LiDAR Wind Measurement: Benefits of Vector Averaging

Cornelia von Zengen/ September 18, 2018/ Publication, Site Assessment

Learn how WindGuard stumbled upon a surprising effect some remote sensing devices show when it comes to measuring wind speed in turbulent wind flow and how a different approach to evaluating LiDAR data can help to decrease uncertainty in your wind measurement campaign.

Wind measurements with LiDAR and SoDAR have become increasingly popular in the wind energy industry to perform wind measurements, especially in locations where traditional measurements with met masts and cup anemometers are difficult or even impossible. Since the official publication of standard IEC 61400-12-1, Ed.2 in March last year, remote sensing devices are increasingly accepted as an equal alternative to wind measurements with cup or ultrasonic anemometers.

As WindGuard has been closely involved in the development of standards for remote sensing devices (RSD) from the very start, ranging from how to best apply RSD for wind or power curve measurements to “how to calibrate a LiDAR”, we thought we knew everything there is to know about remote sensing of wind. Imagine our surprise, when we noticed unexpectedly high deviations between a LiDAR and an adjacent met mast in one of our power curve measurements.

Unexpected deviations between a LiDAR and an adjacent met mast in a measurement campaign led to a thorough investigation

The deviations occurred only under certain conditions. When high turbulence occurred, the LiDAR would measure a much higher wind speed than the cup anemometer. As the LiDAR – a Windcube V2 – had shown high accuracy at a prior calibration, we were puzzled as to why the sensitivity with regards to wind turbulence intensity suddenly increased by 10% in comparison to previous classification tests. In close cooperation with LEOSPHERE, the manufacturer of the LiDAR, we conducted a thorough investigation to find the cause of the deviations.

Sensitivity Analysis sheds light on possible cause

Sensitivity Analysis - LiDAR vs. Cup

We soon became aware that the phenomenon was not specific to this model of LiDAR, but there rather seemed to be a more general problem at the root of this. The whole scenario reminded us of earlier discussions about nacelle LiDARs, where similar mistakes were observed. What if the same mistake that lead nacelle LiDARs to produce inaccurate results was causing a malfunction in our measurement with the ground-based LiDAR? A look into the way most LiDARs evaluate wind speed set things in motion. For historic reasons most ground-based wind LiDARs use scalar averaging as the algorithm to calculate wind speed.

Learn more on the difference between scalar and vector averaging

A LiDAR measures wind speed along its detection beam at different locations. Each measurement at one location lasts about one second. The desired horizontal wind speed has to be calculated from this data under the assumption, that the wind speed is the same at every beam position. There are two ways to arrive at a 10-minute average horizontal wind speed:

  1. First calculate the horizontal wind speed for every measurement and then average over 10 Minutes, or
  2. First average the measurements of each beam position and after that calculate the horizontal wind speed.

Approach 1) averages the scalar horizontal wind speed and is therefore called scalar averaging, whereas approach 2) results in averaging the full wind vector, which gives it its name: vector averaging.

In the case of nacelle LiDARs, implementing vector averaging instead of scalar averaging lead to a significant reduction of their sensitivity to turbulence intensity to almost zero. Using data from different units of the type Windcube V2, we applied vector averaging instead of scalar averaging and investigated how the sensitivity changed with regards to turbulence intensity and wind shear.

Scalar averaged wind speed prooved unrealistically high

As expected, the difference of the scalar and vector averaged wind speed calculated from Windcube measurement data proved unrealistically high under certain conditions. It was a multiple higher than the difference evaluated based on simultaneous measurements of a cup anemometer and vane. The sensitivity of the Windcube on turbulence intensity almost vanishes when comparing the vector averaged wind speed measured by the LiDAR to the scalar average of the cup anemometer. In addition, the analysed sensitivity of the wind speed measurements of the Windcube on the wind shear is reduced by a factor of about 2 by using vector averaging, which is likely caused by the correlation of wind shear and turbulence intensity. Also, the absolute agreement of cup anemometer measurements to the LiDAR measurements improves when using vector averaging.

Results show measurement error in monostatic RSDs

classification results with regards to sensitivity

The results indicate a true measurement error of the Windcube, caused by its monostatic character in combination with scalar averaging. This is the exact same phenomenon that had already been reported in the case of 2-beam monostatic nacelle LiDARs. As a conclusion, we identified scalar averaging as a key driver of the relatively high sensitivities of the wind speed measurement by monostatic RSDs on environmental variables. By the implementation of vector averaging, the measurement uncertainty due to the sensitivities is reduced significantly. The results strongly suggest that the classical scalar averaging approach used in most of today’s commercially available monostatic ground-based LiDARs and SoDARs should be replaced by vector averaging.

Conclusions with regards to vector averaging


Implementing vector averaging instead of scalar averaging into the software of remote sensing devices is a job for all manufacturers who want high accuracy for their measurement device. But anyone evaluating wind measurement data from remote sensing devices has to be aware of the effect the change in approach has on their results. The case also shows that we still have a lot to learn when it comes to accurately measuring wind with remote sensing devices. One starting point for further research we noticed during our analysis: wind speed calculated via velocity azimuth display (VAD) seems to show sensitivity to turbulence intensity in the opposite direction. To confirm this, we still have some research to do. Stay tuned for more on this topic in the future.

Click here to download a presentation on the topic

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Co Authors :

Axel Albers

Axel Albers is managing director and senior consultant of accredited testing and calibration lab Deutsche WindGuard Consulting GmbH. He started working in the wind industry in 1992 and is a leading expert in field of wind measurement.

Get in touch with Axel

Klaus Franke

Dr. Klaus Franke is the head of the remote sensing calibration department at WindGuard. He has a PhD in physics and extensive experience in wind remote sensing.

Get in touch with Klaus

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