A new wind resource map for the North Sea
Combining the strengths of Earth Observation data, Mesoscale Modelling and Mast Measurements
European Offshore Wind, 14 September 2009, StockholmJoe Phillips < [email protected] >
• Background
• Methodology
• Results
Or….
Why ?.... How ?.... What ?....
Contents
Background
Wind Resource is critical
► High enough energy production
► High enough certainty
Onshore measurements
► Relatively inexpensive
► Standard industry practice
Offshore measurements
► Relatively expensive
► Varied industry approach
At an early stage, wind mapping can add value to aid site selection and feasibility
Background
For early stage projects several data sources may be considered
► Published Studies
◪ e.g. European Wind Atlas, UK RE Atlas, GH-GL 1995 EU Study etc
► ReAnalysis Data
► Coastal meteorological stations
► Offshore meteorological stations
► Earth Observation data
► Mesoscale Modelling
► Offshore met masts
Each data source has strengths and weaknesses
► So, why not combine them to …..
◪ Accentuate strengths
◪ Mitigate weaknesses
Method - rationale
Source Strengths Weaknesses
Offshore Met Mast Best absolute accuracy Only for single point
Long-term representation
Earth Observation Wide spatial coverage Low absolute accuracy
Unusable in coastal areas
Limited temporal coverage
Mesoscale Modelling Localised coastal variation Moderate absolute accuracy
IMAGE: ESA
Use as ‘calibration point’ to inject absolute accuracy
Use to characterise broad synoptic spatial trends
Use to establish wind variation close to the coast
CORMA – Composite Offshore Resource Mapping Analysis
Method - overview
EO Analysis(Matrix Averaging)
Mast Analysis(GH Standard Practise)
Synoptic Grid(Calibration)
Meso Model(MC2 – coastal grids)
Final Grid(Quadrant Blending)
Recently utilised for the EC FP7 Project – WindSpeed (www.windspeed.eu)
Matrix Averaging
EO AnalysisEO
Mast
Synoptic Grid
Meso Model
Final Grid
6.5 6.3 5.9
6.7 6.6 6.3
7.1 7.0 7.0
1 0.94 0.98 0.94 0.98 0.91 0.96 0.91 1.04
1.06 1 1.06 0.98 0.96 1.11 1.09 1.04 0.91
1.02 0.94 1 1.06 1.00 1.00 0.91 1.06 0.96
1.06 1.02 0.94 1 0.94 0.96 0.91 0.93 0.94
1.02 1.04 1.00 1.06 1 0.91 1.00 1.11 0.93
1.10 0.90 1.00 1.04 1.10 1 0.96 0.91 0.94
1.04 0.92 1.10 1.10 1.00 1.04 1 1.09 0.94
1.10 0.96 0.94 1.08 0.90 1.10 0.92 1 0.91
0.96 1.10 1.04 1.06 1.08 1.06 1.06 1.10 1
Matrix Averaging
EO AnalysisEO
Mast
Synoptic Grid
Meso Model
Final Grid
9.2 9.9 9.7
8.9 9.0 9.1
- 9.4 9.5
1 1.02 0.94 1.00 1.04 1.11 0.96 0.91
0.98 1 1.09 0.98 0.93 1.00 0.91 0.91
1.06 0.92 1 0.93 1.11 0.94 1.02 0.91
1.00 1.02 1.08 1 0.91 1.06 0.93 1.11
0.96 1.08 0.90 1.10 1 0.94 1.09 0.96
0.90 1.00 1.06 0.94 1.06 1 0.94 1.00
1.04 0.92 1.10 1.10 1.00 1.04 1 1.09 0.94
1
1.10 1.10 1.10 0.90 1.04 1.00 1.02 1
Matrix Averaging
EO AnalysisEO
Mast
Synoptic Grid
Meso Model
Final Grid
1 1.04 0.99 1.00 0.93 1.08 0.94 1.00 1.00
0.97 1 0.96 1.03 1.02 1.00 1.00 1.04 0.99
1.01 1.05 1 0.95 0.96 1.05 0.98 1.00 0.95
1.01 0.97 1.05 1 1.08 1.03 1.00 1.03 1.00
1.08 0.99 1.05 0.93 1 1.05 1.01 1.02 1.08
0.93 1.01 0.95 0.98 0.95 1 0.96 0.97 1.09
1.07 1.01 1.03 1.00 0.99 1.04 1 1.01 0.94
1.01 0.96 1.00 0.98 0.99 1.03 0.99 1 0.98
1.00 1.01 1.05 1.01 0.93 0.92 1.06 1.03 1
1 0.96 0.96 1.04 0.91 1.11 0.94 0.98 1.06
1.04 1 0.93 1.02 0.93 0.94 1.06 1.02 0.98
1.04 1.08 1 1.00 1.02 1.09 0.96 0.94 0.94
0.96 0.98 1.00 1 1.11 0.94 0.98 1.09 0.91
1.10 1.08 0.98 0.90 1 1.02 0.91 0.98 1.04
0.90 1.06 0.92 1.06 0.98 1 1.00 0.94 1.06
1.06 0.94 1.04 1.02 1.10 1.00 1 0.96 0.94
1.02 0.98 1.06 0.92 1.02 1.06 1.04 1 0.93
0.94 1.02 1.06 1.10 0.96 0.94 1.06 1.08 1
11.04
1.00
1.00
0.94
0.94
1.06
1.04
0.96
10.91
1.02
1.04
0.94
1.11
0.98
1.00
1.10
10.93
0.93
0.91
1.00
1.00
1.00
0.98
1.08
11.04
1.04
0.91
1.04
1.06
0.96
1.08
0.96
11.09
1.11
1.11
1
1.06
1.06
1.10
0.96
0.92
11.06
0.98
0.94
0.90
1.00
1.10
0.90
0.94
10.96
0.96
1.02
1.00
0.96
0.90
1.02
1.04
1
11.11
1.00
0.94
1.04
0.93
0.94
0.91
0.90
10.91
1.04
0.98
1.06
1.04
1.00
1.00
1.10
10.93
1.02
1.04
1.04
0.91
1.06
0.96
1.08
11.11
0.98
1.09
1.04
1
0.96
1.02
0.98
0.90
10.98
0.93
1.11
1.08
0.94
0.96
1.02
1.02
11.02
0.91
1.06
0.96
0.96
0.92
1.08
0.98
11.04
1.10
1.00
1.10
0.96
0.90
1.10
0.96
1
1
1 1.09 1.09 1.06 0.93 1.00
0.92 1 0.93 1.04 1.00 1.02
1
0.92 1.08 1 1.09 1.04 0.96
0.94 0.96 0.92 1 0.91 1.04
1.08 1.00 0.96 1.10 1 1.00
1.00 0.98 1.04 0.96 1.00 1
1
1 1.02 0.94 1.00 1.04 1.11 0.96 0.91
0.98 1 1.09 0.98 0.93 1.00 0.91 0.91
1.06 0.92 1 0.93 1.11 0.94 1.02 0.91
1.00 1.02 1.08 1 0.91 1.06 0.93 1.11
0.96 1.08 0.90 1.10 1 0.94 1.09 0.96
0.90 1.00 1.06 0.94 1.06 1 0.94 1.00
1.04 0.92 1.10 1.10 1.00 1.04 1 1.09 0.94
1
1.10 1.10 1.10 0.90 1.04 1.00 1.02 1
Matrix Averaging
EO AnalysisEO
Mast
Synoptic Grid
Meso Model
Final Grid
0.99 1.01 1.001.01 1.00 1.01 0.99 0.98 1.02 0.99 1.01 1.00MEAN 0.99 0.98 1.021.01 1.00 1.01
Long-termnormalised wind map
1 1.04 0.99 1.00 0.93 1.08 0.94 1.00 1.00
0.97 1 0.96 1.03 1.02 1.00 1.00 1.04 0.99
1.01 1.05 1 0.95 0.96 1.05 0.98 1.00 0.95
1.01 0.97 1.05 1 1.08 1.03 1.00 1.03 1.00
1.08 0.99 1.05 0.93 1 1.05 1.01 1.02 1.08
0.93 1.01 0.95 0.98 0.95 1 0.96 0.97 1.09
1.07 1.01 1.03 1.00 0.99 1.04 1 1.01 0.94
1.01 0.96 1.00 0.98 0.99 1.03 0.99 1 0.98
1.00 1.01 1.05 1.01 0.93 0.92 1.06 1.03 1
Mast AnalysisEO
Mast
Synoptic Grid
Meso Model
Final Grid
Standard GH procedures
► Campaign traceability checks
► Raw data screening
► Mast effect corrections
► Long-term adjustment
► Wind shear analysis
Resulting in…..
► Long-term mean wind
► Long-term wind rose
► At hub height level
Calibrate EO Grid to Long-term mean wind speed
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
240 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
270 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
300 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
330 degrees
15%10%5%
0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s0-3 3-6 6-9 >9m/s
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
240 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
270 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
300 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
330 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
240 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
270 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
0
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
300 degrees
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Wind Speed 1 [m/s]
0
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Win
d S
peed
2 [m/s]
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
DataBest Fit
330 degrees
Meso ModelEO
Mast
Synoptic Grid
Meso Model
Final Grid
MC2 Model
► NWP model, run as series of climate simulations
► Aim is to capture local coastal variation
Quadrant Blending
Meso speed-up applied along row
absAF10=EF9(AF10/AF9)
absAF11=absAF10(AF11/AF10)
1 2 3 4 5 6 7 8 9 10 11 12
A
B
C
D
E
F
G
H
I
J
K
L
Where, DN = (1/dx2)+(1/dy
2)+(1/(dx2+dy
2))dx
dy
absAC3 = absAC4((1/dx
2)/DN) + absAD3((1/dy2)/DN) + ED4((1/(dx
2+dy2))/DN)
Meso speed-up applied outwards
(Inverse Distance Weighted Average)
Results – 1. EO Matrix Averaging
Notes on Stage 1
► ERS 1 & 2 Missions
► Matrix Averaging
► Normalised synoptic variation
► Coarse resolution (~25km)
Results – 2. Calibration to Mast
Notes on Stage 2
► FINO-1 as reference node
► Shear analysis to 80m
► LT mean wind speed = 9.8m/s
► Uniform calib. of EO grid
Results – 3. Meso Quadrant Blending
Notes on Stage 3
► Meso quadrant blending
► Final resolution = 5km
► Some noise (just like life !)
► Primarily measurement-based
Validation
Notes on Validation
► 5 published estimates
► Bias = 0.04 m/s
► Mean abs. error = 0.23 m/s
► RMS error = 0.62 m/s
Conclusions
CORMA method introduced
► Composite Offshore Resource Mapping Analysis
► Combining strengths of three data sources
► Measurement-driven technique
(with support from modelling)
Applications
► Wind mapping for new markets
► Site finding and feasibility (+/- 0.5 m/s)
North Sea wind map
► North Sea used as example region
► GH will provide pictured final wind map free of charge
► Including GIS data
► Visit us at stand B0828 !
► NORSEWInD Project to spearhead further development in this field. (www.norsewind.eu)
Acknowledgements
Many thanks to data providers…
► KNMI
► ESA
► NordzeeWind (NZW-MEP)
► BSH
► DONG
► Norwegian Meteorological Institute
And to contributing authors…
► Nick Baldock
► Jerome Jacquemin
► Sam Crawley
► Dan Bacon
