How a state-of-the-art w ind atlas is m ade: The exam ple of the W - - PowerPoint PPT Presentation

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How a state-of-the-art w ind atlas is m ade: The exam ple of the W ind Atlas for South Africa Andrea N. Hahmann (ahah@dtu.dk), Jake Badger, Patrick Volker, Jens Carsten Hansen, Niels G. Mortensen Department of Wind Energy, Technical University

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How a state-of-the-art w ind atlas is m ade: The exam ple of the W ind Atlas for South Africa

Andrea N. Hahmann (ahah@dtu.dk), Jake Badger, Patrick Volker, Jens Carsten Hansen, Niels G. Mortensen Department of Wind Energy, Technical University of Denmark, Risø Campus, Roskilde, Denmark Chris Lennard and Brendan Argent Climate System Analysis Group University of Cape Town, Cape Town, South Africa Final WASA Workshop Windaba, Cape Town 2 November 2016

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Outline

Mesoscale modeling within the WASA project Why do we need downscaling? How was the downscaling done for the WASA project

  • Generalization procedure
  • Validation and comparisons of wind climate

Validation of seasonal and diurnal cycles Microscale downscaling and validation Available products Conclusions

2 03/ 11/ 2016

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W hat is a w ind atlas?

A wind atlas is much more than a simple map containing mean wind speed (or kinetic energy flux) for a region of the Earth

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Andrea Hahmann, 12 Nov 2015

The atm osphere is in constant m otion…

Shown is the wind speed at 10 m AGL, January 1998 snapshots every 6 hours The w ind clim atology is a summary of all these motions

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Num erical W ind Atlas m ethodology Downscaling from global reanalysis data + verification

Microscale m odelling Microscale m odelling Mesoscale m odelling

Global wind data Local surface wind

Global wind resources

Regional wind climate

2 0 0 km × 2 0 0 km 3 km × 3 km

Local surface wind Measurements

Verification 1 – 1 0 m

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W hat kind of processes does the m esoscale m odel sim ulates?

coastal jet valley circulations upslope and download winds sea breezes

Andrea Hahmann, 12 Nov 2015

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W RF Sim ulations for the W ASA project

Simulating 8 years of the wind (on a 3 km x 3 km grid)

  • f South Africa took

~ 6 weeks in our cluster

Andrea Hahmann, 12 Nov 2015

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Once w e run the m odel for a ( pretty) long period

  • f tim e… W e get

How ever…

A very big collection of numbers

Andrea Hahmann, 12 Nov 2015

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I m portance of resolution

Wind resource (power density) calculated at different resolutions 2.5 km 0.1 km 323 W/m2 378 W/m2 505 W/m2 641 W/m2 mean power density of total area mean power density for windiest 50% of area

Badger et al (2011)

average over the light blue to green bits (50% percentile)

  • f the image.

Andrea Hahmann, 12 Nov 2015

meso only meso + micro

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Nature Mesoscale Model

GENERALI ZATI ON W AsP “lib” files

W hy do w e need generalization?

Andrea Hahmann, 12 Nov 2015

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Microscale modelling Microscale modelling

mesoscale model output site conditions

Mesoscale generalisation direct micro corrections only meso & micro corrections

From m esoscale m odel to site conditions

Numerical Wind Atlas

  

Badger, J., H. Frank, A. N. Hahmann and G. Giebel, 2014: Wind climate estimation based on mesoscale and microscale modeling: statistical-dynamical downscaling for wind energy applications. J. Applied Meteorology and Climatology, 5 3 , 1901-1919.

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W RF-based sim ulations

Steps towards the new research-based new numerical wind atlas  Determine optimal model configuration (some learned from previous wind atlases), others are new to WASA project  Run simulations (18 days on a almost fully dedicated cluster; a total of 293 runs; each 6 hours, on 8 nodes)  Data processing – output from simulations are 8Tb!  Generalization and validation  Generation of data products

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Mesoscale & Microscale Meteorology Division / NCAR

Weather, Research and Forecast (WRF) model Complex model with m any options that need to chosen by the user Best configuration not found by chance: Extensive set of year-long simulations were performed to optimize domain size and location and various parameterizations.

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Sensitivity Experim ents

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Forcing reanalysis Boundary layer scheme Radiative param Land use class

One year-long (Oct 2010 – Sep 2011) simulations (5 km x 5 km grid) Compare mean annual wind speed (m/ s) at 100 meters

Convective param Land surface model

| dU| < 0.5 m/ s

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Results from the various sensitivity experim ents

WASA Final Wind Seminar

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WM01 WM02 WM03 WM04 WM05 WM06 WM07 WM08 WM09 WM10 MAE

  • 25.0%
  • 20.0%
  • 15.0%
  • 10.0%
  • 5.0%

0.0% 5.0% 10.0% 15.0% 20.0% ERA CFSR ERA YSU ERA ULCC ERA RRTMG ERA YSU RRTMG ERA PLX (var Z0)

5 km x 5 km grid spacing Error= (Umodel-Uobs)/ Uobs , U= year-long mean generalized wind speed Error reduction by using high-resolution

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Very large (309 x 435) inner grid (3km x 3 km grid spacing) Changes to standard WRF land use and roughness ERA-Interim forcing, 1/ 12 degree SSTs; MYJ PBL; 41 vertical levels (further details in incoming report)

New research w ind atlas: W RF Model Configuration

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Simulations: 8 years for (27/ 9/ 3 km) – 2005-2013; High- resolution SSTs; 24 years (27/ 9 km) – 1990-2013;

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Validation after generalization

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Nature Mesoscale Model

GENERALI ZATI ON W AsP “lib” files

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Mesoscale generalization procedure

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Similar generalization procedure for KAMM and WRF simulations. Wind speeds and directions from WRF simulations are binned according to wind direction, wind speed, and stability (1/ L). Each binned wind class is then generalized and aggregated using their frequency of

  • ccurrence

Neutral or non-neutral assumption was tested

Term modified to account for non- neutral conditions.

Hahmann, A. N., Pena Diaz, A., & Hansen, J. C. (2016). WRF Mesoscale Pre-Run for the Wind Atlas of

  • Mexico. DTU Wind Energy. (DTU Wind Energy E, Vol. E-0126).
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Num erical w ind atlas – W RF 3 km sim ulation

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Generalized wind speed, h= 100 m, z0= 0.03 m

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Microscale m odelling at the 1 0 W ASA m asts Som e background

Wind-climatological inputs

  • Three-years-worth of wind data
  • Five levels of anemometry

Topographical inputs

  • Elevation maps (SRTM 3 data)
  • Simple land cover maps (SWBD +

Google Earth); water + land Preliminary results

  • Microscale modelling verification
  • Site and station inspection
  • Simple land cover classification
  • Adapted heat flux values
  • Wind atlas data sets from 10 sites

This data was used to verify the numerical wind atlas, but not to create them Analysis show prevalence

  • f non-neutral conditions at

the sites.

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Verification of the w ind atlas by m easurem ents

So we can compare the numerical wind atlas GWC that is closest to each mast with the GWC derived from the mast data SDC Cour WM10 GWC NWA GWC Please note:

  • Both sets of GWCs

must have the same attributes i.e.

  • Same height a.g.l.
  • Flat terrain
  • Uniform roughness

(The NWA data was also adjusted so that it was representative over the same period the met mast measurements were taken.)

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The verified num erical w ind atlas

A state-of-the-art wind atlas is verified by measurements The Wind Atlas project is designed from the beginning to include high quality measurements against which the numerical wind atlas could be checked This produces a “Verified Numerical Wind Atlas” So, alongside the mesoscale modelling, the project has a second, parallel, activity:

Numerical wind atlas

Mesoscale modelling Generalised climatological wind climates @ grid points

High quality measurements

Microscale modelling Generalised wind climates @ mast locations

VERIFICATION

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Com parison at specific sites

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WM01 Observed versus numerical wind atlas at 3 sites h= 100 meters, z0= 0.03 m October 2010-September 2013

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Exam ple: W ASA site 1 , far northw est

Observed wind atlas Numerical wind atlas WRF

Weighted (solid) Re-fit (dashed)

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Com parison at specific sites

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WM05 Observed versus numerical wind atlas at 3 sites h= 100 meters, z0= 0.03 m October 2010-September 2013

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Exam ple: W M0 5 , southern coast

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Observed wind atlas Numerical wind atlas WRF

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Com parison at specific sites

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WM10 Observed versus numerical wind atlas at 3 sites h= 100 meters, z0= 0.03 m

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Exam ple: W M1 0 , Eastern cape

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Observed wind atlas Numerical wind atlas WRF

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Observational W ind Atlas

Wind speed at 80 m above ground level WAsP resource grids from Observational Wind Atlas

  • 10 x 10 km 2 grid
  • 100 meter grid spacing
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W ASA w ind resource @ 1 0 0 m – w ind speed

Andrea Hahmann, 12 Nov 2015

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W ind Atlas for South Africa – verification of num erical w ind atlas Modelling versus m easurem ents @ 6 2 m

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Wind speed

  • Slope: 102%
  • Spread: 5.9%

Energy yield

  • Slope: 105%
  • Spread: 12%

wind speed yield

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Seasonal and diurnal cycles in the observations and the W RF sim ulations

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Chris Lennard and Brendan Argent

  • Univ. Cape Town
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Freely available products derived from the num erical w ind atlases

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KAMM/ W AsP W RF-based Product horizontal resolution time resolution Product horizontal resolution time resolution GIS layers 5 km x 5 km climate GIS layers 3 km x 3 km climate WAsP lib files (5 levels) 5 km x 5 km horizontal resolution long-term (30 year) climate WAsP lib files (5 levels) 3 km x 3 km horizontal resolution 8 years climate Time series (1 vertical level) 27 km x 27 km (based

  • n 9 km x 9

km) Hourly 24 years Available May 2014 from WASA web site

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Sum m ary and conclusions

Results from a new verified numerical wind atlas for South Africa are presented Production of the new wind atlas required a large amount of work – many knowledge and software was not available at the inset of the project WRF method, numerically very expensive, gives excellent results

  • Excellent comparison between wind roses in model and observations
  • Stability conditions should be taken into account at generalization

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Presentation name 42 11/ 3/ 2016

W RF Sim ulations for the W ASA project

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Acknow ledgem ents

The Wind Atlas for South Africa (WASA) project is an initiative of the South African Government - Department of Energy (DoE) and the project is co-funded by

  • UNDP-GEF through South African Wind Energy Programme

(SAWEP)

  • Royal Danish Embassy

WASA Project Steering Committee: DoE (chair), DEA, DST, UNDP, Danish Embassy, SANEDI

43 21 Oct 2012