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

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Presentation on theme: "Downscaling Global Reanalyses with WRF for Wind Energy Resource Assessment Mark Stoelinga, Matthew Hendrickson, and Pascal Storck 3TIER, Inc."— Presentation transcript:

1 Downscaling Global Reanalyses with WRF for 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 Variability of 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

6 Estimating Temporal Variability of 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 pitfalls 1.Coarse resolution of underlying model ( deg) 2.Flaws/limitations in DA method 3.Changes in observations over 50 years 4.Grids available only every 6 h (hourly is preferred)

7 Estimating Temporal Variability of 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)

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 mean wind 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 ) R1 8 0

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

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

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

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

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

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

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

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

20 Regional Runs at Tower Sites Towers provide hourly data for periods ranging from 1 – 8 years. Wind speed error metrics R 2 and MAE were calculated for WRF time series at the tower sites at hourly, daily, monthly, and yearly time scales

21 Wind Speed R 2 for downscaled CFSR vs. NCAR/NCEP “R1” DailyMonthl y R1 R 2 CFSR R 2 N. Amer S.Amer Europe Africa India Austr.

22 Wind Speed R 2 for downscaled ERA-Int vs. NCAR/NCEP “R1” DailyMonthl y R1 R 2 ERA-Int R 2 N. Amer S.Amer Europe Africa India Austr.

23 Wind Speed R 2 for downscaled CFSR vs. raw CFSR DailyMonthl y Raw CFSR R 2 Downscaled CFSR R 2 N. Amer S.Amer Europe Africa India Austr.

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 1 st -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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