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This dataset contains raw data from the ANL scanning Doppler lidar, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains raw data from the UND scanning Doppler lidar, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains raw data from the UND scanning Doppler lidar, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains raw data from the UND scanning Doppler lidar, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
Preliminary results of CS lidar measurements over the Pacific Ocean using two CO2 Doppler lidars operating at 9.1 and 10.6 microns are presented. SNR calculations during real-time and postprocessing were performed using a multistep signal search and signal power estimation algorithm optimized for weak signal conditions. Typical aerosol structure observed in the troposphere was found to consist of a boundary layer, a cloud-pumped layer, and a clean background layer; backscatter from cloud-free air was found to vary over three to four orders of magnitude or more.
The Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) project has developed a suite of prototype sensors for enabling autonomous and safe precision land- ing of robotic or crewed vehicles on solid solar bodies under varying terrain lighting condi- tions. The sensors include a Lidar-based Hazard Detection System (HDS), a multipurpose Navigation Doppler Lidar (NDL), and a long-range Laser Altimeter (LAlt). Preparation for terrestrial ight testing of ALHAT onboard the Morpheus free- ying, rocket-propelled ight test vehicle has been in progress since 2012, with ight tests over a lunar-like ter- rain eld occurring in Spring 2014. Signi cant work e orts within both the ALHAT and Morpheus projects has been required in the preparation of the sensors, vehicle, and test facilities for interfacing, integrating and verifying overall system performance to ensure readiness for ight testing. The ALHAT sensors have undergone numerous stand-alone sensor tests, simulations, and calibrations, along with integrated-system tests in special- ized gantries, trucks, helicopters and xed-wing aircraft. A lunar-like terrain environment was constructed for ALHAT system testing during Morpheus ights, and vibration and thermal testing of the ALHAT sensors was performed based on Morpheus ights prior to ALHAT integration. High- delity simulations were implemented to gain insight into integrated ALHAT sensors and Morpheus GN&C system performance, and command and telemetry interfacing and functional testing was conducted once the ALHAT sensors and electronics were integrated onto Morpheus. This paper captures some of the details and lessons learned in the planning, preparation and integration of the individual ALHAT sen- sors, the vehicle, and the test environment that led up to the joint ight tests.
These data include scanning Doppler lidar measurements from the deployment of the ARM HALO XR+ (s/n 236) at the A5 site. The measurements include calibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate. The data are provided in NetCDF.
These data include scanning Doppler lidar measurements from the deployment of the LLNL HALO XR+ (s/n 190) at the A5 site. The measurements include calibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate. The data are provided in NetCDF.
This dataset contains processed, standardized data from the UND scanning Doppler lidar at WFIP3's BARG site, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains processed, standardized data from the UND scanning Doppler lidar at WFIP3's BARG site, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains standardized data from the PNNL scanning Doppler lidar (S/N 184), consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
Several examples of applications of coherent CO2 Doppler lidar systems are summarized to illustrate the potential of these systems. The applications discussed include the use of continuous-wave systems for detecting and tracking aircraft wake vortices, transverse velocity measurements, and measurements of mass flow rates of high stack emissions. The use of pulsed coherent lidars is illustrated by applications involving the measurement of thunderstorm gust fronts, the measurement of wind profiles, and clear air turbulence detection. Following a summary of previous efforts, some current programs are reviewed. These include investigations into two-dimensional wind field measurements, atmospheric backscatter measurements, transverse velocity measurements, and the feasibility of space operations.
From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long term measurements of hub-height wind speed in the marine environment. In this study, we performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) model, to see if simulated hub height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a standalone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled-Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 08 May 2015 and the cold frontal case of 25 Mar 2016, but not the nor-easter of 18 Jan 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 Mar 2016 but worsening them for 08 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the VA buoy, largely due to greater self-consistency between wind and wave fields.
This dataset contains data from the CLAMPS 1 Halo Photonics StreamLine Doppler Lidar at the Rolling Meadows Golf Course site. CLAMPS 1 deployed during fall-winter 2022 (10/3/22-12/27/23) and summer-fall 2023 (6/16/23-10/1/23).
The 10-min wind statistics from ground-based Doppler lidar at site A1 were calculated using a modified version of the Sathe et al., 2015 paper in the references.
Abstract. Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random errors. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume and spectral noise due primarily to limited photon capture. These two uncertainties, especially that due to solid interference, can be reduced with high-fidelity retrieval techniques (i.e., including both quality assurance/quality control and subsequent parameter estimation). Our work compares three such techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed nonideal spectral features while keeping data availability high. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an adjacent meteorological tower within the sampling volume permit experimental validation of the instantaneous velocity uncertainty remaining after retrieval that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3 m s−1, or a 1 %–22 % improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5 % higher overall data availability, while the machine learning offers a faster runtime (i.e., ∼ 1 s to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The retrieval techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty in the output of a high-fidelity lidar retrieval technique using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three techniques rather than one.
A design concept for a 100 mJ/0.5 m 2 micron coherent Doppler lidar demonstration mission is summarized. Earth System Science Pathfinder is described in detail.
The International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week in July 2018 to demonstrate unmanned aircraft systems' (UASs) capabilities in sampling the atmospheric boundary layer. This week-long experiment was called the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. Numerous remotely piloted aircraft and ground-based instruments were deployed with the objective of capturing meso- and microscale phenomena in the atmospheric boundary layer. The University of Oklahoma deployed one Halo Streamline lidar, and the University of Colorado Boulder deployed two WindCube lidars. In this paper, we use data collected from these Doppler lidars to estimate turbulence dissipation rate throughout the campaign. We observe large temporal variability of turbulence dissipation close to the surface with the WindCube lidars that is not detected by the Halo Streamline. However, the Halo lidar enables estimating dissipation rate within the whole boundary layer, where a diurnal variability emerges. We also find a higher correspondence in turbulence dissipation between the WindCube lidars, which are not co-located, compared to the Halo and WindCube lidar that are co-located, suggesting a significant influence of measurement volume on the retrieved values of dissipation rate.