Assessing the quality of Synthetic Aperture Radar (SAR) wind retrieval in coastal zones using multiple Lidars
Tobias Ahsbahs
Merete Badger, Ioanna Karagali, Xiaoli Larsen
DTU Wind Energy, Technical University of Denmark
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What is the coastal zone?
• Coastal zone here defined as the region over water where the boundary layer is not in equilibrium.
• “Results suggest that the distance from the coastline over which wind speed vertical profiles are not at equilibrium with the sea surface (which defines the coastal zone) extends to 20 km and possibly 70 km from the coast”. 1
• Typically, up to 50km distance to shore is define as the coastal zone for wind energy applications.
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1 Barthelmie et.al., Offshore Coastal Wind Speed Gradients: issues for the design and development of large offshore windfarms, Wind Engineering, 2009
DTU Wind Energy, Technical University of Denmark
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SAR wind retrieval
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Radar backscatter High resolution o cean wind
speed at10 m
DTU Wind Energy, Technical University of Denmark
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From radar backscatter to wind speed
• Radar backscatter is related to the
”roughness” of the ocean surface
• Geophysical Model Function (GMF)
• CMOD5.n for this study
Input:
– Angle between wind
direction and radar beam
– Radar backscatter
signal
Output:
– Wind speed at 10m
– RMSE typically 1.3m/s
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Hasager, C.B. et al. (2015): Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sensing of Environment, 156, 247–263. 10.1016/j.rse.2014.09.030.
DTU Wind Energy, Technical University of Denmark
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Motivation for coastal winds from SAR
Why do we want SAR wind in the coastal zone?
• High resolution can resolve horizontal wind speed gradients
• Large data archive available
What is the problem?
• Coastal effects like currents, breaking waves, limited fetch etc.
• Performance of the GMFs in the coastal zone unknown
Approach:
• Use ground based remote sensing techniques to measure the wind in the coastal zone.
• Compare to available Sentinel-1 data
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DTU Wind Energy, Technical University of Denmark
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The RUNE project
Reducing Uncertainty of Near-shore wind resource Estimates using onshore lidars
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• Data from December
2015 to March 2016
• West Coast of Denmark
• Remote sensing instruments for wind speed measurements
DTU Wind Energy, Technical University of Denmark
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Radial velocity sampling • Wind vector consists of 3
components (u, v, w)
• Radial velocity is a projection of the true wind speed along the laser’s line of sight
• One LiDAR can only measure a portion of the wind vector!
Thanks to Elliot Simon for this slide
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DTU Wind Energy, Technical University of Denmark
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The RUNE project
For more information: Peña et.al., Report on the coastal experiment and first inter-comparisons between measurement systems, 2016
A near shore wind measurement campaign
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Multiple Lidars for:
• Wind profiles
• Sector scans (SC)
• Dual Doppler (DD)
Additional data
• WRF model runs
• SAR images
• Høvsøre tall met mast
DTU Wind Energy, Technical University of Denmark
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The RUNE project
For more information: Peña et.al., Report on the coastal experiment and first inter-comparisons between measurement systems, 2016
A near shore wind measurement campaign
Multiple Lidars for:
• Wind profiles
• Sector scans (SC)
• Dual Doppler (DD)
Additional data
• WRF model runs
• SAR images
• Høvsøre tall met mast
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DTU Wind Energy, Technical University of Denmark
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SAR and lidar wind measurements from the RUNE project
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SAR image of Danish West Coast
Zoom into the RUNE area
Scanning lidar 10min mean speed
Image: Sentinel-1, Processing with SAROPS from APL/NOAA
DTU Wind Energy, Technical University of Denmark
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Comparing Lidar and SAR. HOW?
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• Colocate in time and space
• Logarithmic wind profile for vertical displacement
• Assuming homogenuity along the coast in the SAR wind
DTU Wind Energy, Technical University of Denmark
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Comparisons of 10m winds
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Assuming homogenuity along the coast
Scanning lidar 10min mean speed
SAR and scanning lidar comaprison at 10m
DTU Wind Energy, Technical University of Denmark
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Scanning lidars and SAR wind – over the transect
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Left: Differences U(10m) DD-SAR wind. Right: Differences U(10m) SC-SAR wind
• 10m wind from the lowest lidar level
• Cases: 10 for DD, 11 for SC
• RMSE: 1.4 m/s DD, 1.8 m/s SC
• Bias: 0.6 m/s DD, 0.2 for SC
DTU Wind Energy, Technical University of Denmark
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Average from 1500m to 5000m
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• Box average of the SAR wind
• Mean over 10m wind from the lowest lidar
• All wind directions
• All stabilities
• RMSE comparable to literature
DTU Wind Energy, Technical University of Denmark
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Average from 1500m to 5000m – easterly wind
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• Box average of the SAR wind
• Mean over 10m wind from the lowest lidar
• Wind from the land
• All stratifications
• Large RMSE
• Internal boundary layer unaccouted
DTU Wind Energy, Technical University of Denmark
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Average from 1500m to 5000m – westerly wind
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• Box average of the SAR wind
• Mean over 10m wind from the lowest lidar
• Wind from the sea
• All stratifications
• RMSE comparable to literature
• Still considerable deviations
DTU Wind Energy, Technical University of Denmark
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Non-logarithmic cases
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• Use 10m wind from all lidar levels
• 10m wind inconsistent
• Non-logarithmic profile
DTU Wind Energy, Technical University of Denmark
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Non-logarithmic cases
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• Use 10m wind from all lidar levels
• 10m wind inconsistent
• Non-logarithmic profile
DTU Wind Energy, Technical University of Denmark
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Average from 1500m to 5000m – westerly wind excluding 2 cases
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• Box average of the SAR wind
• Mean over 10m wind from the lowest lidar
• Wind from the sea
• Excluding inconsistent 10m wind
• Better RMSE
• Even fewer cases
DTU Wind Energy, Technical University of Denmark
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Average from 1500m to 5000m – westerly wind and neutral stratification
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• Box average of the SAR wind
• Mean over 10m wind from the lowest lidar
• Wind from the sea
• Neutral (|L|>500m) at 40m (onshore mast)
• Good RMSE
• Very few cases
DTU Wind Energy, Technical University of Denmark
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Conclusions
• SAR wind retrievals in the coastal zone can work.
• Using Lidar measurements above 10m for comparisons is challenging.
• Cases with westerly winds and neutral stratification agree very well.
Outlook:
• More data from other locations for better statistics.
• More detailed investigation of each case in a case study.
• Use SAR for investigation of coastal gradients.
Acknowledgement:
ESA for the use of Sentinel-1 data
Thanks to ForskEL for funding the RUNE experiment and DTU’s technical staff for the execution
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