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