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RTFDDA and E-RTFDDA Systems
Wind Energy Prediction - R & D Workshop11-12 May 2010. NCAR/CG1, Boulder, CO
Yubao LiuWill Cheng, Gregory Roux, Yuewei Liu, Luca Delle Monache,
Matt Pocernich, Branko Kosovic, Al Bourgeois, Gerry Wiener, Tom Warner, David Johnson and Bill Mahoney
Thanks to John Exby, Doug Small, Becky Ruttenberg, B. Lambi, B. Myers, A. Fouriers, Tom Hopson and collaborators from NREL and Xcel Energy
Overview1. Challenges for wind analysis and forecasts2. NWP models designed for wind forecasting WRF based RTFDDA Brief description RTFDDA versus Cold-start forecasting Assimilation wind farm data
Ensemble-RTFDDA Brief description Sample results
3. Summary and on-going work
“The Needs” Multi-scale weather Intra-hour, 1 – 6h,
6 – 24h, day ahead accurate wind forecasts
0 – 200m winds(speed, shear, turbulence) Other weather factors:
icing, lightning … Uncertainties Micro-climatography
Meteorology and Climate for Wind Energy
Regional wind resources
Wind plantsiting
Micrositingof turbines
Production Load & Trade
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
The NCAR RTFDDA SystemRTFDDA: Real-Time Four Dimensional Data
Assimilation and forecasting system(Liu et al. 2008a,b JAMC)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
RTFDDARegional-scaleNWP modelsWRF / MM5
MESONETs
GOES
Wind Prof
4-D Continuous Data Assimilation and Forecasts
Radars
Etc.
ACARS
Forecast
Cold start
tFDDA
Weather observations
WRF/MM5
Modified WRF/MM5:
Dx/Dt = ... + GW (xobs – xmodel )
where x = T, U, V, Q, P1, P2 …
W is weight function
All WMO/GTS
Farm MetYuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Weather Data Incorporated in Xcel RTFDDA
Existing weather dataprovide rich flow info aboutwind farms
00UTCMay 10,2010
Advanced Modeling With RTFDDA
WRF-RTFDDA
Climate-FDDA
Ensemble-RTFDDA
RTFDDA-LES
Nesteddown
ClimateDownscaling
ProbabilisticAnalysis & forecasting
Rife et al. 4:20 Liu et al. 11:50
Climate-FDDA
RTFDDA-LES
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Implementation for Xcel Energy Wind Power Prediction
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
The NCAR Wind Forecasting SystemWind Farm Data (status, power generation, etc.)
Input MET Data & Observations(NCEP NAM, GFS, RUC, MOS and observations)
Input Wind Farm Weather Data (Met-tower data and turbine nacelle anemometers)
Linux cluster
Linux cluster
Network LinuxWorkstation
Linux workstation/cluster
OperatorGUI
Meteorologist GUI
Supplemental Wind Farm Data(Met-towers, wind profiler, surface stations, sodarWindcube lidar and other field instruments)
Dynamical Integrated Forecast System
(DICast®) Energy Conversion
& Verification
Deterministic Forecasting(WRF-RTFDDA)
Probabilistic Forecasting(Ensemble-RTFDDA)
Nowcasting system(VDRAS radar analysis)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
(E-)RTFDDA Model Domain Configurations
Deterministic: Grid: 3.3 km
RTFDDA Deterministic Prediction: 3h update cycles 24-72h forecasts15min to 1h outputs
Ensemble: Grid: 10km
(D2E)
(D1E)
E-RTFDDA Probabilistic Prediction: 6h update cycles 72h forecasts 1h outputs
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Xcel RTFDDA Example Forecasts
XXX Farm, CO
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
11 June 2009: Initial Operating Capability
11 November 2009: Interim Operating System
31 May 2010: Ensemble RTFDDA IOC
1 June 2010: Phase-2 starts
Major Milestones of Xcel (E-)RTFDDA
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
WRF-RTFDDA Forecast of 80m-AGL Winds on D3 (3.3 km Grid)
March 9 March 10 March 11 March 12 March 13
13:00 UTCMarch 12, 2010
March 9 March 10 March 11 March 12 March 13
March 9 March 10 March 11 March 12 March 13
March 9 March 10 March 11 March 12 March 13
Observed; Forecasted
Observed; Forecasted
Observed; Forecasted
Observed; Forecasted
Farm
mea
n w
inds
(m/s
)Fa
rm m
ean
win
ds (m
/s)
0
20
0
20
0
20
0
20
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Comparison of WRF-RTFDDA Forecasts with Cold-start Forecasts (XXXX, CO)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Assimilation of Wind Farm Data
Met Tower wind spd/dir
Turbine hubwind spd
Data QCand processing
Data combining and reformat
WRFRTFDDA
All other weather Observations
Other met-towerweather Observations
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
W/O Farm DA
With Farm DA
Gain:32/29/7%
March 2010Days (mm/dd; 00UTC)
Impact on Analysis at XXXX, CO
Note: One of the RTFDDA advantages is to assimilate all available weather data at, near and even far-away from wind farms to get best possible wind farm wind analyses. This study also show further improved wind farm wind analysis using farm-site weather data.
Hub
hei
ght w
ind
spee
d (m
/s)
(Raw Output of Controlled Experiments using RTFDDA)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Impact on Forecasts at XXXX, CO
W/O Farm DA
With Farm DA
Gain:17/20/11%
March 2010Days (mm/dd; 00UTC)
(Gain %: RMSE/MAE/Corr)0 - 3h forecasts
(Raw Output of Controlled Experiments using RTFDDA)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Hub
hei
ght w
ind
spee
d (m
/s)
Impact on Forecasts at XXXX, CO
W/O Farm DA
With Farm DA
Gain:6/8/4%
March 2010Days (mm/dd; 00UTC)
(Gain %: RMSE/MAE/Corr)3 - 6h forecasts
(Raw Output of Controlled Experiments using RTFDDA)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Hub
hei
ght w
ind
spee
d (m
/s)
Ensemble-RTFDDA
Farm Met
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
E-RTFDDA: Example Products
Surface and X-sections – Mean, Spread, Exceedance
Probability, Spaghetti, …
Likelihood for SPD > 10m/s
Mean T & Wind
T Mean and SD
Wind SpeedPDF
T-2m Wind Rose
Pin-point Surface and Profiles – Mean, Spread, Exceedance
probability, spaghetti, Windroses, Histograms …
(For US Army test ranges)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
T RH SPD
30-Member Ensemble Forecasts of Winds and Uncertainties at Xcel Wind Farms
Hei
ght A
GL
(km
)
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Challenges and Opportunities Remain
Imperfect NWPGrid resolutions ParameterizationsInitial conditionsBoundary conditions
Weather ObsAvailability (time lag) RepresentativenessQuality control“Optimal” assimilation
Wind Farm MetAvailability/reliability Quality assuranceOptimal assimilation“Ramps” with new Obs
Power ForecastWind farm power dataModel wind powerProbability/predictability Join stat. approaches?
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research
Summary RTFDDA is a multi-scale, rapid-cycling,
FDDA and forecast system that aims at effectively assimilates observations into full-physics models to produce best-possible high-resolution 4-D synthetic weather.
RTFDDA and its extensions of E-RTFDDA, C-FDDA, and RTFDDA-LES, support the broad need for wind energy applications.
Significant progresses have been made to optimize the model capabilities for Xcel wind forecasting. Continue R&D are necessary to improve the model performances.
The Xcel RTFDDA Modeling Clusters 3.3km RTFDDA(Dell 48x8-cpus) 10km E-RTFDDA
(Dell 32 blades)
Deterministic: 3.3 km
Ensemble: 10km
Yuabo Liu et al. Wind Energy Prediction - R & D Workshop. 11 -12 May 2010 © 2010 University Corporation for Atmospheric Research