on a set of measured metrological values taken in the vicinity of the camera. The predicted results again are compared with the field-imagery ones.
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