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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Optimising measurement strategies to maximise project value: Is the industry making false economies at the expense of project value?' taking place on Tuesday, 11 March 2014 at 11:15-12:45. The meet-the-authors will take place in the poster area.

Raghu Krishnamurthy LEOSPHERE, France
Raghu Krishnamurthy (1) F P
(1) LEOSPHERE, Orsay, France

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Case studies of WINDCUBE (TM ) measurement uncertainty for complex terrain using flow complexity recognition (FCR)


Due to the growth of wind farms being currently built in complex terrain conditions, there has been an increasing demand for the evaulation of lidar measurement uncertainty in complex conditions. The accuracy of lidar measurements in complex terrain is complicated, due to flow homogeneity assumption in calculating average wind speed and direction. Flow Complexity Recognition (FCR®) provides improved correlations with tower measurements. In this presentation, results from several studies conducted for wind resource site assessment in complex terrain will be evaulated.


Case studies from at least 8 campaigns with the FCR® option in varying complex terrain scenarios are compared to co-located tower measurements at various heights. Uncertainty estimates from each case, with and without FCR® option are evaluated. Uncertainty estimates are then classified based on terrain complexity and other meteorological factors (thermal stability classification) observed at the site.

Main body of abstract

Wind flow around complex terrain could potentially induce significant errors in lidar wind speed reconstruction algorithm, due to the flow homogeneity assumption, over the lidar volume, in estimating wind speed and direction. WINDCUBE ™ lidar data in FCR® mode provides better correlation of the data with co-located anemometer measurements. In this paper, at least 8 case studies will be presented, where FCR® was applied to WINDCUBE ™ V2 profiler data. Case studies are classified based on terrain complexity, thermal stability observed at site (based on tower wind speed profiles or temperature [if available]), and wind direction. Wind speed uncertainties for various scenarios with and without FCR® are evaluated. The uncertainties are segregated based on various classes, as mentioned above, which provides better knowledge for the value of FCR® in different scenarios. FCR® performance levels are evaluated based on the linear regression parameters after comparison to co-located tower data at various heights (slope, constant, and Pearson’s correlation coefficient). It was observed that FCR® performed better than classical lidar retrievals, assuming flow homogeneity, in several scenarios. The effect of various stability classes on FCR®’s performance and uncertainty will also presented. Several recommendations for lidar site selection based on historic wind directions and terrain orientation will be presented.


The stability of the atmosphere and terrain affects the lidar homogeneity assumption and increases lidar uncertainty. FCR® provides improved correlations compared to classical lidar measurements and improves the project uncertainty estimates. This would assist wind farm developers in providing higher degree of confidence in lidar measurements in complex terrain, especially when lidars are moved to different locations in complex terrain scenarios for determining micro-siting uncertainty.

Learning objectives
The EWEA audience would get a better understanding of the various complex terrain scenarios where wind farms are currently being developed and the effect of homogeneity assumption over the lidar volume in each case. Also, the effect of atmospheric stability and its impact on uncertainty would be presented. The presentation would also focus on recommendations, based on experience from several campaign studies, on optimal siting of lidars in complex terrain.