Free-Space Optical Communication (FSOC) links are considered a key technology to support the increasing needs of our connected, data-heavy world, but they are prone to disturbance through atmospheric processes such as optical turbulence. Since turbulence is highly dependent on local topographic and meteorological conditions, modeling optical turbulence strength (see manuscript PDF for symbol) is challenging during the design phase of an optical link or network. Over the past 25 years, (see manuscript PDF for symbol) parameterizations of varying complexities have been combined with various numerical weather prediction models for the spatio-temporal estimation of (see manuscript PDF for symbol). However, the outputs of these models can exhibit substantial variability based on the user-defined configuration that determines how atmospheric processes are represented. To address this concern, we propose to run not a single model configuration but multiple diverse ones to generate an ensemble estimate of (see manuscript PDF for symbol). We employ the Weather Research and Forecasting model (WRF) with ten different Planetary Boundary Layer (PBL) physics schemes forming a diverse ensemble yielding a probabilistic (see manuscript PDF for symbol) estimate. We demonstrate that this ensemble outperforms the individual runs when compared to scintillometer field measurements and show it to be robust against outliers. We believe that FSOC downstream tasks such as link budget estimations should also become more robust if based on a (see manuscript PDF for symbol) ensemble estimate compared to single model runs.
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.
Accurate simulation and forecasting of over-the-horizon propagation events are essential for various civilian and defense applications. We demonstrate the prowess of a newly proposed coupled mesoscale modeling and ray tracing framework in reproducing such an event. Wherever possible, routinely measured meteorological data from various platforms (e.g., radar and satellite) are utilized to corroborate the simulated results.
In this paper, we propose a novel parameterization for optical turbulence (C2n) simulations in the atmosphere. In this approach, C2n is calculated from the output of atmospheric models using a high-order turbulence closure scheme. An important feature of this parameterization is that, in the free atmosphere (i.e., above the boundary layer), it is consistent with a well-established C2n formulation by Tatarskii. Furthermore, it approaches a Monin-Obukhov similarity-based relationship in the surface layer. To test the performance of the proposed parameterization, we conduct mesoscale modeling and compare the simulated C2n values with those measured during two field campaigns over the Hawaii island. A popular regression-based approach proposed by Trinquet and Vernin (2007) is also used for comparison. The predicted C2n values, obtained from both the physically and statistically-based parameterizations, agree reasonably well with the observational data. However, in the presence of a large-scale atmospheric phenomenon (a breaking mountain wave), the physically-based parameterization outperforms the statistically-based one.
We propose a novel framework for the estimation of C2n in the atmosphere by utilizing an inherent vertical scaling characteristics of the temperature fields. Observations from a field campaign over the Hawaii island are used for rigorous validation. Furthermore, the strength of the proposed approach is demonstrated by direct comparison against an alternative approach based on the so-called Thorpe scale.
In this paper, we reconstruct the meteorological and optical environment during the time of Titanic’s disaster utilizing a state-of-the-art meteorological model, a ray-tracing code, and a unique public-domain dataset called the Twentieth Century Global Reanalysis. With high fidelity, our simulation captured the occurrence of an unusually high Arctic pressure system over the disaster site with calm wind. It also reproduced the movement of a polar cold front through the region bringing a rapid drop in air temperature. The simulated results also suggest that unusual meteorological conditions persisted several hours prior to the Titanic disaster which contributed to super-refraction and intermittent optical turbulence. However, according to the simulations, such anomalous conditions were not present at the time of the collision of Titanic with an iceberg.
The focus of this paper is on the estimation of optical turbulence (commonly characterized by C2n ) near the land-surface using routinely measured meteorological variables (e.g., temperature, wind speed). We demonstrate that an artificial neural network-based approach has the potential to be effectively utilized for this purpose. We use an extensive scintillometer-based C2n dataset from a recent field experiment in Texas, USA to evaluate the accuracy of the proposed approach.
Conventional techniques used to model optical wave propagation through the Earth’s atmosphere typically as- sume flow fields based on various empirical relationships. Unfortunately, these synthetic refractive index fields do not take into account the influence of transient macroscale and mesoscale (i.e. larger than turbulent microscale) atmospheric phenomena. Nevertheless, a number of atmospheric structures that are characterized by various spatial and temporal scales exist which have the potential to significantly impact refractive index fields, thereby resulting dramatic impacts on optical wave propagation characteristics. In this paper, we analyze a subset of spatio-temporal dynamics found to strongly affect optical waves propagating through these atmospheric struc- tures. Analysis of wave propagation was performed in the geometrical optics approximation using a standard ray tracing technique. Using a numerical weather prediction (NWP) approach, we simulate multiple realistic atmospheric events (e.g., island wakes, low-level jets, etc.), and estimate the associated refractivity fields prior to performing ray tracing simulations. By coupling NWP model output with ray tracing simulations, we demon- strate the ability to quantitatively assess the potential impacts of coherent atmospheric phenomena on optical ray propagation. Our results show a strong impact of spatio-temporal characteristics of the refractive index field on optical ray trajectories. Such correlations validate the effectiveness of NWP models as they offer a more comprehensive representation of atmospheric refractivity fields compared to conventional methods based on the assumption of horizontal homogeneity.
In this study, we present a brief review on the existing approaches for optical turbulence estimation in various layers of the Earth’s atmosphere. The advantages and disadvantages of these approaches are also discussed. An alternative approach, based on mesoscale modeling with parameterized turbulence, is proposed and tested for the simulation of refractive index structure parameter (C2n ) in the atmospheric boundary layer. The impacts of a few atmospheric flow phenomena (e.g., low-level jets, island wake vortices, gravity waves) on optical turbulence are discussed. Consideration of diverse geographic settings (e.g., flat terrain, coastal region, ocean islands) makes this study distinct.
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