scholarly journals A review of turbulence measurements using ground-based wind lidars

2013 ◽  
Vol 6 (11) ◽  
pp. 3147-3167 ◽  
Author(s):  
A. Sathe ◽  
J. Mann
Keyword(s):  
Turbulence Model ◽  
Isotropic Turbulence ◽  
Lidar Data ◽  
Turbulence Statistics ◽  
Probe Volume ◽  
Turbulence Measurements ◽  
Lidar Measurements ◽  
Processing Algorithms ◽  
New Algorithms ◽  
Standard Techniques

Abstract. A review of turbulence measurements using ground-based wind lidars is carried out. Works performed in the last 30 yr, i.e., from 1972–2012 are analyzed. More than 80% of the work has been carried out in the last 15 yr, i.e., from 1997–2012. New algorithms to process the raw lidar data were pioneered in the first 15 yr, i.e., from 1972–1997, when standard techniques could not be used to measure turbulence. Obtaining unfiltered turbulence statistics from the large probe volume of the lidars has been and still remains the most challenging aspect. Until now, most of the processing algorithms that have been developed have shown that by combining an isotropic turbulence model with raw lidar measurements, we can obtain unfiltered statistics. We believe that an anisotropic turbulence model will provide a more realistic measure of turbulence statistics. Future development in algorithms will depend on whether the unfiltered statistics can be obtained without the aid of any turbulence model. With the tremendous growth of the wind energy sector, we expect that lidars will be used for turbulence measurements much more than ever before.

scholarly journals A review of turbulence measurements using ground-based wind lidars

2013 ◽  
Vol 6 (4) ◽  
pp. 6815-6871 ◽  
Author(s):  
A. Sathe ◽  
J. Mann
Keyword(s):  
Turbulence Model ◽  
Isotropic Turbulence ◽  
Lidar Data ◽  
Turbulence Statistics ◽  
Probe Volume ◽  
Turbulence Measurements ◽  
Lidar Measurements ◽  
Processing Algorithms ◽  
New Algorithms ◽  
Standard Techniques

Abstract. A review of turbulence measurements using ground-based wind lidars is carried out. Works performed in the last 30 yr, i.e. from 1972–2012 are analyzed. More than 80% of the work has been carried out in the last 15 yr, i.e. from 1997–2012. New algorithms to process the raw lidar data were pioneered in the first 15 yr, i.e. from 1972–1997, where standard techniques could not be used to measure turbulence. Obtaining unfiltered turbulence statistics from the large probe volume of the lidars has been and still remains the most challenging aspect. Until now, most of the processing algorithms that have been developed have shown that by combining an isotropic turbulence model with raw lidar measurements, we can obtain unfiltered statistics. We believe that an anisotropic turbulence model will provide a more realistic measure of a turbulence statistic. Future development in algorithms will depend on whether the unfiltered statistics can be obtained without the aid of any turbulence model. With the tremendous growth of the wind energy sector, we expect that lidars will be used for turbulence measurements much more than ever before.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Numerical and experimental study on turbulence statistics in a large fan-stirred combustion vessel

2021 ◽  
Vol 62 (5) ◽  
Author(s):  
M. E. Morsy ◽  
J. Yang
Keyword(s):  
Isotropic Turbulence ◽  
Burning Velocity ◽  
Three Dimensional ◽  
Flow Characteristics ◽  
Turbulence Models ◽  
Initial Pressure ◽  
Dynamics Modeling ◽  
Turbulence Statistics ◽  
Experimental Conditions ◽  
Measured Velocity

Abstract Particle image velocimetry (PIV) has become a popular non-intrusive tool for measuring various types of flows. However, when measuring three-dimensional flows with two-dimensional (2D) PIV, there are some uncertainties in the measured velocity field due to out-of-plane motion, which might alter turbulence statistics and distort the overall flow characteristics. In the present study, three different turbulence models are employed and compared. Mean and fluctuating fields obtained by three-dimensional computational fluid dynamics modeling are compared to experimental data. Turbulence statistics such as integral length scale, Taylor microscale, Kolmogorov scale, turbulence kinetic energy, dissipation rate, and velocity correlations are calculated at different experimental conditions (i.e., pressure, temperature, fan speed, etc.). A reasonably isotropic and homogeneous turbulence with large turbulence intensities is achieved in the central region extending to almost 45 mm radius. This radius decreases with increasing the initial pressure. The influence of the third dimension velocity component on the measured characteristics is negligible. This is a result of the axisymmetric features of the flow pattern in the current vessel. The results prove that the present vessel can be conveniently adopted for several turbulent combustion studies including mainly the determination of turbulent burning velocity for gaseous premixed flames in nearly homogeneous isotropic turbulence. Graphic abstract

scholarly journals Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011

2014 ◽  
Vol 7 (9) ◽  
pp. 3095-3112 ◽  
Author(s):  
P. Sawamura ◽  
D. Müller ◽  
R. M. Hoff ◽  
C. A. Hostetler ◽  
R. A. Ferrare ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Measurements ◽  
Index Of Refraction ◽  
High Spectral Resolution ◽  
Lidar Data ◽  
Data Set ◽  
Microphysical Properties ◽  
Lidar Measurements ◽  
Multiwavelength Lidar

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.

scholarly journals Exploring the Twilight Zone: A Multi-Sensor Approach

2020 ◽  
Vol 237 ◽  
pp. 07015
Author(s):  
Jonatan da Silva ◽  
Fernando G. Morais ◽  
Marco A. Franco ◽  
Fábio J. S. Lopes ◽  
Gregori de A. Arruda ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Vertical Profile ◽  
Sao Paulo ◽  
Twilight Zone ◽  
Upward Movement ◽  
São Paulo ◽  
Turbulence Measurements ◽  
Lidar Measurements ◽  
Satellite Instruments

This study shows a set of analysis of measurements from ground-based and satellite instruments to characterize the twilight zone (TLZ) between clouds and aerosols in São Paulo, Brazil. In the vicinity of clouds turbulence measurements showed an intense upward movement of aerosol layers, while sunphotometer results showed an increase in aerosol optical depth, and lidar measurements showed an increase in the backscatter vertical profile signal.

scholarly journals The Independent Variable Interpolation Technique for Nonuniformly Sampled Shallow-Angle Lidar Data

2011 ◽  
Vol 28 (12) ◽  
pp. 1672-1678 ◽  
Author(s):  
M. R. Belmont ◽  
P. Ashwin
Keyword(s):  
Computational Cost ◽  
Lidar Data ◽  
Distributed Data ◽  
Sampled Data ◽  
Sea Waves ◽  
Energy Capture ◽  
New Class ◽  
Lidar Measurements ◽  
Practical Engineering ◽  
Independent Variable

Abstract Shallow-angle lidar offers an attractive approach to acquiring spatial profiles of sea waves, which are of value in both oceanographic research and practical engineering applications, such as in the control of wave energy capture devices and for a variety of vessel operations. However, the wave elevation values produced by shallow-angle lidar are inevitably nonuniformly distributed in space and, given that most processing algorithms require uniformly sampled data, an equivalent set of uniformly distributed data must be derived from the lidar measurements. A new class of algorithm is introduced to achieve this goal and applied to experimental shallow-angle lidar data. Compared to traditional methods the new approach has advantages in terms of both computational cost and the degree of nonuniformity that can be accommodated.

scholarly journals Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea

2009 ◽  
Vol 66 (6) ◽  
pp. 1023-1028 ◽  
Author(s):  
James H. Churnside ◽  
Eirik Tenningen ◽  
James J. Wilson
Keyword(s):  
Data Processing ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Norwegian Sea ◽  
Fish Schools ◽  
Processing Algorithms ◽  
High Level ◽  
Processing Techniques ◽  
Data Processing Algorithms ◽  
Broad Scale

Abstract Churnside, J. H., Tenningen, E., and Wilson, J. J. 2009. Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea. – ICES Journal of Marine Science, 66: 1023–1028. A broad-scale lidar survey was conducted in the Norwegian Sea in summer 2002. Since then, various data-processing techniques have been developed, including manual identification of fish schools, multiscale median filtering, and curve fitting of the lidar profiles. In the automated techniques, applying a threshold to the data, as carrried out already to eliminate plankton scattering, has been demonstrated previously to improve the correlation between lidar and acoustic data. We applied these techniques to the lidar data of the 2002 survey and compared the results with those of a mackerel (Scomber scombrus) survey done by FV “Endre Dyrøy” and FV “Trønderbas” during the same period. Despite a high level of variability in both lidar and trawl data, the broad-scale distribution of fish inferred from the lidar agreed with that of mackerel caught by the FV “Endre Dyrøy”. This agreement was obtained using both manual and automated processing of the lidar data. This work is the first comparison of concurrent lidar and trawl surveys, and it demonstrates the utility of airborne lidar for mackerel studies.

scholarly journals Modern Approaches to Detect and Classify Comment Toxicity Using Neural Networks

2020 ◽  
Vol 27 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Sergey V. Morzhov
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Rapid Evolution ◽  
Learning Technologies ◽  
Training Set ◽  
Online Platforms ◽  
Processing Algorithms ◽  
New Algorithms

The growth of popularity of online platforms which allow users to communicate with each other, share opinions about various events, and leave comments boosted the development of natural language processing algorithms. Tens of millions of messages per day are published by users of a particular social network need to be analyzed in real time for moderation in order to prevent the spread of various illegal or offensive information, threats and other types of toxic comments. Of course, such a large amount of information can be processed quite quickly only automatically. that is why there is a need to and a way to teach computers to “understand” a text written by humans. It is a non-trivial task even if the word “understand” here means only “to classify”. the rapid evolution of machine learning technologies has led to ubiquitous implementation of new algorithms. A lot of tasks, which for many years were considered almost impossible to solve, are now quite successfully solved using deep learning technologies. this article considers algorithms built using deep learning technologies and neural networks which can successfully solve the problem of detection and classification of toxic comments. In addition, the article presents the results of the developed algorithms, as well as the results of the ensemble of all considered algorithms on a large training set collected and tagged by Google and Jigsaw.

scholarly journals Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging

2021 ◽  
Vol 14 (2) ◽  
pp. 1457-1474
Author(s):  
Matteo Puccioni ◽  
Giacomo Valerio Iungo
Keyword(s):  
Turbulent Energy ◽  
Lidar Data ◽  
Spatial Averaging ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Probe Length ◽  
Lidar Measurements ◽  
Low Pass ◽  
Sampling Height ◽  
Wind Lidar

Abstract. Continuous advancements in pulsed wind lidar technology have enabled compelling wind turbulence measurements within the atmospheric boundary layer with probe lengths shorter than 20 m and sampling frequency on the order of 10 Hz. However, estimates of the radial velocity from the back-scattered lidar signal are inevitably affected by an averaging process within each probe volume, generally modeled as a convolution between the true velocity projected along the lidar line-of-sight and an unknown weighting function representing the energy distribution of the laser pulse along the probe length. As a result, the spectral energy of the turbulent velocity fluctuations is damped within the inertial subrange, thus not allowing one to take advantage of the achieved spatio-temporal resolution of the lidar technology. We propose to correct the turbulent energy damping on the lidar measurements by reversing the effect of a low-pass filter, which can be estimated directly from the power spectral density of the along-beam velocity component. Lidar data acquired from three different field campaigns are analyzed to describe the proposed technique, investigate the variability of the filter parameters and, for one dataset, assess the corrected velocity variance against sonic anemometer data. It is found that the order of the low-pass filter used for modeling the energy damping on the lidar velocity measurements has negligible effects on the correction of the second-order statistics of the wind velocity. In contrast, the cutoff wavenumber plays a significant role in spectral correction encompassing the smoothing effects connected with the lidar probe length. Furthermore, the variability of the spatial averaging on wind lidar measurements is investigated for different wind speed, turbulence intensity, and sampling height. The results confirm that the effects of spatial averaging are enhanced with decreasing wind speed, smaller integral length scale and, thus, for smaller sampling height. The method proposed for the correction of the second-order turbulent statistics of wind-velocity lidar data is a compelling alternative to existing methods because it does not require any input related to the technical specifications of the used lidar system, such as the energy distribution over the laser pulse and lidar probe length. On the other hand, the proposed method assumes that surface-layer similarity holds.

scholarly journals Doppler Lidar Estimation of Mixing Height Using Turbulence, Shear, and Aerosol Profiles

2009 ◽  
Vol 26 (4) ◽  
pp. 673-688 ◽  
Author(s):  
Sara C. Tucker ◽  
Christoph J. Senff ◽  
Ann M. Weickmann ◽  
W. Alan Brewer ◽  
Robert M. Banta ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Potential Temperature ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Mixing Height ◽  
Coherent Doppler Lidar ◽  
Lidar Measurements ◽  
Comparison Of The Results ◽  
Humidity Profiles

Abstract The concept of boundary layer mixing height for meteorology and air quality applications using lidar data is reviewed, and new algorithms for estimation of mixing heights from various types of lower-tropospheric coherent Doppler lidar measurements are presented. Velocity variance profiles derived from Doppler lidar data demonstrate direct application to mixing height estimation, while other types of lidar profiles demonstrate relationships to the variance profiles and thus may also be used in the mixing height estimate. The algorithms are applied to ship-based, high-resolution Doppler lidar (HRDL) velocity and backscattered-signal measurements acquired on the R/V Ronald H. Brown during Texas Air Quality Study (TexAQS) 2006 to demonstrate the method and to produce mixing height estimates for that experiment. These combinations of Doppler lidar–derived velocity measurements have not previously been applied to analysis of boundary layer mixing height—over the water or elsewhere. A comparison of the results to those derived from ship-launched, balloon-radiosonde potential temperature and relative humidity profiles is presented.

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