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1. LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework

   https://doi.org/10.5194/amt-2020-227  

   Letizia, Stefano ; Zhan, Lu ; Iungo, Giacomo Valerio ( March 2021 , Atmospheric measurement techniques)

   null (Ed.)

   A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of the velocity statistical moments is proposed. LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional space, and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning lidars. LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for the optimal design of lidar experiments and efficient application of LiSBOA for the postprocessing of lidar measurements. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the lidar scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme. LiSBOA is assessed against a numerical data set generated using the virtual lidar technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind lidar are retrieved through LiSBOA, and then the estimated statistics are compared with those of the original LES data set, showing a maximum error of about 4 % for both mean velocity and turbulence intensity. 

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2. Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality

   https://doi.org/10.1002/we.2865  

   Puccioni, Matteo ; Moss, Coleman ; Iungo, Giacomo Valerio ( October 2023 , Wind Energy)

   Over the last decades, pulsed light detection and ranging (LiDAR) anemometry has gained growing attention in probing the marine atmospheric boundary layer (MABL) due to its ease of use combined with compelling spatio‐temporal resolution. Among several scanning strategies, fixed scans represent the most prominent choice when high‐frequency resolution is required; however, no information is provided about the spatial heterogeneity of the wind field. On the other hand, volumetric scans allow for the characterization of the spatial variability of the wind field with much lower temporal resolution than fixed scans. In this work, the recently developed “LiDAR Statistical Barnes Objective Analysis” (LiSBOA) algorithm for the optimal design of LiDAR scans and retrieval of wind velocity statistics is tailored for applications in the MABL. The LiDAR data, collected during a recent experimental campaign over Lake Lavon in Texas, show a good consistency of mean velocity profiles between fixed and LiSBOA‐interpolated volumetric data, thus further encouraging the use of coupled fixed and volumetric scans for simultaneous characterizations of wind turbulence statistics along the vertical direction and volumetric heterogeneity of the wind field.

    

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3. LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes

   https://doi.org/10.5194/amt-14-2095-2021  

   Letizia, Stefano ; Zhan, Lu ; Iungo, Giacomo Valerio ( January 2021 , Atmospheric Measurement Techniques)

   null (Ed.)

   Abstract. The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in Letizia et al. (2021), is a procedure for the optimal design of lidar scans and calculations over a Cartesian grid of the statistical moments of the velocity field. Lidar data collected during a field campaign conducted at a wind farm in complex terrain are analyzed through LiSBOA for two different tests. For both case studies, LiSBOA is leveraged for the optimization of the azimuthal step of the lidar and the retrieval of the mean equivalent velocity and turbulence intensity fields. In the first case, the wake velocity statistics of four utility-scale turbines are reconstructed on a 3D grid, showing LiSBOA's ability to capture complex flow features, such as high-speed jets around the nacelle and the wake turbulent-shear layers. For the second case, the statistics of the wakes generated by four interacting turbines are calculated over a 2D Cartesian grid and compared to the measurements provided by the nacelle-mounted anemometers. Maximum discrepancies, as low as 3 % for the mean velocity (with respect to the free stream velocity) and turbulence intensity (in absolute terms), endorse the application of LiSBOA for lidar-based wind resource assessment and diagnostic surveys for wind farms. 

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4. Handheld lidar as a tool for characterizing wood‐rich river corridors

   https://doi.org/10.1002/rra.4239  

   Marshall, Anna ; Morrison, Ryan R. ; Jones, Brady ; Triantafillou, Shayla ; Wohl, Ellen ( January 2024 , River Research and Applications)

   Wood accumulations influence geomorphic, hydraulic, and ecologic functions within a river corridor, but characterizing these accumulations presents challenges across a range of field and remote sensing methodologies. We evaluate the ability of handheld lidar scanners, specifically lidar‐scanning capabilities of a fourth‐generation iPad Pro, to collect three‐dimensional wood accumulation data, which can be used to inform measurements of wood volume, porosity, complexity, and roughness. We discuss the potential and limitations of this novel methodology for river research and management. We found that handheld lidar presents a cost‐effective input for data‐processing workflows that field measurements of wood accumulation dimensions cannot as easily replicate including (1) a user‐friendly means of data collection and visualization; (2) accurate comparisons of wood volume over time; (3) integration into workflows to measure porosity parameters; and (4) potential use in informing hydraulic and morphodynamic models. Consideration of study area constraints and intended use of scans are prerequisites to using handheld lidar as an effective tool. We identified some specific limitations of using handheld lidar scanners in wood‐rich river corridors, including (1) scanners perform poorly when wood is under water or surrounded by dense vegetation; (2) scanners require physical access to areas of interest at distances less than 5 m; (3) scans need to be manually georeferenced; and (4) scans require manual measurements for any dimensional data, which still have associated user time and error. Handheld lidar as a scientific tool is rapidly developing and there is substantial room for expansion of applications, utilization, and advances in the use of this tool in river research and management.

    

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5. Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

   https://doi.org/10.1063/5.0070094  

   Iungo, G. V. ; Maulik, R. ; Renganathan, S. A. ; Letizia, S. ( March 2022 , Journal of Renewable and Sustainable Energy)

   Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction. 

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