90 s. MS-Micro. Primitives NOABL. high. low. CPU / Information/ Global Market
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2 s MS-Micro Primitives NOABL low high CPU / Information/ Global Market
3 European Wind Atlas flatland 90 s Danish Revolution Risoe MS-Micro Primitives NOABL low high CPU / Information/ Global Market
4 2010 CFD 2000 Migration Mesoscale Modeling European Wind Atlas flatland 90 s Danish Revolution Risoe MS-Micro Primitives NOABL low high CPU / Information/ Global Market
5 CFD 2010 Coupling CFD 2000 Mesoscale Modeling Migration Mesoscale Modeling European Wind Atlas flatland 90 s Danish Revolution Risoe MS-Micro Primitives NOABL low high CPU / Information/ Global Market
6 CFD 2010 Coupling CFD 2000 Mesoscale Modeling SEAMLESS Mesoscale Modeling NCAR Migration Mesoscale Modeling European Wind Atlas flatland 90 s Danish Revolution Risoe MS-Micro Primitives NOABL low high CPU / Information/ Global Market
7 Can Mesoscale models reach the Microscale? Alex Montornes & Pau Branko EWEA Wind Resource Assessment Workshop Helsinki, June 2015
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9 Background: Why Mesoscale Modeling? DYNAMIC: 4D vision (x,y,z time) REAL conditions (radiation, clouds, surface...) LONG-TERM retrospective scan High RELIABILITY capturing the mean flow features
10 Background: Why Mesoscale Modeling? DYNAMIC: 4D vision (x,y,z time) REAL conditions (radiation, clouds, surface...) LONG-TERM retrospective scan High RELIABILITY capturing the mean flow features Too DISSIPATIVE: features < Δx are unresolved PBL PARAMETERIZATIONS: turbulence not resolved SCALE: effects smaller than Δx cannot be described
11 Background: Why Mesoscale Modeling? DYNAMIC: 4D vision (x,y,z time) REAL conditions (radiation, clouds, surface...) LONG-TERM retrospective scan High RELIABILITY capturing the mean flow features Too DISSIPATIVE: features < Δx are unresolved PBL PARAMETERIZATIONS: turbulence not resolved SCALE: effects smaller than Δx cannot be described SUB-GRID process RESOLVE turbulence ( larger than a scale) SCALE: higher resolution
12 Background: Why Mesoscale Modeling? DYNAMIC: 4D vision (x,y,z time) REAL conditions (radiation, clouds, surface...) LONG-TERM retrospective scan High RELIABILITY capturing the mean flow features Too DISSIPATIVE: features < Δx are unresolved PBL PARAMETERIZATIONS: turbulence not resolved SCALE: effects smaller than Δx cannot be described Y D D E S N IO T A UL SUB-GRID process RESOLVE turbulence ( larger than a scale) E G R SCALE: higher resolution LA SIM
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14 Turbulent = Resolved + Unresolved flow large eddies small eddies LES SGS Moeng (WRF workshop, 2011)
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16 Background: can we go further down and get better results Is WRF-LES a suitable approach under real scenarios? What can WRF-LES do for the wind energy industry? Is a feasible solution for operational use?
17 Experiments Sites Observations 3 km Parameterization 110 m LES 30 m LES Time Coherence Mean Flow Intensity of Turbulence Spectrum
18 Experiments: Results WRF-LES in realdaily cases values WRF PBL 3 km WRF LES 100 m MAE m/s R2 hourly MAE m/s R2 hourly Site 1 1 year Site 2 1 year Site 3 1 month Site 4 1 month Site 5 1 month
19 Experiments: Results WRF-LES 110 m experiences a well-defined day/night turbulence pattern
20 Experiments: Results
21 Experiments: Results WRF-LES 110 m improves the TI-WS relationship for low and mid wind speeds WRF-LES 110 m tends to produce laminar flows at high wind speeds
22 Experiments: Results WRF-PBL 3 km underestimates the energy of the eddies faster than 1-2 hours
23 Experiments: Results WRF-PBL 3 km underestimates the energy of the eddies faster than 1-2 hours WRF-LES 110 m follows the expected 5/3 slope at all scales of the inertial range
24 Outcomes Slide 8-9: Comments on the results Promising TI-<WS> Metrics Low TI for high WS Unrealistic peaks High intraminute variations Turbulence resolved Energy cascade
25 Can Mesoscale models reach the Microscale? Not yet as we would dream of but certainly LES is (the) way
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