Estimation of atmospheric turbulence intensity based on deep learning

2021 ◽  
Author(s):  
Shengjie Ma ◽  
Shiqia Hao ◽  
Qingsong Zhao ◽  
Chenlu Xu
Keyword(s):  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Turbulence Intensity

scholarly journals On the factors influencing surface-layer energy closure and their seasonal variability over the semi-arid Loess Plateau of Northwest China

2012 ◽  
Vol 16 (3) ◽  
pp. 893-910
Author(s):  
X. Xiao ◽  
H. C. Zuo ◽  
Q. D. Yang ◽  
S. J. Wang ◽  
L. J. Wang ◽  
...  
Keyword(s):  
Surface Layer ◽  
Energy Balance ◽  
Atmospheric Turbulence ◽  
Loess Plateau ◽  
Turbulence Intensity ◽  
Source Area ◽  
Northwest China ◽  
The Impact ◽  
Semi Arid

Abstract. The energy observed in the surface layer, when using eddy-covariance techniques to measure turbulent fluxes, is not balanced. Important progress has been made in recent years in identifying potential reasons for this lack of closure in the energy balance, but the problem is not yet resolved. In this paper, long-term data that include output of tower, radiation, surface turbulence flux and soil measurement collected from September 2006 to August 2010 in the Semi-Arid Climate Change and Environment Observatory, Lanzhou University, in the semi-arid Loess Plateau of Northwest China, were analysed, focusing on the seasonal characteristics of the surface energy and the factors that have impact on the energy balance closure (EBC). The analysis shows that (1) the long-term observations are successful; the interaction between the land and the atmosphere in semi-arid climates can be represented by the turbulent transport of energy. In addition, even though the residual is obvious, this suggests that the factors that impact the EBC are stable, and their seasonal variations are identical. The analysis also shows that (2) four factors have obvious impact on the EBC: the diverse schemes for surface soil heat flux, the flux contribution from the target source area, the low-frequency part of the turbulence spectra, and the strength of atmospheric turbulence motion. The impact of these four factors on the EBC are similar in all seasons. Lastly, the results indicate that (3) atmospheric turbulence intensity is a very important factor in terms of its impact on the EBC. The relative turbulence intensity, RIw, characterises the strength of atmospheric turbulence motion, and is found to exert a noticeable impact on the EBC; in all seasons, the EBC is increased when the relative turbulence intensity is enlarged.

Two-dimensional phase unwrapping integrating deep learning and minimal cost flow

2021 ◽  
Author(s):  
Zhipeng Wu ◽  
Teng Wang ◽  
Yingjie Wang ◽  
Daqing Ge
Keyword(s):  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Deformation Monitoring ◽  
Phase Unwrapping ◽  
Minimal Cost ◽  
Localized Deformation ◽  
Flow Solver ◽  
Training Samples ◽  
Cost Flow ◽  
Minimal Cost Flow

<p>InSAR can measure surface deformation in all-weather conditions and has been widely used to study landslides, land subsidence, and many geophysical processes. Since the phase of radar echo is measured in 2π rad modulo (wrapped), phase unwrapping is an indispensable step for InSAR, and its reliability directly determines the feasibility of deformation monitoring. However, temporal and spatial decorrelation often leads to severe noises, localized deformation or strong atmospheric turbulence may result in dense fringes, both making traditional unwrapping methods fail in acquiring continual unwrapped phases. Here, we present a deep convolutional neural network (DENet) to identify the probability of phase discontinuities between every two adjacent pixels in the interferogram and apply the probability as cost in the widely-used minimal cost flow solver to achieve phase unwrapping. To train the network effectively, we design a simulation strategy to generate sufficient training samples: the terrain-related phases are used as the background phases, and the deformation phases, atmospheric turbulence phases, and noises are superimposed to build the training samples. Unlike classical methods such as GAMMA and SNAPHU that use the coherence map as the quality index, we use the probability of phase discontinuities estimated by the DENet as the arc-cost of the minimum cost flow problem. We apply the proposed method to unwrap simulated and real interferograms and compare the results with 8 existing methods (including traditional and deep learning-based ones).  On the simulated data set, the root-mean-square error (RMSE) of the proposed method is lower than all the 8 existing methods. We also test different methods to unwrap the real Sentinel-1 interferograms and verified the reliability using ALOS-2 data with a nearly identical acquisition period. Our results show strong robustness and stability when unwrapping very large interferograms with complicated phase patterns. The proposed method takes advantages of both deep learning and traditional minimal cost flow solver, which can effectively unwrap interferograms with low coherence and/or dense fringes, providing strong potential for large-scale SAR interferometry applications.</p>

scholarly journals Deep learning based atmospheric turbulence compensation for orbital angular momentum beam distortion and communication

2019 ◽  
Vol 27 (12) ◽  
pp. 16671 ◽  
Author(s):  
Junmin Liu ◽  
Peipei Wang ◽  
Xiaoke Zhang ◽  
Yanliang He ◽  
Xinxing Zhou ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Orbital Angular Momentum

scholarly journals Laboratory Measurements of the Influence of Turbulence Intensity on the Instantaneous-Fading Reciprocity of Bidirectional Atmospheric Laser Propagation Link

2021 ◽  
Vol 11 (8) ◽  
pp. 3499
Author(s):  
Yi Liu ◽  
Zhi Liu ◽  
Yidi Chang ◽  
Yang Liu ◽  
Huilin Jiang
Keyword(s):  
Correlation Coefficient ◽  
Atmospheric Turbulence ◽  
Turbulence Intensity ◽  
Optical Signal ◽  
Optical Channel ◽  
Optical Signals ◽  
Optical Channels ◽  
Laser Propagation ◽  
Hot Air ◽  
Air Convection

The reciprocity of the atmospheric turbulence channel in the bidirectional atmospheric laser propagation link is experimentally tested. The bidirectional transceiving coaxial atmospheric laser propagation link is built by using a hot air convection-type atmospheric turbulence emulation device with adjustable turbulence intensity. The influence of different turbulence intensities on the instantaneous-fading correlation of channel is analyzed by the spot characteristics. When there is no atmospheric turbulence in the bidirectional transceiving coaxial atmospheric laser propagation link, the value of channel instantaneous fading correlation coefficient was merely 0.023, which indicates we did not find any reciprocity in the optical channel. With the increment in turbulence intensity, the channel instantaneous fading correlation coefficient presented a constant increasing trend and then tended to be stable around 0.9 in the end. At this moment, the similarity of the instantaneous change trends for these two receiving terminal optical signals, and the consistency of their probability density function, indicates that there is good reciprocity between the bidirectional atmospheric turbulence optical channels. With the increase in the optical signal scintillation factor, we can obtain the result where the correlation coefficient value decreases accordingly.

scholarly journals Deep learning wavefront sensing and aberration correction in atmospheric turbulence

PhotoniX ◽  
2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Kaiqiang Wang ◽  
MengMeng Zhang ◽  
Ju Tang ◽  
Lingke Wang ◽  
Liusen Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Wavefront Sensor ◽  
Wavefront Aberration ◽  
Wavefront Sensing ◽  
Aberration Correction ◽  
Single Object ◽  
Multiple Objects ◽  
Correction Effect ◽  
And Performance

AbstractDeep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways: (i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What’s more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.

Analysis on the Effects of Atmospheric Turbulence on Line-of-Sight Ultraviolet Communication System Performance

2014 ◽  
Vol 1014 ◽  
pp. 102-105
Author(s):  
Yong Wang ◽  
Meng Ying Su
Keyword(s):  
Information Transmission ◽  
Atmospheric Turbulence ◽  
Turbulence Intensity ◽  
Communication System ◽  
Transmission Rate ◽  
Optical Signals ◽  
Communication System Performance ◽  
Intensity Changes ◽  
Ultraviolet Communication ◽  
Ber Performance

In the ultraviolet communication system, optical signals being transmitted are not only attenuated in energy, but also affected by the atmospheric turbulence. The effects of atmospheric turbulence intensity and information transmission rate on the BER performance of UV communication system are analyzed and simulated in this paper. It can be found that when the atmospheric turbulence intensity changes from weak to strong, the BER performance deteriorates along, and under the certain atmospheric turbulence intensity, information transmission rate will also have an impact on the system BER performance, the information transmission rate becomes faster, the BER performance gets worse.

Doppler Radar Measurements of Spatial Turbulence Intensity in the Atmospheric Boundary Layer

2019 ◽  
Vol 58 (7) ◽  
pp. 1535-1555 ◽  
Author(s):  
James B. Duncan ◽  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Atmospheric Turbulence ◽  
Turbulence Intensity ◽  
Large Scale ◽  
Doppler Radar ◽  
Atmospheric Stability ◽  
Temporal Analysis ◽  
Spatiotemporal Variability ◽  
Complex Flow

AbstractRemote sensing instruments that scan have the ability to provide high-resolution spatial measurements of atmospheric boundary layer winds across a region. However, the time required to collect the volume of measurements used to produce this spatial representation of atmospheric winds typically limits the extraction of atmospheric turbulence information using traditional temporal analysis techniques. To overcome this constraint, a spatial turbulence intensity (STI) metric was developed to quantify atmospheric turbulence intensity (TI) through analysis of spatial wind field variability. The methods used to determine STI can be applied throughout the measurement domain to transform the spatially distributed velocity fields to analogous measurement maps of STI. This method enables a comprehensive spatial characterization of atmospheric TI. STI efficacy was examined across a range of wind speeds and atmospheric stability regimes using both single- and dual-Doppler measurements. STI demonstrated the ability to capture rapid fluctuations in TI and was able to discern large-scale TI trends consistent with the early evening transition. The ability to spatially depict atmospheric TI could benefit a variety of research disciplines such as the wind energy industry, where an understanding of wind plant complex flow spatiotemporal variability is limited.

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