downscaling global reanalyses with wrf for wind energy resource assessment
DESCRIPTION
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 PresentationTRANSCRIPT
DownscalingGlobal 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?
Wind Resource Assessment
Install “met towers” for a period of ≥ 1 year.
60 m
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)
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
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)
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)
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
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
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
80-m Mean Wind Speed (m s-1)
R1
8
0
80-m Mean Wind Speed (m s-1)
CFSR
8
0
80-m Mean Wind Speed (m s-1)
ERA-Interim
8
0
80-m Mean Wind Speed (m s-1)
MERRA
8
0
80-m Mean Wind Speed (m s-1)
R1
8
0
80-m Mean Wind Speed (m s-1)
R1 downscaled
8
0
80-m Mean Wind Speed (m s-1)
R1 downscaled
8
0
80-m Mean Wind Speed (m s-1)
ERA-Interim
8
0
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
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
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.
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.
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.
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
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