Mapping Offshore Winds Around Iceland Using Satellite Synthetic Aperture Radar and Mesoscale Model Simulations

Hasager, Charlotte Bay; Badger, Merete; Nawri, Nikolai; Furevik, Birgitte Rugaard; Petersen, Guðrún Nína; Björnsson, Halldór; Clausen, Niels-Erik

doi:10.1109/jstars.2015.2443981

Cited by 12 publications

(10 citation statements)

References 42 publications

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“…LiDAR measurements are averaged over 10 min and SAR wind retrievals give an almost instantaneous wind speed. This difference is usually addressed by spatially averaging SAR winds around the in LiDARsitu measurement with a box window [17,41] or more advanced footprint averaging methods [16]. Rectangular averaging bins centered around the LiDAR transect with R east perpendicular to the coast (East-West) and R north parallel to the coast (North-South) are applied for this study.…”

Section: Temporal and Horizontal Displacementmentioning

confidence: 99%

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

Ahsbahs¹,

Badger²,

Karagali³

et al. 2017

Remote Sensing

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High-accuracy wind data for coastal regions is needed today, e.g., for the assessment of wind resources. Synthetic Aperture Radar (SAR) is the only satellite borne sensor that has enough resolution to resolve wind speeds closer than 10 km to shore but the Geophysical Model Functions (GMF) used for SAR wind retrieval are not fully validated here. Ground based scanning light detection and ranging (LiDAR) offer high horizontal resolution wind velocity measurements with high accuracy, also in the coastal zone. This study, for the first time, examines accuracies of SAR wind retrievals at 10 m height with respect to the distance to shore by validation against scanning LiDARs. Comparison of 15 Sentinel-1A wind retrievals using the GMF called C-band model 5.N (CMOD5.N) versus LiDARs show good agreement. It is found, when nondimenionalising with a reference point, that wind speed reductions are between 4% and 8% from 3 km to 1 km from shore. Findings indicate that SAR wind retrievals give reliable wind speed measurements as close as 1 km to the shore. Comparisons of SAR winds versus two different LiDAR configurations yield root mean square error (RMSE) of 1.31 ms −1 and 1.42 ms −1 for spatially averaged wind speeds.

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Section: Temporal and Horizontal Displacementmentioning

confidence: 99%

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

Ahsbahs¹,

Badger²,

Karagali³

et al. 2017

Remote Sensing

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“…Satellite SAR was used for resource assessment for the North Sea (Hasager et al, 2005(Hasager et al, , 2015bChristiansen et al, 2016;Badger et al, 2010) and the Baltic Sea (Hasager et al, 2011;Badger et al, 2016) and was compared to meteorological mast data. Coastal mast data and mesoscale model results were compared to SAR-based wind resource estimates for the Icelandic waters (Hasager et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

Europe's offshore winds assessed with synthetic aperture radar, ASCAT and WRF

et al. 2020

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Abstract. Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. The wind statistics at 10 and 100 m above mean sea level (a.m.s.l.) height using an extrapolation procedure involving simulated long-term stability over oceans are presented for both SAR and ASCAT. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented. This has 3 km grid spacing with data every 30 min for 30 years from 1989 to 2018, while ASCAT has 12.5 km and SAR has 2 km grid spacing. Offshore mean wind speed maps at 100 m a.m.s.l. height from ASCAT, SAR, WRF and ERA5 at a European scale are compared. A case study on offshore winds near Crete compares SAR and WRF for flow from the north, west and all directions. The paper highlights the ability of the WRF model to simulate the overall European wind climatology and the near-coastal winds constrained by the resolution of the coastal topography in the WRF model simulations.

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“…These include water depth, environmental considerations, and shipping routes. The methodology developed here to create the resource estimation measure (referred hereafter as the resource energy) is based on several technical reports and publications aimed to produce practical results that could be directly used by decision makers and stakeholders (Hasager et al, 2015;Lopez et al, 2012;Musial et al, 2016a, Musial et al, 2016bBeiter et al, 2017;U.S. Department of Energy, 2016;Hasager et al, 2017;Schwartz et al, 2010;Adams and Keith, 2013).…”

Section: Wind Energy Resources Estimationmentioning

confidence: 99%

“…> 2500 images) is available over a relatively long period of time (e.g. Hasager et al, 2015), the wind climatology used for the wind resource estimation is simply constructed by averaging the available SAR fields. In this research, several wind climatologies are constructed and validated against in-situ observations.…”

Section: Sar-based Wind Mappingmentioning

confidence: 99%

“…Offshore wind resources are typically investigated by calculating wind power density at the turbine hub height or by estimating the potential energy in electrical units. Wind speed information is obtained from wind atlases produced from routine in-situ surface wind measurements (Bina et al, 2018), direct use of in-situ measurements (Dhanju et al, 2008), mesoscale numerical weather prediction model simulations with remote sensing data such as Synthetic Aperture Radar (SAR; Hasager et al, 2015), or combinations of these (Pimenta et al, 2008). The development of offshore wind energy requires consideration of several technical limitations such as: sufficient wind resources, relatively shallow waters that allow installing fixed-foundation or floating turbines, and avoiding major and busy transportation routes particularly in narrow channels and water corridors.…”

Section: Introductionmentioning

confidence: 99%

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Assessing offshore wind power resources in British Columbia

Bakri¹

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Wind resources are investigated and estimated offshore of the northern and central coasts of British Columbia, Canada. Remote sensing-based wind speed observations from a Synthetic Aperture Radar (SAR) mounted on the Canadian RADARSAT-2 satellite are used for mapping offshore winds. In addition, in-situ wind speed observations extracted from several buoys distributed in the study region are used to analyze the temporal and spatial wind speed variations in relation to wind power generation. Sustained winds above several wind turbine thresholds are analyzed and values of 50-yr and 100-yr return extreme wind speed levels are calculated. The wind variability analysis suggests few interruptions to power generation by either very low wind speeds or extreme wind speed events with high spatial variability between offshore areas and sites located within the coastal mountains. The SAR wind speed fields are characterized by a high spatial resolution but cover a period of less than 2.5 years with a random temporal availability. The SAR fields are extrapolated to reanalysis long-term wind fields that are available over a climatological time period with a sub-daily temporal resolution but a coarse spatial resolution. The extrapolation procedure is developed by applying a statistical downscaling model and a bias-based correction method. Wind fields from both methods are validated against the in-situ observations from buoys. The extrapolated wind fields are used for mapping offshore winds by creating a robust wind climatology that represents the mesoscale wind variance as well as the diurnal wind variability. This wind climatology is used to calculate the wind statistics and power density, in addition to estimate offshore wind resources. Viable areas for wind power development are defined by using high resolution bathymetric data and considering the general environmental and ecological constraints in the region. The estimated offshore wind resource energy using only theiv determined viable areas is found to resemble a large portion of the current total power generation in British Columbia. Most suitable areas for offshore wind farms are determined by developing criteria based on a combination of the turbine tower technology, water depth zoning and power density values.

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Mapping Offshore Winds Around Iceland Using Satellite Synthetic Aperture Radar and Mesoscale Model Simulations

Cited by 12 publications

References 42 publications

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

Europe's offshore winds assessed with synthetic aperture radar, ASCAT and WRF

Assessing offshore wind power resources in British Columbia

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