Hub Height Ocean Winds over the North Sea Observed by the NORSEWInD Lidar Array: Measuring Techniques, Quality Control and Data Management

Hasager, Charlotte Bay; Stein, Detlef; Courtney, Michael; Peña, Alfredo; Mikkelsen, Torben; Stickland, Matthew; Oldroyd, Andrew

doi:10.3390/rs5094280

Cited by 49 publications

(46 citation statements)

References 20 publications

(33 reference statements)

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“…Fino1 is operated by Bundesamt für Seeschifffart und Hydrographie. The Fino1 wind speed data has been used in several studies [26,28,29]. In September 2009, the Alpha Ventus wind farm east of Fino1 came into operation and since then turbine wake influence (reduce) the mast wind speed observations for easterly winds.…”

Section: Meteorological Mast Datamentioning

confidence: 99%

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Hasager¹,

Astrup²,

Zhu

et al. 2016

Remote Sensing

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Abstract:We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas.

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Section: Meteorological Mast Datamentioning

confidence: 99%

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Hasager¹,

Astrup²,

Zhu

et al. 2016

Remote Sensing

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“…The effect of complex terrain has been explored analytically (Bingöl et al, 2008) with linear flow models such as WAsP (Bingöl et al, 2009), and with Reynolds-averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) models Harris et al, 2010;Meissner and Boquet, 2011;Bezault and Boquet, 2011;Gkainias et al, 2011;Butler and Quail, 2012;Bradley et al, 2012). Hasager et al (2013) use RANS CFD to correct measurements in flow over offshore platforms. Wainright et al (2014) use large-eddy simulation (LES) to simulate fields retrieved by sodar toward error quantification.…”

Section: Introductionmentioning

confidence: 99%

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

et al. 2015

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Abstract. Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors.To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s −1 at the turbine hub-height of 80 m. This slightly stable case results in 15 • of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30 % of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: crossstream component errors are on the order of 15 % of the hubheight inflow wind speed (1.0 m s −1 ) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s −1 (or 15 % of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8 % while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

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“…Four of the offshore sites are located around the North Sea and use LIDAR (Siri, TAQA platform, Utsira, Schooner, see [24]), with one offshore site (Stora Middelgrund) located between Sweden and Denmark; the microscale winds of all offshore sites are unaffected by coasts. One land site, Østerild, uses LIDAR data; the LIDAR is located in an extended clearing within a limited forest, but we use only measurements higher than twice the mean tree height in order to avoid the forest-induced roughness sublayer and also related distortion of the LIDAR measurements [25].…”

Section: Validation and Resultsmentioning

confidence: 99%

Probabilistic stability and ‘tall’ wind profiles: theory and method for use in wind resource assessment

Kelly¹,

Troen²

2015

Wind Energy

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A model has been derived for calculating the effects of stability and the finite height of the planetary boundary layer upon the long-term mean wind profile. A practical implementation of this probabilistic extended similarity-theory model is made, including its incorporation within the European Wind Atlas (EWA) methodology for site-to-site application. Theoretical and practical implications of the EWA methodology are also derived and described, including unprecedented documentation of the theoretical framework encompassing vertical extrapolation, as well as some improvement to the methodology. Results of the modelling are shown for a number of sites, with discussion of the models' efficacy and the relative improvement shown by the new model, for situations where a user lacks local heat flux information, as well as performance of the new model using measured flux statistics. Further, the uncertainty in vertical extrapolation is characterized for the EWA model contained in standard (i.e. WAsP) wind resource assessment, as well as for the new model.

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Hub Height Ocean Winds over the North Sea Observed by the NORSEWInD Lidar Array: Measuring Techniques, Quality Control and Data Management

Cited by 49 publications

References 20 publications

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

Probabilistic stability and ‘tall’ wind profiles: theory and method for use in wind resource assessment

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