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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Testing and validation of multi‐lidar scanning strategies for wind energy applications

Abstract Several factors cause lidars to measure different values of turbulence than an anemometer on a tower, including volume averaging, instrument noise and the use of a scanning circle to estimate the wind field. One way to avoid the use of a scanning circle is to deploy multiple scanning lidars and point them toward the same volume in space to collect velocity measurements and extract high‐resolution turbulence information. This paper explores the use of two multi‐lidar scanning strategies, the tri‐Doppler technique and the virtual tower technique, for measuring 3‐D turbulence. In summer 2013, a vertically profiling Leosphere WindCube lidar and three Halo Photonics Streamline lidars were operated at the Southern Great Plains Atmospheric Radiation Measurement site to test these multi‐lidar scanning strategies. During the first half of the field campaign, all three scanning lidars were pointed at approximately the same point in space and a tri‐Doppler analysis was completed to calculate the three‐dimensional wind vector every second. Next, all three scanning lidars were used to build a ‘virtual tower’ above the WindCube lidar. Results indicate that the tri‐Doppler technique measures higher values of horizontal turbulence than the WindCube lidar under stable atmospheric conditions, reduces variance contamination under unstable conditions and can measure high‐resolution profiles of mean wind speed and direction. The virtual tower technique provides adequate turbulence information under stable conditions but cannot capture the full temporal variability of turbulence experienced under unstable conditions because of the time needed to readjust the scans. Copyright © 2016 John Wiley & Sons, Ltd.

Newman, Jennifer F.↗
DOE OSTI2017

Vertically Resolved Retrievals of Aerosol Concentrations and Effective Radii from the DOE Combined HSRL and Raman lidar Measurement Study (CHARMS) Merged High-Spectral-Resolution Lidar-Raman Lidar Data Set

The Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is a key component of DOE’s research strategy to address global climate change. The objective of the ARM Facility is to provide an experimental testbed for studying important atmospheric effects, particularly cloud and aerosol processes, and testing parameterizations of these processes for use in climate change models. ARM observational sites include a broad array of instruments to characterize cloud and aerosol properties, atmospheric state variables, and incoming and outgoing radiation. Advanced lidar systems provide valuable and unique information about the atmospheric column. We examine the potential for providing vertically resolved information about aerosol size and concentration with a combination of two such lidar systems, the Raman lidar and the High-Spectral-Resolution Lidar (HSRL). Currently ARM has a limited ability to remotely measure profiles of aerosol optical and microphysical properties on an operational basis. Although the Southern Great Plains (SGP), Eastern North Atlantic (ENA), and North Slope of Alaska (NSA, Oliktok Point location only) Raman lidars provide profiles of aerosol backscatter and extinction at 355 nm, additional information is required to better characterize the impacts of aerosols on radiation and clouds. Recent advances in lidar retrieval theory and algorithm development (Müller et al. 1999, 2014; Chemyakin et al. 2014) demonstrate that multi-wavelength lidar retrieval algorithms that use measurements of aerosol extinction at both 355 and 532 nm and backscatter at 355, 532, and 1064 nm have the potential to constrain both the aerosol optical (e.g., complex index of refraction, scattering, etc.) and microphysical properties (e.g., effective radius, concentration). This advancement greatly increases the utility of the remote-sensed aerosol observations. Based on this work, the HSRL group at the National Aeronautics and Space Administration (NASA)’s Langley Research Center (LaRC) developed automated algorithms for retrieving aerosol optical and microphysical properties, demonstrated these retrievals using data from the unique NASA/LaRC airborne multi-wavelength HSRL-2 system, and validated these retrievals using coincident airborne in situ data (Müller et al. 2014; Sawamura et al. 2017). We report here on the use of ARM instrumentation to gather a multi-wavelength ground-based lidar data set to test such retrievals. Continuous (24/7) operation of the co-located SGP Raman lidar and University of Wisconsin (UW) HSRL during the 10-week Combined HSRL and Raman lidar Measurement Study (CHARMS) (mid-July through September 2015) allowed the acquisition of a unique, multi-wavelength ground-based lidar data set. The ARM SGP Raman lidar (RLID) measured profiles of aerosol backscatter, extinction, and depolarization at 355 nm and profiles of water vapor mixing ratio and temperature. The UW HSRL simultaneously measured profiles of aerosol backscatter, extinction and depolarization at 532 nm and aerosol backscatter at 1064 nm. This RLID+HSRL combination provided a test data set to investigate these more advanced aerosol retrieval techniques with the potential to greatly improve aerosol characterization in the lower atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Wind Farm Wakes and Farm-to-Farm Interactions: Lidar and Wind Tunnel Tests

Recent experimental and numerical evidence has shown that the cumulative wake generated from the overlapping of multiple wakes within a wind farm could reduce power performance and enhance fatigue loads of wind turbines installed in neighboring downstream wind farms and may also extend up to distances one order of magnitude larger than those typically considered for intra-farm wake interactions. Similar to individual wind turbine wakes, wind farm wakes have a velocity deficit and added turbulence intensity, both affected by the turbine rotor thrust forces and the incoming turbulence intensity. Therefore, the evolution of wind farm wakes will vary for different operational and atmospheric conditions. In this paper, lidar measurements collected during the American WAKE experimeNt (AWAKEN) and wind tunnel tests of wind farms reproduced by porous disks are leveraged to investigate wind farm wakes.

17 WIND ENERGY↗
DOE OSTI2017

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

17 WIND ENERGY↗
DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.

17 WIND ENERGY↗
NASA NTRS2015

Lidar-Radiometer Inversion Code (LIRIC) for the Retrieval of Vertical Aerosol Properties from Combined Lidar Radiometer Data: Development and Distribution in EARLINET

This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code)algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar radiometric input data we use measurements from European Aerosol Re-search Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data by the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height-dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode. The LIRIC software package was implemented and tested at a number of EARLINET stations. Inter-comparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLNET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.

Chaikovsky, A.↗
DOE OSTI2015

Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap between Lidar Systems and Wind Turbines: Preprint

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited, or can even result in harmful control action. An online analysis of the lidar and turbine data are necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross correlation to determine the time shift between both signals. Further, initial results from an ongoing campaign in which this system was employed for providing lidar preview for feed-forward pitch control are presented.

17 WIND ENERGY↗
DOE OSTI2018

Polar Liquid Cloud Base Detection Algorithms for High Spectral Resolution or Micropulse Lidar Data

Liquid layers in clouds affect their microphysical processes, as well as the atmospheric energy budget. However, explicit liquid cloud base height (LCBH) detection algorithms using ground-based instruments (e.g., lidars) are currently absent in the literature. Most studies use either fixed lidar parameter thresholds or cloud base height data products that do not distinguish between ice and liquid, all of which might introduce inconsistencies and errors in the resolved LCBHs. In this paper, two LCBH detection algorithms are presented; the first algorithm uses the high spectral resolution lidar (HSRL) data, and yields satisfactory results. The second algorithm incorporates micropulse lidar (MPL) data for the LCBH detection. A 1-year long comparison of data gathered at Barrow, Alaska, and McMurdo Station, Antarctica, which includes other cloud base detection methodologies (ceilometer, MPL value-added product cloud base height, LCBHs detected using a fixed MPL depolarization threshold) emphasize the merits of the MPL algorithm.

Silber, Israel↗
NASA NTRS1991

Aerosol backscatter measurements at 10.6 microns with airborne and ground-based CO2 Doppler lidars over the Colorado High Plains. I - Lidar intercomparison

An airborne continuous-wave (CW) focused CO2 Doppler lidar and a ground-based pulsed CO2 Doppler lidar were to obtain seven pairs of comparative measurements of tropospheric aerosol backscatter profiles at 10.6-micron wavelength, near Denver, Colorado, during a 20-day period in July 1982. In regions of uniform backscatter, the two lidars show good agreement, with differences usually less than about 50 percent near 8-km altitude and less than a factor of 2 or 3 elsewhere but with the pulsed lidar often lower than the CW lidar. Near sharp backscatter gradients, the two lidars show poorer agreement, with the pulsed lidar usually higher than the CW lidar. Most discrepancies arise from a combination of atmospheric factors and instrument factors, particularly small-scale areal and temporal backscatter heterogeneity above the planetary boundary layer, unusual large-scale vertical backscatter structure in the upper troposphere and lower stratosphere, and differences in the spatial resolution, detection threshold, and noise estimation for the two lidars.

Bowdle, David A.↗
DOE OSTI2019

Validation of the first LiDAR wind resource assessment buoy system offshore the Northeast United States

The US offshore wind industry is maturing with several projects in various stages of development. These projects require site wind and environmental data before and during operation. Conventional techniques such as fixed-bottom meteorological towers present economical and permitting challenges for the US. Floating Light Detection and Ranging (LiDAR) buoys offer significant advantages including reduced costs, less permitting, and reusability. This paper presents the validation of the first floating LiDAR buoy in Northeast US waters. The buoy, named DeepCLiDAR, includes a LiDAR, ecological monitoring sensors, and metocean sensors. A three-phase LiDAR validation plan was executed, and its results are presented. The objective of the validation plan was to verify the accuracy of measurements made by the LiDAR buoy in wave environments against an unmoving reference wind measurement. Due to a lack of reference met masts, the use of a LiDAR on land as a baseline reference was implemented for validation. Comparison to a reference LiDAR instead of a traditional meteorological tower was a unique approach required in the Northeast US waters due to the absence of a reference fixed-bottom meteorological tower in the region at the time of this study. The testing included a comparison of wind speed measurements made by the buoy deployed 15 km offshore from the mainland and a land-based reference LiDAR located on a nearby island. This paper presents the methodology and results of this program, which indicate favorable agreement. This was the first such validation program in the Northeast USA which is now seeing rapid development of offshore wind.

17 WIND ENERGY↗
DOE OSTI2016

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Characterizing NWP Model Errors Using Doppler-Lidar Measurements of Recurrent Regional Diurnal Flows: Marine-Air Intrusions into the Columbia-River Basin

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon-Washington, where these winds are an important windenergy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia-River Basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time-height cross sections of wind speed identified eight days when the diurnal-flow cycle, (peak wind speeds at midnight, afternoon minima) was obvious and strong. Eight-day composite time-height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP-HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s-1 . Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field-measurement programs can be designed to find and address the sources of these NWP errors.

Banta, Robert M.↗
DOE OSTI2016

Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations

Since turbulence measurements from Doppler lidars are being increasingly used within wind energy and boundary-layer meteorology, it is important to assess and improve the accuracy of these observations. While turbulent quantities are measured by Doppler lidars in several different ways, the simplest and most frequently used statistic is vertical velocity variance ( w ' 2 ) from zenith stares. But, the competing effects of signal noise and resolution volume limitations, which respectively increase and decrease w ' 2 , reduce the accuracy of these measurements. Herein, an established method that utilises the autocovariance of the signal to remove noise is evaluated and its skill in correcting for volume-averaging effects in the calculation of w ' 2 is also assessed. In addition, this autocovariance technique is further refined by defining the amount of lag time to use for the most accurate estimates of w ' 2 . And through comparison of observations from two Doppler lidars and sonic anemometers on a 300 m tower, the autocovariance technique is shown to generally improve estimates of w ' 2 . After the autocovariance technique is applied, values of w ' 2 from the Doppler lidars are generally in close agreement ( R 2 ≈ 0.95 -0.98) with those calculated from sonic anemometer measurements.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Backscatter Modeling at 2.1 Micron Wavelength for Space-Based and Airborne Lidars Using Aerosol Physico-Chemical and Lidar Datasets

Space-based and airborne coherent Doppler lidars designed for measuring global tropospheric wind profiles in cloud-free air rely on backscatter, beta from aerosols acting as passive wind tracers. Aerosol beta distribution in the vertical can vary over as much as 5-6 orders of magnitude. Thus, the design of a wave length-specific, space-borne or airborne lidar must account for the magnitude of 8 in the region or features of interest. The SPAce Readiness Coherent Lidar Experiment under development by the National Aeronautics and Space Administration (NASA) and scheduled for launch on the Space Shuttle in 2001, will demonstrate wind measurements from space using a solid-state 2 micrometer coherent Doppler lidar. Consequently, there is a critical need to understand variability of aerosol beta at 2.1 micrometers, to evaluate signal detection under varying aerosol loading conditions. Although few direct measurements of beta at 2.1 micrometers exist, extensive datasets, including climatologies in widely-separated locations, do exist for other wavelengths based on CO2 and Nd:YAG lidars. Datasets also exist for the associated microphysical and chemical properties. An example of a multi-parametric dataset is that of the NASA GLObal Backscatter Experiment (GLOBE) in 1990 in which aerosol chemistry and size distributions were measured concurrently with multi-wavelength lidar backscatter observations. More recently, continuous-wave (CW) lidar backscatter measurements at mid-infrared wavelengths have been made during the Multicenter Airborne Coherent Atmospheric Wind Sensor (MACAWS) experiment in 1995. Using Lorenz-Mie theory, these datasets have been used to develop a method to convert lidar backscatter to the 2.1 micrometer wavelength. This paper presents comparison of modeled backscatter at wavelengths for which backscatter measurements exist including converted beta (sub 2.1).

Srivastava, V.↗
NASA NTRSDate not supplied

An Optimal Estimation Aerosol Retrieval Algorithm for Lidar-only and Combined Lidar Polarimeter Remote Sensors

Global measurements of aerosol vertical profile, composition, concentration, and size distribution are very important due to aerosol impacts on air quality, climate, clouds, and ocean ecosystems. We have developed a lidar-only and combined lidar and polarimeter algorithm to retrieve vertically-resolved profiles of aerosol microphysical properties. The new retrieval system is modular in design. It consists of three modules, a vertically-resolved aerosol profile retrieval module for lidar data, an aerosol and cloud/ocean retrieval module for polarimeter data, and a combined retrieval module for both lidar and polarimeter measurements. In addition to performing optimal estimation retrievals on various data sources, we have designed the system so that it can be used to carry out aerosol retrieval performance trade studies for various lidar and polarimeter configurations. For example, the retrieval system can take inputs from attenuated backscatter lidar at two wavelengths (e.g. CALIPSO like instrument), aerosol backscattering measured at two wavelengths and and aerosol extinction measured at one wavelength (HSRL-1), or aerosol backscattering measured at three wavelengths and aerosol extinction measured at two wavelengths (HSRL-1) (HSRL-2). We have applied the lidar-only algorithm to both simulated data and various field campaign data (DISCOVER-AQ, CHARMS, TCAP, and ORACLES). For polarimeter-only modules, we have done the same for SABOR, TCAP, NAAMES, and ORACLES data. We have applied the combined retrieval algorithm to ORACLES data. The simulated retrieval studies show that the combined lidar+polarimeter retrieval provides much higher information content relative to their individual counterparts for retrieving effective radius, particle concentrations, and absorption properties.

Xu Liu↗