average. They investigate the behaviour of the reconstruction error caused by the temporal and spatial quantisation of the scanning behaviour and the projection of the LOS velocities.
A different approach of reconstruction is based on the modal decomposition of the flow field using Proper Orthogonal Decomposition (POD). Both research teams of Bastine et al. [92, 93, 94]
and Iungo et al. [95] show that different approaches and implementations of POD can be used to dynamically reconstruct wake wind fields in high precision. In all studies, however, it is described that the reconstruction quality depends on the number and selection of modes.
However, some reconstructions do not aim to reproduce wake wind fields, but only to map properties of the wake. In studies including wake characterisations, the wake motion is mostly reconstructed in the form of the temporal resolved centreline motion to obtain information about the position and/or the atmospheric driven wake meandering [66, 69, 85]. In addition, the recovery of the wind speed deficit in downstream direction and geometric derivatives, such as the wake width, are considered [49, 53, 96, 97].
filtering of outliers is necessary to enable an as exact as possible mapping of the flow. Otherwise, errors may be amplified in the process chain leading to falsification of the flow characteristics not only in the form of erroneous statistics but also of unrealistic time series. To reduce these influences, filtering the LiDAR data is indispensable. In Chapter 2, the following questions will be addressed:
• How can a filter method for the unattended and automated application be conceptualised as adaptively and dynamically as possible?
• How can LOS velocities be filtered to achieve maximum mapping accuracy?
• What are the differences to the reference measurements regarding the filtering of LiDAR data with different methods?
In Chapter 2, an evaluation will be presented in which different common filtering approaches are applied. Staring-mode LiDAR data is compared with wind speed data of a 3D ultrasonic anemometer from the offshore meteorological mast FINO1. In this framework, a filter method is proposed that reconstruct the flow statistics by minimising the error for stationary and scanning measurements while maximising data availability with the approach of data self-similarity.
For the reconstruction of dynamic wake wind field, temporal and spatially highly resolved planar LiDAR measurements of the wake are favoured for the purpose of analysing and reproducing the flow dynamics as accurately as possible. Due to the space-time dilemma of a scanning measurement, the whole flow field cannot be recorded concurrently using the current LiDAR technology. Consequently and simplified, each measurement is a compromise between good temporal resolution and good spatial resolution, from which it becomes evident that, based on the characteristics of the processes to be mapped, an optimal combination of measurement parameters exists. Depending on the scan speed, angular resolution and the measurement trajectory (among other parameters), scan repetition times of a few seconds to several deca minutes can occur limiting the time scale of the resolvable dynamics. To compensate temporal and spatial limitations, several LiDAR systems are increasingly being used in complex measurement campaigns, most of which perform unsynchronised LiDAR measurements. To cope the requirements of data quality for reconstruction and to overcome technical limitations of scanning measurement with current LiDAR devices, Chapter 3 focuses on the following questions:
• How can the temporal resolution of scanned measurements be retrospectively improved?
• How to synchronise planar scanned LiDAR measurements with another temporal quantisation?
• What are the characteristics of the flow statistics that were temporal up-sampled?
In Chapter 3, a temporal up-sampling method based on a space-time conversion, later called the wind field propagation, is introduced. The up-sampling is the prerequisite for accurate representation and synchronisation of LiDAR scans with external data which in turn is the requirement for wind field reconstruction. Within a bivariant parameter study it is shown first how the scan speed influences the mapping error and secondly how the wind field propagation can reduce the statistical error due to insufficient samples.
The final reconstruction of a dynamic three-dimensional respectively a four-dimensional wake wind field is based on model assumptions in order to satisfy the information that cannot be
recorded by measurements. In Chapter 4, all the research presented afore will be brought together to answer the following questions:
• How can wake dynamics be captured using todays LiDAR (volumetric-) measurements?
• How do LiDAR measurements need to be processed to be used for a dynamic 3D1C wake wind-field reconstruction?
• Which assumptions are required for the wake wind-field reconstruction?
• What are the characteristics of the reconstruction deviations?
In Chapter 4, the wind field propagation presented in Chapter 3 is used to up-sample LiDAR data to reconstruct the wake dynamics in a finer temporal resolution than the insufficient measurement scale. Wake assumptions are defined, which are similar to those of the DWM, but exceed the DWM in terms of the number of dynamic tracking parameters. For verifiability, the evaluation of the reconstruction quality the reconstruction method is applied to a data set of a synthetic wake wind field and a numerical LiDAR simulator. Also here a parameter study is shown how the scan speed of the LiDAR affects the reconstruction result.
Chapter 5 concludes the research presented and answer the main question with reference to the individual chapters and place it in the overall context, which leads to an outlook on subsequent and further research.
2 D YNAMIC D ATA F ILTERING
OF L ONG -R ANGE D OPPLER L I DAR W IND S PEED
M EASUREMENTS
The content of this chapter is identical to the following journal article:
Beck, H.; Kühn, M.: Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements. Remote Sensing. 2017, 9, 561, doi: 10.3390/rs9060561.
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Article
Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
Hauke Beck* and Martin Kühn
ForWind - University of Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany;
* Correspondence: hauke.beck@uni-oldenburg.de; Tel.: +49-441-798-5069
Received: 24 December 2016 / Revised: 26 May 2017 / Accepted: 31 May 2017 / Published: 4 June 2017
Abstract: Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.
Keywords: data density; spatial normalisation; temporal normalisation; carrier-to-noise-ratio; line-of-sight velocity; radial velocity; threshold filter