Editorial for the special issue "remote sensing of atmospheric conditions forwind energy applications"

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

Validation of three-component wind lidar sensor for traceable highly resolved wind vector measurements

Conventional monostatic wind lidar (light detection and ranging) systems are well-established wind speed remote sensing devices in the field of wind energy that provide reliable measurement results for flat terrain and homogeneous wind fields. These conventional wind lidar systems use a common transmitting and receiving unit and become unacceptably inaccurate as the wind fields become increasingly inhomogeneous due to their spatial and temporal averaging procedure (large measurement volume) that is inherent to the monostatic measurement principle. The new three-component fiber laser-based wind lidar sensor developed by the Physikalisch-Technische Bundesanstalt (PTB) uses one transmitting unit (fiber laser) and three receiving units to measure the velocity vector of single aerosols in a spatially highly resolved measurement volume (with diameter d and length l) in heights from 5&thinsp;m (d=300&thinsp;µm, l=2&thinsp;mm) to 250&thinsp;m (d=14&thinsp;mm, l=4&thinsp;m) with a resolution of about 0.1&thinsp;m&thinsp;s−1. Detailed comparison measurements with a 135&thinsp;m high wind met mast and a conventional lidar system have proven that the high spatial and temporal resolution of the new, so-called bistatic lidar leads to a reduced measurement uncertainty compared to conventional lidar systems. Furthermore, the comparison demonstrates that the deviation between the bistatic lidar and the wind met mast lies well within the measurement uncertainty of the cup anemometers of the wind met mast for both homogeneous and inhomogeneous wind fields. At PTB, the aim is to use the bistatic wind lidar as a traceable reference standard to calibrate other remote sensing devices, necessitating an in-depth validation of the bistatic lidar system and its measurement uncertainty. To this end, a new, specially designed wind tunnel with a laser Doppler anemometer (LDA) as flow velocity reference has been erected on a platform at a height of 8&thinsp;m; this allows the new wind lidar to be positioned below the wind tunnel test section to be validated for wind vector measurements that are traceable to the SI units. A first validation measurement within the wind tunnel test section is presented, showing a deviation between the bistatic lidar system and the LDA clearly below 0.1&thinsp;%.</p

IEA Wind Task 32: Wind lidar identifying and mitigating barriers to the adoption of wind lidar

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

An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions

Wind profiling by Doppler lidar is common practice and highly useful in a wide range of applications. Airborne Doppler lidar can provide additional insights relative to ground-based systems by allowing for spatially distributed and targeted measurements. Providing a link between theory and measurement, a first large eddy simulation (LES)-based airborne Doppler lidar simulator (ADLS) has been developed. Simulated measurements are conducted based on LES wind fields, considering the coordinate and geometric transformations applicable to real-world measurements. The ADLS provides added value as the input truth used to create the measurements is known exactly, which is nearly impossible in real-world situations. Thus, valuable insight can be gained into measurement system characteristics as well as retrieval strategies. As an example application, airborne Doppler lidar wind profiling is investigated using the ADLS. For commonly used airborne velocity azimuth display (AVAD) techniques, flow homogeneity is assumed throughout the retrieval volume, a condition which is violated in turbulent boundary layer flow. Assuming an ideal measurement system, the ADLS allows to isolate and evaluate the error in wind profiling which occurs due to the violation of the flow homogeneity assumption. Overall, the ADLS demonstrates that wind profiling is possible in turbulent wind field conditions with reasonable errors (root mean squared error of 0.36 m s−1 for wind speed when using a commonly used system setup and retrieval strategy for the conditions investigated). Nevertheless, flow inhomogeneity, e.g., due to boundary layer turbulence, can cause an important contribution to wind profiling error and is non-negligible. Results suggest that airborne Doppler lidar wind profiling at low wind speeds (<5ms −1) can be biased, if conducted in regions of inhomogeneous flow conditions

Towards improved turbulence estimation with Doppler wind lidar velocity-azimuth display (VAD) scans

The retrieval of turbulence parameters with profiling Doppler wind lidars (DWLs) is of high interest for boundary layer meteorology and its applications. DWLs provide wind measurements above the level of meteorological masts while being easier and less expensive to deploy. Velocity-azimuth display (VAD) scans can be used to retrieve the turbulence kinetic energy (TKE) dissipation rate through a fit of measured azimuth structure functions to a theoretical model. At the elevation angle of 35.3° it is also possible to derive TKE. Modifications to existing retrieval methods are introduced in this study to reduce errors due to advection and enable retrievals with a low number of scans. Data from two experiments are utilized for validation: first, measurements at the Meteorological Observatory Lindenberg–Richard-Aßmann Observatory (MOL-RAO) are used for the validation of the DWL retrieval with sonic anemometers on a meteorological mast. Second, distributed measurements of three DWLs during the CoMet campaign with two different elevation angles are analyzed. For the first time, the ground-based DWL VAD retrievals of TKE and its dissipation rate are compared to in situ measurements of a research aircraft (here: DLR Cessna Grand Caravan 208B), which allows for measurements of turbulence above the altitudes that are in range for sonic anemometers. From the validation against the sonic anemometers we confirm that lidar measurements can be significantly improved by the introduction of the volume-averaging effect into the retrieval. We introduce a correction for advection in the retrieval that only shows minor reductions in the TKE error for 35.3° VAD scans. A significant bias reduction can be achieved with this advection correction for the TKE dissipation rate retrieval from 75° VAD scans at the lowest measurement heights. Successive scans at 35.3 and 75° from the CoMet campaign are shown to provide TKE dissipation rates with a good correlation of R>0.8 if all corrections are applied. The validation against the research aircraft encourages more targeted validation experiments to better understand and quantify the underestimation of lidar measurements in low-turbulence regimes and altitudes above tower heights

Complex terrain experiments in the New European Wind Atlas

The New European Wind Atlas project will create a freely accessible wind atlas covering Europe and Turkey, develop the model chain to create the atlas and perform a series of experiments on flow in many different kinds of complex terrain to validate the models. This paper describes the experiments of which some are nearly completed while others are in the planning stage. All experiments focus on the flow properties that are relevant for wind turbines, so the main focus is the mean flow and the turbulence at heights between 40 and 300 m. Also extreme winds, wind shear and veer, and diurnal and seasonal variations of the wind are of interest. Common to all the experiments is the use of Doppler lidar systems to supplement and in some cases replace completely meteorological towers. Many of the lidars will be equipped with scan heads that will allow for arbitrary scan patterns by several synchronized systems. Two pilot experiments, one in Portugal and one in Germany, show the value of using multiple synchronized, scanning lidar, both in terms of the accuracy of the measurements and the atmospheric physical processes that can be studied. The experimental data will be used for validation of atmospheric flow models and will by the end of the project be freely available. This article is part of the themed issue ‘Wind energy in complex terrains’

An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain

Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point next to a reference mast in complex terrain. This multi-lidar (ML) technique is also compared to a profiling lidar using the Doppler beam swinging (DBS) method. First- and second-order statistics of the radial wind velocities from the individual instruments and the horizontal wind components of several ML combinations are analysed in comparison to sonic anemometry and DBS measurements. The results for the wind speed show significantly reduced scatter and directional error for the ML method in comparison to the DBS lidar. The analysis of the second-order statistics also reveals a significantly better correlation for the ML technique than for the DBS lidar, when compared to the sonic. However, the probe volume averaging of the lidars leads to an attenuation of the turbulence at high wave numbers. Also the configuration (i.e., angles) of the WindScanners in the ML method seems to be more important for turbulence measurements. In summary, the results clearly show the advantages of the ML technique in complex terrain and indicate that it has the potential to achieve significantly higher accuracy in measuring turbulence quantities for wind-energy applications than classical profiling lidars

Model-based study of the five main influencing factors on the wind speed error of lidars in complex and forested terrain

Wind energy will significantly contribute to renewable power generation in the future. Much of the onshore wind energy potential is located at complex and forested sites. Remote sensing, in particular, light detection and ranging (lidar), has become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, common wind profile Doppler lidars suffer from errors at complex terrain sites because of their measurement principle that assumes homogeneous flow between the measurement points. This dissertation answers the question about how well lidars measure at complex terrain sites. The five main influencing factors on the lidar error are orographic complexity, measurement height, surface roughness and forest, atmospheric stability and half-cone opening angle. Structured by five hypotheses, the impact of the different factors is analyzed in a model-based parameter study. In a novel approach, the lidar error due to orographic complexity ε is split up into the part ε_c, caused by flow curvature at the measurement points of the lidar and the part ε_s, caused by the local speed-up effects between the measurement points. This approach, e.g., allows for a systematic and complete interpretation of the influence of the half-cone opening angle φ of the lidar. It also provides information about the uncertainty of simple lidar error estimations that are based on inflow and outflow angles at the measurement points. A non-dimensional approach is chosen to ensure the transferability of the acquired results to actual applications at real-world sites. The model-based parameter study is limited to two-dimensional Gaussian hills with hill height H and hill half-width L, facilitating the possibility to cover a wide range of terrain complexities and variations of the model parameters. H/L and z/L are identified as the main scaling factors for the lidar error. With a potential flow model, the linearized flow model WEng and the RANS CFD model Meteodyn WT, three models of different complexity are used. The outcome of the study provides manifold findings that enable an assessment of the applicability of these flow models. Separating the lidar error ε into ε_c and ε_s shows that, depending on the z/L ratio, speed-up effects cause 10-30 % of the total lidar error. Therefore, a significant uncertainty must be assigned on simple lidar error estimation approaches, which are based on flow inclination angles at the measurement points and neglect this effect. Orographic complexity is found to be the major influencing factor on the lidar error. Depending on the flow model used, the lidar error is about 4-5 times larger when increasing the H/L ratio from 0.1 to 0.4. It is furthermore dependent on measurement height and reaches a maximum at a z equal to 50-60 % of the hill half-width L. Below and above the maximum point, the lidar error decreases and becomes negligible at low and high levels above ground. The height-dependence is sensitive to H/L and z/L and should be assessed before a planned measurement campaign. Opposed effects of reduced φ are found on ε_c and ε_s. This explains the small differences in the total lidar error for symmetric flows in the literature. Contrary to that, in asymmetric flow situations (e.g. forested hills), φ can significantly influence the lidar error. An adaption to the actual flow situation might reduce the lidar error, but would require a more flexible technology, such as a scanning lidar. Non-linear or detached flow effects in the lee of the steep hills, induced by high surface roughness or forest, significantly reduce the lidar error. Therefore, potential flow and linearized models should not be applied at such sites, as they generally overestimate the lidar error. In an evaluation campaign, these findings are confirmed and the best results of lidar error estimation are achieved when considering the forest in the flow model. The influence of atmospheric stability in the lidar error estimations from Meteodyn WT is significant, particularly for stable stratification. At sites where significant changes in atmospheric stability occur, the lidar error is potentially overestimated by assuming neutral stratification. The dissertation clearly shows that orographic complexity, roughness and forest characteristics, as well as atmospheric stability, have a significant influence on lidar error estimation. The choice and parameterization of flow models and the design of methods for lidar error estimation are essential to achieve accurate results. The use of a RANS CFD model in conjunction with an appropriate forest model is highly recommended for lidar error estimations in complex terrain. If atmospheric stability variation at a measurement site plays a vital role, it should also be considered in the modeling. Under certain flow conditions, the half-cone opening angle can additionally affect the magnitude of the lidar error. When planning a wind farm, an accurate estimation of the prospective lidar error should be carried out before the measurement campaign. The additional uncertainty of the lidar error correction should be assessed in this context to make a profound decision on whether a lidar measurement is feasible at the given site

Using scanning Doppler lidar to enhance aviation safety in Iceland

Lidar systems have been used widely to measure wind profiles and atmospheric aerosols. The scanning Doppler lidars operated by the Icelandic Meteorological Office can provide continuous measurements of the wind velocity and direction based on the Doppler effect, from the emitted signal, as well as the backscatter coefficient and depolarization ratio for retrieving aerosol properties. In this project, we investigate the use of Doppler lidars in Iceland, especially for enhancing aviation safety. The project was divided into three main tasks have been conducted: 1) atmospheric turbulence measurements; 2) airborne aerosol detection; 3) real-time lidar signal classification with machine learning algorithms. In the first task, an algorithm was developed based on the Kolmogorov theory to retrieve eddy dissipation rate, as an indicator of turbulence intensity, from lidar wind measurements. The method was tested on two cases from 2017. In the second task, the Doppler lidar was used in combination with ceilometers, a sun-photometer and other instruments, to detect aerosols, including dust and volcanic ash in Iceland. In the third task, both supervised and unsupervised machine learning algorithms were developed to identify the noise signal and classify the lidar measurements, with the aim of providing real-time lidar signal classification for potential end-users. The results indicate that the Doppler lidar can significantly improve aviation safety and complement meteorological measurements by detecting atmospheric turbulence or volcanic ash clouds in Iceland.Vind- og agnasjár (e. lidar) hafa verið notaðar víða til að mæla vindsnið og nema svifryk og aðrar agnir í lofthjúpnum. Veðurstofa Ísland á tvo Doppler agnasjár sem geta veitt samfelldar mælingar á vindhraða og -stefnu, byggðar á Dopplerhrifa, og endurkastsstuðul og tvípólunarhlutfall (e. depolarization ratio) agna. Í þessu verkefni var könnuð notkun Doppler agnasjár á Íslandi til að auka flugöryggi. Verkefninu var skipt niður í þrjá verkþætti: i) ókyrrðarmælingar í jaðarlaginui; ii) svifryk og aðrar agnir í lofthjúpnum; iii) rauntímaflokkun agna með vélrænu námi (e. machine learning). Í verkþætti i) var þróuð reikniaðferð byggð á kenningu Kolmogorov til að meta sveipeyðingarákefð (e. eddy dissipation rate) frá vindmælingum, sem vísbendingu um ókyrrðarstyrk Aðferðin var prófuð í tveimur tilvikum frá árið 2017. Í verkþætti ii) voru gögn frá Doppler agnasjánni notuð, með gögnum úr skýjahæðamæli, sólarljósamæli og öðrum tækjum til að greina svifryk, þar með talið ryk og eldfjallaösku á Íslandi. Í verkþættii iii) var þróaðvélrænt nám, bæði undir eftirliti og án eftirlits, til að bera kennsl á suð og flokka mælingarnar með það að markmiði að veita rauntímaliðaflokkun fyrir hugsanlega notendur. Niðurstöðurnar benda til þess að Doppler vind- og agnasjá geti bætt verulega flugöryggi og verið góð viðbót við hefðbundnari veðurmælingar á Íslandi, með því að greina ókyrrð og öskuí lofti.ISAVI