Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT

Hasager, Charlotte Bay; Mouche, Alexis; Badger, Merete; Bingöl, Ferhat; Karagali, Ioanna; Driesenaar, Tilly; Stoffelen, Ad; Peña, Alfredo; Longépé, Nicolas

doi:10.1016/j.rse.2014.09.030

Cited by 88 publications

(77 citation statements)

References 45 publications

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“…Therefore, the quantification of pronounced horizontal wind speed gradients in the coastal zone is extremely important for the placement of offshore wind farms. The value of SAR derived wind resource maps largely depends on the validity close to shore according to Hasager et al [9] and Hasager et al [14]. This validation study further supports the use of SAR wind retrievals for wind resource mapping in coastal waters.…”

Section: Discussionsupporting

confidence: 75%

“…Unlike previous validation studies in coastal areas [9][10][11][12][13][14][15][16], the use of scanning LiDARs made comparisons to SAR wind speeds on a transect possible. The effect of currents, waves and limited fetch on e.g., the Charnock parameter in coastal waters has not been taken into account in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Hasager et al [9] 149 to 197 Denmark/The Netherlands 1.27 to 1.65 Envisat Chang et al [10] 552 South Chinese Sea 2.09 Envisat Takeyama et al [11] 42 Japan 0.75 to 2.24 Envisat Chang et al [12] 522 East Chinese Sea 1.99 Envisat Takeyama et al [13] 33 to 73 Japan 0.64 to 2.34 Envisat Hasager et al [14] 875 Baltic Sea 1.17 Envisat Christiansen et al [15] 91 Denmark 1.1 to 1.8 ERS2 Hasager et al [16] 61 Denmark 0.9 to 1.14 ERS2…”

Section: Referencementioning

confidence: 99%

“…Most wind farms are placed within 50 km off the coast where the marine boundary layer can still be affected by the coast [26]. Scatterometer and SAR retrieved winds in combination are used for wind resource assessment, e.g., over the Great Lakes [27] and Northern Europe [9,28]. High resolution wind speed measurements from SAR can show the coastal horizontal wind speed gradient and validation of this ability will e.g., increase the value of SAR measurements for wind resource assessment.…”

Section: Referencementioning

confidence: 99%

“…The influence of those effects can change when moving from the open ocean to the coastal zone. Table 1 shows an overview of selected past studies validating C-band SAR winds in coastal regions using meteorological masts [9][10][11][12][13][14][15][16]. Resulting Root Mean Square Errors (RMSE) ranged between 0.64 ms −1 and 2.34 ms −1 .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Ahsbahs¹,

Badger²,

Karagali³

et al. 2017

Remote Sensing

Self Cite

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Microwave Remote Sensing

Chen

2017

International Encyclopedia of Geography

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Microwave remote sensing, an indispensable earth observation technology, receives and analyzes signatures either transmitted or reflected from features using electromagnetic waves with wavelength primarily ranging from 1 mm to 100 cm. Microwave remote sensing can be divided into two categories, based on the type of sensor used: passive and active. The passive approach uses natural illumination energy emitted via either thermal or microwave radiation. The active approach, on the other hand, transmits its own energy toward earth; this interacts with the atmosphere and/or surface and reflects back toward the sensor. There are two types of data acquisition which are feasible through this technology, and techniques include the imaging systems of the microwave radiometer and side‐looking radar and the non‐imaging systems of the microwave scatterometer and altimeter. In the past half century, microwave remote sensing applications on ocean, land, atmosphere, and ice, for disaster mitigation, and even for archaeology and other areas of work have registered significant progress, particularly owing to their all‐weather, all‐day operational capability, which combines penetration and interferometry.

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Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Liu

et al. 2020

Energy Science & Engineering

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Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT

Cited by 88 publications

References 45 publications

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

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

Microwave Remote Sensing

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

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