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Special Issue "Remote Sensing of Atmospheric Conditions for Wind Energy Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: closed (1 November 2018)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Dr. Charlotte Bay Hasager

Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
Website | E-Mail
Interests: marine boundary-layer meteorology; satellite remote sensing; offshore wind energy; wind farm wakes; land- and sea-surface roughness; wind resources; ground-based remote sensing
Guest Editor
Dr. Mikael Sjöholm

Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
Website | E-Mail
Interests: Ground-based remote sensing of wind, rain, aerosols, temperature, and gases; scanning wind lidar technology; multiple-beam systems; wind-energy and boundary-layer meteorology; remote sensing in wind tunnels; inflows and wakes

Special Issue Information

Dear Colleagues,

Wind energy is the renewable energy source that contributes the most to the electricity generation worldwide. The need to further understand and efficiently use available wind resources is the key motivation for research in wind energy. The levelized cost of energy from wind power is competitive with that of the conventional energy sources at wind-favorable land sites, while efforts are made to lower the cost of wind energy at offshore, complex, and forested areas. The wake effect within and between wind farms and wind-power forecasting are areas with increasing importance because of the need to accurately predict wind power. There is, therefore, a need for reliable, robust, and accurate measurements and datasets to further improve our understanding of the physical conditions in which wind turbines and wind farms operate and for flow model evaluation.

Nowadays, remote sensing observations are used widely in wind energy applications. During the last couple of years, remote sensing technologies for wind have been improved, both in terms of accuracy and costs. Combined measurement infrastructures, such as that of WindScanner.eu, and new advancements for the measurement of atmospheric turbulence and the wind turbine power performance, turbine wakes, and for improvement of the turbine control, are being progressively achieved. Commercial acceptance of lidars, including floating/buoy lidars, for wind resource assessment, is also on-going. Based on airborne lidar, high-resolution land surface maps are retrieved in forested and complex terrain and provide new valuable inputs to micro- and meso-scale modeling. Surface roughness, terrain elevation, albedo, vegetation parameters, and land- and sea surface temperatures are assessed based on Earth Observation (EO) data and used as input for flow modeling of wind resources (wind atlas) and for the forecasting of wind power at short temporal scales. EO microwave data are used for offshore wind field mapping and applied for wind resource estimation, wind park wake effect, and long-term wind climate conditions.

We welcome submission on all aspects of remote sensing for wind energy and atmospheric boundary-layer application. This includes the above-mentioned topics and those listed below.

  • Lidar, sodar, radar, and other ground-based remote sensing
  • EO data from SAR, scatterometer and passive microwaves
  • EO-based surface roughness and terrain elevation
  • Remote sensing contribution to wind energy, wind resources, boundary-layer, and wind-power meteorology
  • Remote sensing in atmospheric turbulence and wind-flow modeling
  • Remote sensing in wind tunnels
  • Remote sensing for wake of wind turbines and wind farms
  • Remote sensing application in forecasting of winds and wind power
  • Remote sensing for control of wind turbines and wind farms
  • Remote sensing for the wind turbine blade erosion environment
  • Theoretical and experimental issues within remote sensing for wind energy

We would like to invite you to submit articles about your recent research. Review articles covering one or more of these topics are also welcome.

Dr. Charlotte Hasager
Dr. Mikael  Sjöholm
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Wind energy
  • Remote sensing
  • Wind tunnel
  • Boundary-layer meteorology
  • Inflow and wakes

Published Papers (16 papers)

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Editorial

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Open AccessEditorial
Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”
Remote Sens. 2019, 11(7), 781; https://doi.org/10.3390/rs11070781
Received: 27 March 2019 / Accepted: 28 March 2019 / Published: 1 April 2019
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Abstract
This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine [...] Read more.
This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented. Full article

Research

Jump to: Editorial, Other

Open AccessArticle
Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar
Remote Sens. 2018, 10(12), 2037; https://doi.org/10.3390/rs10122037
Received: 30 October 2018 / Revised: 26 November 2018 / Accepted: 11 December 2018 / Published: 14 December 2018
Cited by 1 | PDF Full-text (3057 KB) | HTML Full-text | XML Full-text
Abstract
This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a [...] Read more.
This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a ZephIR™300 continuous-wave focusable Doppler lidar and does not require access to individual line-of-sight radial-wind information along the scanning pattern. The method combines a software-based velocity-azimuth-display and motion simulator and a statistical recursive procedure to estimate the horizontal wind speed standard deviation—as a well as the turbulence intensity—due to floating lidar buoy motion. The motion-induced error is estimated from the simulator’s side by using basic motional parameters, namely, roll/pitch angular amplitude and period of the floating lidar buoy, as well as reference wind speed and direction measurements at the study height. The impact of buoy motion on the retrieved wind speed and related standard deviation is compared against a reference sonic anemometer and a reference fixed lidar over a 60-day period at the IJmuiden test site (the Netherlands). Individual case examples and an analysis of the overall campaign are presented. After the correction, the mean deviation in the horizontal wind speed standard deviation between the reference and the floating lidar was improved by about 70%, from 0.14 m/s (uncorrected) to −0.04 m/s (corrected), which makes evident the goodness of the method. Equivalently, the error on the estimated turbulence intensity (3–20 m/s range) reduced from 38% (uncorrected) to 4% (corrected). Full article
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Open AccessArticle
On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts
Remote Sens. 2018, 10(11), 1701; https://doi.org/10.3390/rs10111701
Received: 30 August 2018 / Revised: 22 October 2018 / Accepted: 24 October 2018 / Published: 29 October 2018
Cited by 2 | PDF Full-text (5785 KB) | HTML Full-text | XML Full-text
Abstract
Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of [...] Read more.
Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper. Full article
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Open AccessArticle
The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
Remote Sens. 2018, 10(10), 1620; https://doi.org/10.3390/rs10101620
Received: 31 August 2018 / Revised: 4 October 2018 / Accepted: 6 October 2018 / Published: 12 October 2018
Cited by 1 | PDF Full-text (2565 KB) | HTML Full-text | XML Full-text
Abstract
This article presents the Ferry Lidar Experiment, which is one of the NEWA Experiments, a set of unique flow experiments conducted as part of the New European Wind Atlas (NEWA) project. These experiments have been prepared and conducted to create adequate datasets for [...] Read more.
This article presents the Ferry Lidar Experiment, which is one of the NEWA Experiments, a set of unique flow experiments conducted as part of the New European Wind Atlas (NEWA) project. These experiments have been prepared and conducted to create adequate datasets for mesoscale and microscale model validation. For the Ferry Lidar Experiment a Doppler lidar instrument was placed on a ferry connecting Kiel and Klaipeda in the Southern Baltic Sea from February to June 2017. A comprehensive set of all relevant motions was recorded together with the lidar data and processed in order to obtain and provide corrected wind time series. Due to the existence of the motion effects, the obtained data are essentially different from typical on-site data used for wind resource assessments in the wind industry. First comparisons show that they can be well related to mapped wind trajectories from the output of a numerical weather prediction model showing a reasonable correlation. More detailed validation studies are planned for the future. Full article
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Open AccessArticle
Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
Remote Sens. 2018, 10(9), 1465; https://doi.org/10.3390/rs10091465
Received: 2 July 2018 / Revised: 4 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
Cited by 1 | PDF Full-text (8603 KB) | HTML Full-text | XML Full-text
Abstract
In complex terrain, ground-based lidar wind speed measurements sometimes show noticeable differences compared to measurements made with in-situ sensors mounted on meteorological masts. These differences are mostly caused by the inhomogeneities of the flow field and the applied reconstruction methods. This study investigates [...] Read more.
In complex terrain, ground-based lidar wind speed measurements sometimes show noticeable differences compared to measurements made with in-situ sensors mounted on meteorological masts. These differences are mostly caused by the inhomogeneities of the flow field and the applied reconstruction methods. This study investigates three different methods to optimize the reconstruction algorithm in order to improve the agreement between lidar measurements and data from sensors on meteorological masts. The methods include a typical velocity azimuth display (VAD) method, a leave-one-out cross-validation method, and a linear model which takes into account the gradients of the wind velocity components. In addition, further aspects such as the influence of the half opening angle of the scanning cone and the scan duration are considered. The measurements were carried out with two different lidar systems, that measured simultaneously. The reference was a 100 m high meteorological mast. The measurements took place in complex terrain characterized by a 150 m high escarpment. The results from the individual methods are quantitatively compared with the measurements of the cup anemometer mounted on the meteorological mast by means of the three parameters of a linear regression (slope, offset, R 2 ) and the width of the 5th–95th quantile. The results show that expanding the half angle of the scanning cone from 20 to 55 reduces the offset by a factor of 14.9, but reducing the scan duration does not have an observable benefit. The linear method has the lowest uncertainty and the best agreement with the reference data (i.e., lowest offset and scatter) of all of the methods that were investigated. Full article
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Open AccessArticle
Investigation of the Fetch Effect Using Onshore and Offshore Vertical LiDAR Devices
Remote Sens. 2018, 10(9), 1408; https://doi.org/10.3390/rs10091408
Received: 12 August 2018 / Revised: 31 August 2018 / Accepted: 2 September 2018 / Published: 5 September 2018
Cited by 2 | PDF Full-text (9232 KB) | HTML Full-text | XML Full-text
Abstract
An offshore wind measurement campaign using vertical light detection and ranging (LiDAR) devices was conducted at the Hazaki Oceanographic Research Station (HORS) as part of an investigation into determining the optimal distance from the coast for a nearshore wind farm from a meteorological [...] Read more.
An offshore wind measurement campaign using vertical light detection and ranging (LiDAR) devices was conducted at the Hazaki Oceanographic Research Station (HORS) as part of an investigation into determining the optimal distance from the coast for a nearshore wind farm from a meteorological perspective. The research platform was a 427 m long pier located on a rectilinear coastline on the Pacific coast of the central Honshu Island in Japan. The relationship between the ratios of the increase of wind speed near the surface and fetch length within 5 km of the coast was analyzed via LiDAR observations taken at heights from 40 to 200 m. The results showed that the speed of the coastal wind blowing from land to sea gradually increased as the fetch length increased, by approximately 15–20% at 50 m above sea level around a fetch length of 2 km. Moreover, empirical equations were derived by applying the power law to the relationship between the increase of wind speed and fetch lengths at 1–5 km, as obtained from the LiDAR measurements. It was also found that the wind speed increase at a 2 km fetch length was equivalent to the effect of a 50–90 m vertical height increase on the coast in this region. Full article
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Open AccessArticle
Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop
Remote Sens. 2018, 10(6), 863; https://doi.org/10.3390/rs10060863
Received: 20 April 2018 / Revised: 21 May 2018 / Accepted: 30 May 2018 / Published: 1 June 2018
Cited by 3 | PDF Full-text (964 KB) | HTML Full-text | XML Full-text
Abstract
IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop “Optimizing Lidar Design for Wind Energy Applications” was held in July 2016 [...] Read more.
IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop “Optimizing Lidar Design for Wind Energy Applications” was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model. Full article
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Open AccessArticle
Airborne Doppler Wind Lidar Observations of the Tropical Cyclone Boundary Layer
Remote Sens. 2018, 10(6), 825; https://doi.org/10.3390/rs10060825
Received: 16 April 2018 / Revised: 17 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
Cited by 2 | PDF Full-text (9436 KB) | HTML Full-text | XML Full-text
Abstract
This study presents a verification and an analysis of wind profile data collected during Tropical Storm Erika (2015) by a Doppler Wind Lidar (DWL) instrument aboard a P3 Hurricane Hunter aircraft of the National Oceanic and Atmospheric Administration (NOAA). DWL-measured winds are compared [...] Read more.
This study presents a verification and an analysis of wind profile data collected during Tropical Storm Erika (2015) by a Doppler Wind Lidar (DWL) instrument aboard a P3 Hurricane Hunter aircraft of the National Oceanic and Atmospheric Administration (NOAA). DWL-measured winds are compared to those from nearly collocated GPS dropsondes, and show good agreement in terms of both the wind magnitude and asymmetric distribution of the wind field. A comparison of the DWL-measured wind speeds versus dropsonde-measured wind speeds yields a reasonably good correlation (r2 = 0.95), with a root mean square error (RMSE) of 1.58 m s−1 and a bias of −0.023 m s−1. Our analysis shows that the DWL complements the existing P3 Doppler radar, in that it collects wind data in rain-free and low-rain regions where Doppler radar is limited for wind observations. The DWL observations also complement dropsonde measurements by significantly enlarging the sampling size and spatial coverage of the boundary layer winds. An analysis of the DWL wind data shows that the boundary layer of Erika was much deeper than that of a typical hurricane-strength storm. Streamline and vorticity analyses based on DWL wind observations explain why Erika maintained intensity in a sheared environment. This study suggests that DWL wind data are valuable for real-time intensity forecasts, basic understanding of the boundary layer structure and dynamics, and offshore wind energy applications under tropical cyclone conditions. Full article
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Open AccessArticle
Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake
Remote Sens. 2018, 10(5), 721; https://doi.org/10.3390/rs10050721
Received: 23 March 2018 / Revised: 30 April 2018 / Accepted: 4 May 2018 / Published: 7 May 2018
Cited by 2 | PDF Full-text (4989 KB) | HTML Full-text | XML Full-text
Abstract
Scanning Doppler lidars are the best tools for acquiring 3D velocity fields of full scale wind turbine wakes, whether the objective is a better understanding of some features of the wake or the validation of wake models. Since these lidars are based on [...] Read more.
Scanning Doppler lidars are the best tools for acquiring 3D velocity fields of full scale wind turbine wakes, whether the objective is a better understanding of some features of the wake or the validation of wake models. Since these lidars are based on the Doppler effect, a single scanning lidar normally relies on certain assumptions when estimating some components of the wind velocity vector. Furthermore, in order to reconstruct volumetric information, one needs to aggregate data, perform statistics on it and, most likely, interpolate to a convenient coordinate system, all of which introduce uncertainty in the measurements. This study simulates the performance of a virtual lidar performing stacked step-and-stare plan position indicator (PPI) scans on large-eddy simulation (LES) data, reconstructs the wake in terms of the average and the standard deviation of the longitudinal velocity component, and quantifies the errors. The variables included in the study are as follows: the location of the lidar (ground-based and nacelle-mounted), different atmospheric conditions, and varying scan speeds, which in turn determine the angular resolution of the measurements. Testing different angular resolutions allows one to find an optimum that balances the different error sources and minimizes the total error. An optimum angular resolution of 3 has been found to provide the best results. The errors found when reconstructing the average velocity are low (less than 2% of the freestream velocity at hub height), which indicates the possibility of high quality field measurements with an optimal angular resolution. The errors made when calculating the standard deviation are similar in magnitude, although higher in relative terms than for the mean, thus leading to a poorer quality estimation of the standard deviation. This holds true for the different inflow cases studied and for both ground-based and nacelle-mounted lidars. Full article
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Open AccessArticle
Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation
Remote Sens. 2018, 10(5), 668; https://doi.org/10.3390/rs10050668
Received: 31 March 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
Cited by 8 | PDF Full-text (4194 KB) | HTML Full-text | XML Full-text
Abstract
This study presents the setup, methodology and results from a measurement campaign dedicated to the characterization of full-scale wind turbine wakes under different inflow conditions. The measurements have been obtained from two pulsed scanning Doppler lidars mounted on the nacelle of a 2.5 [...] Read more.
This study presents the setup, methodology and results from a measurement campaign dedicated to the characterization of full-scale wind turbine wakes under different inflow conditions. The measurements have been obtained from two pulsed scanning Doppler lidars mounted on the nacelle of a 2.5 MW wind turbine. The first lidar is upstream oriented and dedicated to the characterization of the inflow with a variety of scanning patterns, while the second one is downstream oriented and performs horizontal planar scans of the wake. The calculated velocity deficit profiles exhibit self-similarity in the far wake region and they can be fitted accurately to Gaussian functions. This allows for the study of the growth rate of the wake width and the recovery of the wind speed, as well as the extent of the near-wake region. The results show that a higher incoming turbulence intensity enhances the entrainment and flow mixing in the wake region, resulting in a shorter near-wake length, a faster growth rate of the wake width and a faster recovery of the velocity deficit. The relationships obtained are compared to analytical models for wind turbine wakes and allow to correct the parameters prescribed until now, which were obtained from wind-tunnel measurements and large-eddy simulations (LES), with new, more accurate values directly derived from full-scale experiments. Full article
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Open AccessArticle
Wind Gust Detection and Impact Prediction for Wind Turbines
Remote Sens. 2018, 10(4), 514; https://doi.org/10.3390/rs10040514
Received: 25 January 2018 / Revised: 17 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
Cited by 3 | PDF Full-text (19525 KB) | HTML Full-text | XML Full-text
Abstract
Wind gusts on a scale from 100 m to 1000 m are studied due to their significant influence on wind turbine performance. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm utilizes [...] Read more.
Wind gusts on a scale from 100 m to 1000 m are studied due to their significant influence on wind turbine performance. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm utilizes the “peak over threshold method,” Moore-Neighbor tracing algorithm, and Taylor’s frozen turbulence hypothesis. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian shape distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed, and an empirical power function is derived. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced, in which the Gaussian plume model and random walk theory including size dependency are applied. The prediction model was tested and the results reveal that the prediction model can represent the spanwise deviation of the gusts and capture the effect of gust size. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted. Full article
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Open AccessArticle
Coherent Focused Lidars for Doppler Sensing of Aerosols and Wind
Remote Sens. 2018, 10(3), 466; https://doi.org/10.3390/rs10030466
Received: 16 January 2018 / Revised: 19 February 2018 / Accepted: 21 February 2018 / Published: 16 March 2018
Cited by 2 | PDF Full-text (5337 KB) | HTML Full-text | XML Full-text
Abstract
Many coherent lidars are used today with aerosol targets for detailed studies of e.g., local wind speed and turbulence. Fibre-optic lidars operating near 1.5 μm dominate the wind energy market, with hundreds now installed worldwide. Here, we review some of the beam/target physics [...] Read more.
Many coherent lidars are used today with aerosol targets for detailed studies of e.g., local wind speed and turbulence. Fibre-optic lidars operating near 1.5 μm dominate the wind energy market, with hundreds now installed worldwide. Here, we review some of the beam/target physics for these lidars and discuss practical problems. In a monostatic Doppler lidar with matched local oscillator and transmit beams, focusing of the beam gives rise to a spatial sensitivity along the beam direction that depends on the inverse of beam area; for Gaussian beams, this sensitivity follows a Lorentzian function. At short range, the associated probe volume can be extremely small and contain very few scatterers; we describe predictions and simulations for few-scatterer and multi-scatterer sensing. We review the single-particle mode (SPM) and volume mode (VM) modelling of Frehlich et al. and some numerical modelling of lidar detector time series and statistics. Interesting behaviour may be observed from a modern coherent lidar used at short ranges (e.g., in a wind tunnel) and/or with weak aerosol seeding. We also review some problems (and solutions) for Doppler-sign-insensitive lidars. Full article
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Open AccessArticle
Assessing Global Ocean Wind Energy Resources Using Multiple Satellite Data
Remote Sens. 2018, 10(1), 100; https://doi.org/10.3390/rs10010100
Received: 29 October 2017 / Revised: 29 December 2017 / Accepted: 9 January 2018 / Published: 12 January 2018
Cited by 4 | PDF Full-text (5558 KB) | HTML Full-text | XML Full-text
Abstract
Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around [...] Read more.
Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies. Full article
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Open AccessArticle
Wind in Complex Terrain—Lidar Measurements for Evaluation of CFD Simulations
Remote Sens. 2018, 10(1), 59; https://doi.org/10.3390/rs10010059
Received: 13 November 2017 / Revised: 21 December 2017 / Accepted: 28 December 2017 / Published: 4 January 2018
Cited by 4 | PDF Full-text (3946 KB) | HTML Full-text | XML Full-text
Abstract
Computational Fluid Dynamics (CFD) is widely used to predict wind conditions for wind energy production purposes. However, as wind power development expands into areas of even more complex terrain and challenging flow conditions, more research is needed to investigate the ability of such [...] Read more.
Computational Fluid Dynamics (CFD) is widely used to predict wind conditions for wind energy production purposes. However, as wind power development expands into areas of even more complex terrain and challenging flow conditions, more research is needed to investigate the ability of such models to describe turbulent flow features. In this study, the performance of a hybrid Reynolds-Averaged Navier-Stokes (RANS)/Large Eddy Simulation (LES) model in highly complex terrain has been investigated. The model was compared with measurements from a long range pulsed Lidar, which first were validated with sonic anemometer data. The accuracy of the Lidar was considered to be sufficient for validation of flow model turbulence estimates. By reducing the range gate length of the Lidar a slight additional improvement in accuracy was obtained, but the availability of measurements was reduced due to the increased noise floor in the returned signal. The DES model was able to capture the variations of velocity and turbulence along the line-of-sight of the Lidar beam but overestimated the turbulence level in regions of complex flow. Full article
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Other

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Open AccessComment
Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”
Remote Sens. 2018, 10(10), 1625; https://doi.org/10.3390/rs10101625
Received: 12 July 2018 / Revised: 8 October 2018 / Accepted: 10 October 2018 / Published: 12 October 2018
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Abstract
We refute statements in “Zhou, K., et al. Wind gust detection and impact prediction for wind turbines. Remote Sens. 2018, 10, 514.” about the impracticality of motion estimation methods to derive two-component vector wind fields from single scanning aerosol lidar data. [...] Read more.
We refute statements in “Zhou, K., et al. Wind gust detection and impact prediction for wind turbines. Remote Sens. 2018, 10, 514.” about the impracticality of motion estimation methods to derive two-component vector wind fields from single scanning aerosol lidar data. Our assertion is supported by recently published results on the performance of two image-based motion estimation methods: cross-correlation (CC) and wavelet-based optical flow (WOF). The characteristics and performances of CC and WOF are compared with those of a two-dimensional variational (2D-VAR) method that was applied to radial velocity fields from a single scanning Doppler lidar. The algorithmic aspects of WOF and 2D-VAR are reviewed and we conclude that these two approaches are in fact similar and practical. Full article
Open AccessProject Report
IEA Wind Task 32: Wind Lidar
Identifying and Mitigating Barriers to the Adoption of Wind Lidar
Remote Sens. 2018, 10(3), 406; https://doi.org/10.3390/rs10030406
Received: 24 January 2018 / Revised: 19 February 2018 / Accepted: 23 February 2018 / Published: 6 March 2018
Cited by 6 | PDF Full-text (1361 KB) | HTML Full-text | XML Full-text
Abstract
IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex [...] Read more.
IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants. Full article
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Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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