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Downscaling Global Reanalyses with WRF for Wind Energy Resource Assessment

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? .

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Downscaling Global Reanalyses with WRF for Wind Energy Resource Assessment

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  1. DownscalingGlobal Reanalyses with WRFfor Wind Energy Resource Assessment • Mark Stoelinga, • Matthew Hendrickson, and Pascal Storck • 3TIER, Inc.

  2. Wind Resource Assessment • What is the long-term average wind resource at each turbine location within a proposed wind farm?

  3. Wind Resource Assessment • Install “met towers” for a period of ≥ 1 year. 60 m

  4. 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)

  5. Estimating Temporal Variabilityof Wind Resource • How can we extend the short (1-year) record into a long-term mean? • 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

  6. Estimating Temporal Variabilityof Wind Resource • First-Generation Reanalysis Data Sets • (NCAR/NCEP “R1”, ERA-40): Can potentially provide a “synthetic long-term reference station”, but with potential pitfalls • Coarse resolution of underlying model (1.5-2.5 deg) • Flaws/limitations in DA method • Changes in observations over 50 years • Grids available only every 6 h (hourly is preferred)

  7. Estimating Temporal Variabilityof Wind Resource • 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)

  8. 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

  9. 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

  10. 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

  11. 80-m Mean Wind Speed (m s-1) 8 0 R1

  12. 80-m Mean Wind Speed (m s-1) 8 0 CFSR

  13. 80-m Mean Wind Speed (m s-1) 8 0 ERA-Interim

  14. 80-m Mean Wind Speed (m s-1) 8 0 MERRA

  15. 80-m Mean Wind Speed (m s-1) 8 0 R1

  16. 80-m Mean Wind Speed (m s-1) 8 0 R1 downscaled

  17. 80-m Mean Wind Speed (m s-1) 8 0 R1 downscaled

  18. 80-m Mean Wind Speed (m s-1) 8 0 ERA-Interim

  19. 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 1 6 9 4 9 1 1 2

  20. 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

  21. Wind Speed R2 fordownscaled CFSR vs. NCAR/NCEP “R1” Daily Monthly CFSR R2 CFSR R2 N. Amer S.Amer Europe Africa India Austr. R1 R2 R1 R2

  22. Wind Speed R2 fordownscaled ERA-Int vs. NCAR/NCEP “R1” Daily Monthly ERA-Int R2 ERA-Int R2 N. Amer S.Amer Europe Africa India Austr. R1 R2 R1 R2

  23. Wind Speed R2 fordownscaled CFSR vs. raw CFSR Daily Monthly Downscaled CFSR R2 Downscaled CFSR R2 N. Amer S.Amer Europe Africa India Austr. Raw CFSR R2 Raw CFSR R2

  24. 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

  25. 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

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