Doppler Lidar Estimation of Mixing Height Using Turbulence, Shear, and Aerosol 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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Assimilating Coherent Doppler Lidar Measurements into a Model of the Atmospheric Boundary Layer. Part I: Algorithm Development and Sensitivity to Measurement Error

Journal of Atmospheric and Oceanic Technology ◽

10.1175/1520-0426(2004)021<1328:acdlmi>2.0.co;2 ◽

2004 ◽

Vol 21 (9) ◽

pp. 1328-1345 ◽

Cited By ~ 15

Author(s):

Rob K. Newsom ◽

Robert M. Banta

Keyword(s):

Boundary Layer ◽

Measurement Error ◽

Atmospheric Boundary Layer ◽

Doppler Lidar ◽

Algorithm Development ◽

Coherent Doppler Lidar ◽

Lidar Measurements

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

10.5194/amt-2017-140 ◽

2017 ◽

Cited By ~ 1

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.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-10-4191-2017 ◽

2017 ◽

Vol 10 (11) ◽

pp. 4191-4208 ◽

Cited By ~ 26

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.

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Assimilating Coherent Doppler Lidar Measurements into a Model of the Atmospheric Boundary Layer. Part II: Sensitivity Analyses

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-1676.1 ◽

2004 ◽

Vol 21 (12) ◽

pp. 1809-1824 ◽

Cited By ~ 6

Author(s):

Rob K. Newsom ◽

Robert M. Banta

Keyword(s):

Boundary Layer ◽

Temperature Profile ◽

Potential Temperature ◽

Eddy Diffusivity ◽

Grid Resolution ◽

Sensitivity Analyses ◽

Doppler Lidar ◽

Sonic Anemometers ◽

Lidar Measurements ◽

Virtual Potential Temperature

Abstract A series of trials are performed to evaluate the sensitivity of a 4DVAR algorithm for retrieval of microscale wind and temperature fields from single-Doppler lidar data. These trials use actual Doppler lidar measurements to examine the sensitivity of the retrievals to changes in 1) the prescribed eddy diffusivity profile, 2) the first-guess or base-state virtual potential temperature profile, 3) the phase and duration of the assimilation period, and 4) the grid resolution. The retrieved fields are well correlated among trials over a reasonable range of variation in the eddy diffusivity coefficients. However, the retrievals are quite sensitive to changes in the gradients of the first-guess or base-state virtual potential temperature profile, and to changes in the phase (start time) and duration of the assimilation period. Retrievals using different grid resolutions exhibit similar larger-scale structure, but differ considerably in the smaller scales. Increasing the grid resolution significantly improved the fit to the radial velocity measurements, improved the convergence rate, and produced variances and fluxes that were in better agreement with tower-based sonic anemometers. Horizontally averaged variance and heat flux profiles derived from the final time steps of all the retrievals are similar to typical large-eddy-simulation (LES) results for the convective boundary layer. However, all retrieved statistics show significant nonstationarity because fluctuations in the initial state tend to be confined within the boundaries of the scan.

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

Remote Sensing ◽

10.3390/rs13132433 ◽

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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Improved mixing height monitoring through a combination of lidar and radon measurements

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-5-6835-2012 ◽

2012 ◽

Vol 5 (5) ◽

pp. 6835-6866 ◽

Cited By ~ 2

Author(s):

A. D. Griffiths ◽

S. D. Parkes ◽

S. D. Chambers ◽

M. F. McCabe ◽

A. G. Williams

Keyword(s):

Boundary Layer ◽

Time Series ◽

Radon Concentration ◽

Quantitative Measure ◽

Potential Method ◽

Length Scale ◽

Mixing Length ◽

Mixing Height ◽

Lidar Measurements ◽

Radon Measurements

Abstract. Surface-based radon (222Rn) measurements can be combined with lidar backscatter to obtain a higher quality time series of mixing height within the Planetary Boundary-Layer (PBL) than is possible from lidar alone, and a more quantitative measure of mixing height than is possible from only radon. The lidar measurements benefit because even when aerosol layers are detected, reliably attributing the mixing height to the correct layer presents a challenge. By combining lidar with a mixing length scale derived from a time series of radon concentration, automated and robust attribution is possible during the morning transition. Radon measurements also provide mixing information during the night and with the addition of lidar these measurements become insensitive to night-to-night changes in radon emissions. After calibration with lidar, the radon-derived equivalent mixing height agrees with other measures of mixing on daily and hourly time scales and is a potential method for studying intermittent mixing in nocturnal boundary layers.

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