Offshore wind mapping using synthetic aperture radar and meteorological model data

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Offshore wind mapping using synthetic aperture
radar and meteorological model data

• Iain Cameron
• David Miller
• Nick Walker
• Iain Woodhouse

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Offshore Wind
2 MW Vestas

• UK has largest offshore wind resource in EU
• 10 km off shore 25 more energy than on land
• BUT more expensive than land based technology
• Accurate understanding of wind resource vital

Blade Diameter 90m
Tower Height 80m
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Overview
Synthetic Aperture Radar Data
UKMO Unified Mesoscale Model (UMM)
SAR Wind Inversion
Retrieved Wind
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Envisat ASAR

• ASAR (advanced SAR
• C-band (5.6 cm ?, 5.3 GHz)
• Multiple modes of operation
• Image mode
• High res (12.5 m2)
• Low repeat time (25 days)
• Wide swath
• Medium resolution (75 m2)
• High repeat time (3-5 days)

UMM Data
SAR Scenes
Inversion
Retrieved Wind
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Apriori Data

• UKMO Unified Mesoscale Model (UMM)
• 6 hourly analysis levels
• Interpolated to SAR time
• Interpolated to 2.5 km Grid

UMM Data
SAR Scenes
Inversion
Retrieved Wind
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Forward Model
CMOD5
UMM Data
SAR Scenes
Inversion
Retrieved Wind
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Model Inversion

• a) Image Directions
• Roll vortices/streaks
• Fourier, wavelet, Sobel filters, cross spectra
  analysis
• Not visible in all scenes (60 of cases)
• b) NWP winds
• Always available
• Poor resolution
• Spatial (0.125 deg)
• Temporal (every 6 hrs)

UMM Data
SAR Scenes
Inversion
Retrieved Wind
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Model Inversion
1) Directional Wind Speed Algorithm (DWSA)
UMM Data
SAR Scenes

• Retrieves wind speed assuming NWP wind direction
  is true
• Problems
• Assumes SAR variation only due to wind speed
  changes
• Doesnt account for known retrieval errors

Inversion
Retrieved Wind
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Model Inversion
2) Maximum Aposteriori Probability (MAP)

• Estimates optimal wind vector given the s0 and
  apriori wind vector

UMM Data
SAR Scenes
Inversion
Retrieved Wind

• Apply Gauss-Newton minimisation
• Stabilises within 3-5 iterations

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Sensitivity Analysis

• Generate s0 using wind speeds 5-25 ms-1 and
  directions 0-180o
• Add 5 Gaussian noise to s0
• Retrieve speed

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Validation Results
R2 0.715 RMSE 1.57 m/s
R2 0.576 RMSE 1.7 m/s
UKMO UMM
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Mean Wind Speeds
UKMO UMM
MAP CMOD5
DWSA CMOD5
MAX 16
0
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Speed m/s
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Conclusions Future Directions

• The MAP methodology shows promise for SAR wind
  field retrieval
• BUT there are limitations in the resolution of
  the weather model data
• Future work will
• Introduce SAR wind direction analysis
• Consider the applicability of these data products
  for wind farm planning

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Offshore wind mapping using synthetic aperture
radar and meteorological model data

• Iain Cameron
• David Miller
• Iain Woodhouse

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Sensitivity Analysis
Gaussian Noise on apriori
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Methodology
Hierarchical Inversion Method For Improving
Retrieval Resolution
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Sensitivity Analysis

• CMOD5 shows increased
• saturation effects at
• high speeds

s0
Wind direction relative to antenna