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Downscaling Global Reanalyses with WRF for Wind Energy Resource Assessment Mark Stoelinga, Matthew Hendrickson, and Pascal Storck 3TIER, Inc.

Downscaling Global Reanalyses with WRF for Wind Energy Resource Assessment

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Downscaling Global Reanalyses with WRF for Wind Energy Resource Assessment. Mark Stoelinga , Matthew Hendrickson, and Pascal Storck 3TIER, Inc. Wind Resource Assessment. What is the long-term average wind resource at each turbine location within a proposed wind farm? . - PowerPoint PPT Presentation

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Page 1: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

DownscalingGlobal Reanalyses with WRF

for Wind Energy Resource Assessment

Mark Stoelinga,Matthew Hendrickson, and Pascal Storck

3TIER, Inc.

Page 2: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Wind Resource Assessment

What is the long-term average wind resource at each turbine location within a proposed wind farm?

Page 3: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Wind Resource Assessment

Install “met towers” for a period of ≥ 1 year.

60 m

Page 4: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Wind Resource Assessment

Need to extend the observed information, both • spatially (around proposed windfarm) and• temporally (to estimate long-term mean from 1 year of measurements)

Page 5: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Estimating Temporal Variabilityof Wind Resource

How can we extend the short (1-year) record into a long-term mean? 1. Conventional approach

Identify a nearby, long-term, routine 10m wind observation (“reference station”) that correlates well with the 1-year tower measurement. Use linear regression to craft a relationship between reference site and tower, and then predict long-term mean at tower -> MCP

Page 6: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Estimating Temporal Variabilityof Wind Resource

2. First-Generation Reanalysis Data Sets(NCAR/NCEP “R1”, ERA-40): Can potentially provide a “synthetic long-term reference station”, but with potential pitfalls1. Coarse resolution of underlying model (1.5-2.5

deg)2. Flaws/limitations in DA method3. Changes in observations over 50 years4. Grids available only every 6 h (hourly is

preferred)

Page 7: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Estimating Temporal Variabilityof Wind Resource

3. Downscaling of Reanalysis Data Sets with a Mesoscale Model• Foundation: a mesoscale model can produce good climatology of local surface wind if provided with appropriate large-scale flow conditions.

• Model can “fill in” at hourly frequency• Model can also provide multiple predictors to inform a statistical relationship between observations and the synthetic long-term reference (e.g., MOS)

Page 8: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

2nd-Generation Reanalyses(CFSR, ERA-Interim, MERRA)

• 33-year record, entirely during satellite era• high-resolution (~0.5 degrees)• modern DA methodologies (4DVAR, or much better 3DVAR)

• Direct assimilation of satellite radiances

Page 9: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

2nd-Generation Reanalyses(CFSR, ERA-Interim, MERRA)

Questions:• Do these new reanalysis data sets result in more accurate downscaled retrospective simulations?

• Are the reanalyses so good that we don’t need to downscale?

Will look at:• global maps• validation of regional runs at individual met towers

Page 10: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Global 80-m long-term meanwind maps

• NCAR/NCEP “R1” Reanalysis• R1 w/ WRF downscaling

• 3TIER “FirstLook” data set• Completed 2008, 5-km / 10-year global land coverage, WRF 2.2, YSU PBL, simple land surface

• CFSR• ERA-Interim• MERRA

Page 11: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

R1

8

0

Page 12: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

CFSR

8

0

Page 13: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

ERA-Interim

8

0

Page 14: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

MERRA

8

0

Page 15: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

R1

8

0

Page 16: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

R1 downscaled

8

0

Page 17: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

R1 downscaled

8

0

Page 18: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

80-m Mean Wind Speed (m s-1)

ERA-Interim

8

0

Page 19: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Regional Runs at Tower Sites• 4.5-km WRF runs, V3.0• PBL: YSU or MYJ; LSM: simple or Noah; grid nudging• 3-day runs strung together continuously for multiple years

9

2

1

1

16 9 4

Page 20: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Regional Runs at Tower Sites• Towers provide hourly data for periods ranging

from 1 – 8 years.• Wind speed error metrics R2 and MAE were

calculated for WRF time series at the tower sites at hourly, daily, monthly, and yearly time scales

Page 21: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Wind Speed R2 fordownscaled CFSR vs. NCAR/NCEP “R1”

Daily Monthly

R1 R2 R1 R2

CFS

R R

2

CFS

R R

2

N. AmerS.AmerEuropeAfricaIndiaAustr.

Page 22: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Wind Speed R2 fordownscaled ERA-Int vs. NCAR/NCEP “R1”

Daily Monthly

R1 R2 R1 R2

ERA

-Int R

2

ERA

-Int R

2

N. AmerS.AmerEuropeAfricaIndiaAustr.

Page 23: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Wind Speed R2 fordownscaled CFSR vs. raw CFSR

Daily Monthly

Raw CFSR R2 Raw CFSR R2

Dow

nsca

led

CFS

R R

2

Dow

nsca

led

CFS

R R

2

N. AmerS.AmerEuropeAfricaIndiaAustr.

Page 24: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Conclusions• Several new 33+ -year reanalysis data sets

with ~0.5° resolution have recently become available for general use

• New reanalyses show improved performance when used to drive downscaled WRF retrospective simulations for wind energy assessment

• Although resolution and DA have been improved compared to 1st-generation reanalyses, considerable value is still added with WRF downscaling

Page 25: Downscaling Global  Reanalyses  with WRF for Wind Energy Resource Assessment

Caveats about new reanalyses• ERA-Interim and MERRA lag real time by a

few months• Mostly “WRF-ready”, though MERRA requires

some work (HDF4 file format)• Freely available• CFSR not consistently produced after Jan

2011