Lidar system for atmospheric turbulence measurement with Mersen telescope

2006 ◽  
Author(s):  
A. V. Savin ◽  
S. Yu. Strakhov ◽  
M. A. Konyaev ◽  
A. V. Trilis
Keyword(s):  
Atmospheric Turbulence ◽  
Lidar System ◽  
Turbulence Measurement

Atmospheric Turbulence Measurement System

1985 ◽  
Author(s):  
R. W. McMillan ◽  
R. A. Bohlander ◽  
R. H. Platt ◽  
D. M. Guillory ◽  
J. T. Priestley ◽  
...  
Keyword(s):  
Atmospheric Turbulence ◽  
Measurement System ◽  
Turbulence Measurement

In Situ Atmospheric Turbulence Measurement Using the Terrestrial Magnetic Field—A Compass for a Radiosonde

2006 ◽  
Vol 23 (3) ◽  
pp. 517-523 ◽  
Author(s):  
R. Giles Harrison ◽  
Robin J. Hogan
Keyword(s):  
Magnetic Field ◽  
Atmospheric Turbulence ◽  
Time Scale ◽  
Magnetic Field Measurement ◽  
Magnetic Fluctuations ◽  
Turbulence Measurement ◽  
Radar Measurements ◽  
In Situ Detection ◽  
Terrestrial Magnetic Field

Abstract A method for in situ detection of atmospheric turbulence has been developed using an inexpensive sensor carried within a conventional meteorological radiosonde. The sensor—a Hall effect magnetometer—was used to monitor the terrestrial magnetic field. Rapid time scale (10 s or less) fluctuations in the magnetic field measurement were related to the motion of the radiosonde, which was strongly influenced by atmospheric turbulence. Comparison with cloud radar measurements showed turbulence in regions where rapid time-scale magnetic fluctuations occurred. Reliable measurements were obtained between the surface and the stratosphere.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

Estimating Atmospheric Turbulence Parameters from the Envelopes of Radio Acoustic Signals

2001 ◽  
Vol 55 (8) ◽  
pp. 5
Author(s):  
V. M. Kartashov ◽  
V. A. Petrov ◽  
Ye. G. Proshkin ◽  
G. I. Sidorov
Keyword(s):  
Atmospheric Turbulence ◽  
Acoustic Signals ◽  
Turbulence Parameters

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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