Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015

2016 ◽  
Vol 162 (3) ◽  
pp. 503-522 ◽  
Author(s):  
Meng Huang ◽  
Zhiqiu Gao ◽  
Shiguang Miao ◽  
Fei Chen ◽  
Margaret A. LeMone ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Layer Depth ◽  
Beijing China

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.

scholarly journals Year-Long Vertical Velocity Statistics Derived from Doppler Lidar Data for the Continental Convective Boundary Layer

2017 ◽  
Vol 56 (9) ◽  
pp. 2441-2454 ◽  
Author(s):  
Larry K. Berg ◽  
Rob K. Newsom ◽  
David D. Turner
Keyword(s):  
Boundary Layer ◽  
Shear Stress ◽  
Vertical Velocity ◽  
Wind Shear ◽  
Time Of Day ◽  
Lidar Data ◽  
Surface Shear Stress ◽  
Doppler Lidar ◽  
Convective Boundary ◽  
Degree Of Instability

AbstractOne year of coherent Doppler lidar data collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement site in Oklahoma was analyzed to provide profiles of vertical velocity variance, skewness, and kurtosis for cases of cloud-free convective boundary layers. The variance was normalized by the Deardorff convective velocity scale, which was successful when the boundary layer depth was stationary but failed in situations in which the layer was changing rapidly. In this study, the data are sorted according to time of day, season, wind direction, surface shear stress, degree of instability, and wind shear across the boundary layer top. The normalized variance was found to have its peak value near a normalized height of 0.25. The magnitude of the variance changes with season, shear stress, degree of instability, and wind shear across the boundary layer top. The skewness was largest in the top half of the boundary layer (with the exception of wintertime conditions). The skewness was also found to be a function of the season, shear stress, and wind shear across the boundary layer top. Like skewness, the vertical profile of kurtosis followed a consistent pattern, with peak values near the boundary layer top. The normalized altitude of the peak values of kurtosis was found to be higher when there was a large amount of wind shear at the boundary layer top.

scholarly journals Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer

2017 ◽  
Author(s):  
Igor N. Smalikho ◽  
Viktor A. Banakh
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Radial Velocity ◽  
Dissipation Rate ◽  
Stream Line ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Integral Scale ◽  
Coherent Doppler Lidar ◽  
Turbulence Parameters

Abstract. The method and results of lidar studies of spatiotemporal variability of wind turbulence in the atmospheric boundary layer are reported. The measurements were conducted by a Stream Line pulsed coherent Doppler lidar with the use of conical scanning by a probing beam around the vertical axis. Lidar data are used to estimate the kinetic energy of turbulence, turbulent energy dissipation rate, integral scale of turbulence, and momentum fluxes. The dissipation rate was determined from the azimuth structure function of radial velocity within the inertial subrange of turbulence. When estimating the kinetic energy of turbulence from lidar data, we took into account the averaging of radial velocity over the sensing volume. The integral scale of turbulence was determined on the assumption that the structure of random irregularities of the wind field is described by the von Karman model. The domain of applicability of the used method and the accuracy of estimation of turbulence parameters were determined. Turbulence parameters estimated from Stream Line lidar measurement data and from data of a sonic anemometer were compared.

scholarly journals Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer

2017 ◽  
Vol 10 (11) ◽  
pp. 4191-4208 ◽  
Author(s):  
Igor N. Smalikho ◽  
Viktor A. Banakh
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Radial Velocity ◽  
Dissipation Rate ◽  
Stream Line ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Integral Scale ◽  
Coherent Doppler Lidar ◽  
Turbulence Parameters

Abstract. The method and results of lidar studies of spatiotemporal variability of wind turbulence in the atmospheric boundary layer are reported. The measurements were conducted by a Stream Line pulsed coherent Doppler lidar (PCDL) with the use of conical scanning by a probing beam around the vertical axis. Lidar data are used to estimate the kinetic energy of turbulence, turbulent energy dissipation rate, integral scale of turbulence, and momentum fluxes. The dissipation rate was determined from the azimuth structure function of radial velocity within the inertial subrange of turbulence. When estimating the kinetic energy of turbulence from lidar data, we took into account the averaging of radial velocity over the sensing volume. The integral scale of turbulence was determined on the assumption that the structure of random irregularities of the wind field is described by the von Kármán model. The domain of applicability of the used method and the accuracy of the estimation of turbulence parameters were determined. Turbulence parameters estimated from Stream Line lidar measurement data and from data of a sonic anemometer were compared.

Structure of aerosol fields of the atmospheric boundary layer according to aerosol and Doppler lidar data during passage of atmospheric fronts

2017 ◽  
Vol 30 (1) ◽  
pp. 18-32 ◽  
Author(s):  
G. P. Kokhanenko ◽  
Yu. S. Balin ◽  
M. G. Klemasheva ◽  
I. E. Penner ◽  
S. V. Samoilova ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Atmospheric Fronts

Estimate of boundary-layer depth in Nanjing city using aerosol lidar data during 2016–2017 winter

2019 ◽  
Vol 205 ◽  
pp. 67-77 ◽  
Author(s):  
Sihui Fan ◽  
Zhiqiu Gao ◽  
John Kalogiros ◽  
Yubin Li ◽  
Jian Yin ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Lidar Data ◽  
Layer Depth ◽  
Nanjing City

An Analysis of the Performance of the UFAM Pulsed Doppler Lidar for Observing the Boundary Layer

2009 ◽  
Vol 26 (2) ◽  
pp. 240-250 ◽  
Author(s):  
Guy Pearson ◽  
Fay Davies ◽  
Chris Collier
Keyword(s):  
Boundary Layer ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Pulsed Doppler ◽  
Statistical Assessment ◽  
Theoretical Predictions ◽  
Ultrasonic Anemometer ◽  
Remote Measurements ◽  
Versus Range ◽  
Field Deployment

Abstract The performance of the 1.5-μm pulsed Doppler lidar, operated by the U.K. Universities Facility for Atmospheric Measurement (UFAM) over a 51-day continuous and unattended field deployment in southern England, is described and analyzed with a view to demonstrating the capabilities of the system for remote measurements of aerosols and velocities in the boundary layer. A statistical assessment of the vertical pointing mode in terms of the availability and errors in the data versus range is presented. Examples of lidar data are compared to theoretical predictions, radiosondes, the UFAM radar wind profiler, and an ultrasonic anemometer.

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.

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