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Offshore wind mapping using synthetic aperture radar and meteorological model data

Iain CameronDavid MillerNick WalkerIain Woodhouse

SCHOOL of GEOSCIENCES

Offshore Wind

• 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 Diameter90m

TowerHeight 80m

2 MW Vestas

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Overview

Retrieved Wind

Synthetic Aperture Radar

Data

UKMO Unified Mesoscale Model (UMM)

SAR Wind Inversion

Analysis cells

Hilbre Island

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Retrieved Wind

SAR Scenes

UMM Data

Inversion

Envisat ASAR

• ASAR (advanced SAR• C-band (5.6 cm λ, 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)

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Apriori Data

Retrieved Wind

SAR Scenes

UMM Data

Inversion

• UKMO Unified Mesoscale Model (UMM)

• 6 hourly analysis levels– Interpolated to SAR time– Interpolated to ~2.5 km Grid

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Forward Model

Retrieved Wind

SAR Scenes

UMM Data

Inversion

NRCS (σ0)Wind direction +Wind speed

GMF

=

CMOD5

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Model Inversion

Retrieved Wind

SAR Scenes

UMM Data

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)

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Model Inversion

Retrieved Wind

SAR Scenes

UMM Data

Inversion

Retrieves wind speed assuming NWP wind direction is true

Problems

•Assumes SAR variation only due to wind speed changes

•Doesn’t account for known retrieval errors

1) “Directional Wind Speed Algorithm” (DWSA)

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Model Inversion

Retrieved Wind

SAR Scenes

UMM Data

Inversion

•Estimates optimal wind vector given the σ0 and apriori wind vector

1 1

ˆ=minTT

a a a e

0 0x x x S x x y f x S y f x

Observation Term

AprioriTerm

2) Maximum Aposteriori Probability (MAP)

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

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Sensitivity Analysis

• Generate σ0 using wind speeds 5-25 ms-1 and directions 0-180o

• Add 5% Gaussian noise to σ0

• Retrieve speed

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Validation Results

R2= 0.715RMSE = 1.57 m/s

R2= 0.576RMSE = 1.7 m/s

UKMO UMM

R2= 0.609RMSE = 2.28 m/s

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Mean Wind Speeds

UKMO UMM MAP CMOD5DWSA CMOD5

0 10Speed 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 CameronDavid MillerIain Woodhouse

SCHOOL of GEOSCIENCES

Sensitivity Analysis

Gaussian Noise on apriori

Why the high speed, Downwind bias?

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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

Wind direction relative to antenna

σ0