assessing wrf pbl schemes for wind energy...

NATIONAL CENTER FOR ATMOSPHERIC RESEARCH

Branko Kosović,Yubao Liu,

Youwei Liu, Will Cheng

NCAR WorkshopMay 12, 2010

Assessing WRF PBL Schemes for Wind Energy Applications

© 2009, University Corporation for Atmospheric Research. All rights reserved.

In the Past PBL Parameterizations Have Not Been Evaluated with Respect to Wind Forecasting at 80m

Accurate representation of internal PBL processes and PBL interaction with surface and upper troposphere is important for accurate wind forecasting in PBL.

We are trying to determine optimal PBL parameterization configuration for wind forecasting:• PBL scheme, • vertical resolution, and • input parameters.

We focus on PBL parameterizations available in WRF.


We Need to Identify and Address Limitations of PBL Schemes that Impact Wind Forecasting

When are wind forecast errors largest?

How can we improve performance of PBL schemes?

• daytime or nightime• are there seasonal differences• under what synoptic conditions, etc.

• through improved representation of physical processes

• by reducing uncertainty in model parameters• better accounting for uncertainties in model

parameters

What is the level of uncertainty in external forcing?



Surface Weather Map March 5, 2010

Lake Benton

Wildorado

High and Low Temperatures March 5, 2010


During Several Days at the Beginning of March We Observed Significant Under Prediction of Wind Speed


Qualitative Comparison with Data from A Profiler Shows that Upper Level Winds are Accurately Predicted

Hei

ght [

m]

24

20

16

8

0

12

4

4000

3000

2000

1000

03 4 5 6 7

March 2010

Wildorado Profilerm/s


Hei

ght [

m]


3 4 5 6 7March 2010

24

20

16

8

0

12

4

4000

3000

2000

1000

0

Wildorado Wind Speed Forecast m/s


Operational Forecast for XXXX Wind Farm Underestimated Wind Speed Between March 3 and 7



Hei

ght [

m]

3 4 5 6 7March 2010

4000

3000

2000

1000

0

24

20

16

8

0

12

4

m/sXXXX Profiler


March 2010

Hei

ght [

m]


3 4 5 6 7

4000

3000

2000

1000

0

24

20

16

8

0

12

4

Wind Speed Forecast m/s


We used several PBL parameterizations available in WRF in our SCM study

• Yonsei University - YSU• Melor-Yamada-Janic - MYJ• Melor-Yamada-Nakanishi-Niino – MYNN (2.5)• Melor-Yamada-Nakanishi-Niino – MYNN3• Quasi Normal Scale Elimination – QNSE

Initial conditions and forcing were derived from the operational NCEP GFS (1 deg) analysis with 6h data frequency to force SCM simulations

We used 56 and 84 grid points in vertical direction© 2009, University Corporation for Atmospheric Research. All rights reserved.

Hei

ght [

m]

March 2010

Temperature from SCM with YSU Parameterization and 56 Grid Points at XXXX Wind Farm

3 4 5 6 7

800

600

400

200

0


Hei

ght [

m]

March 2010

Wind Speed from SCM with YSU Parameterization and 56 Grid Points at XXXX Wind Farm

3 4 5 6 7

800

600

400

200

0


Hei

ght [

m]

3 4 5 6 7March 2010

Wind Speed Difference Between SCM Simulations with YSU and MYJ Schemes are Negligible800

600

400

200

0

m/s1.0

0.5

0.0

-0.5

-1.0


Wind Speed Difference Between SCM Simulations with YSU and MYNN Schemes are Small

Hei

ght [

m]

3 4 5 6 7March 2010

800

600

400

200

0

m/sm/s1.0

0.5

0.0

-0.5

-1.0


Wind Speed Difference Between SCM Simulations with YSU and MYNN3 Schemes are Negligible

Hei

ght [

m]

March 2010

m/s800

600

400

200

0

1.0

0.5

0.0

-0.5

-1.03 4 5 6 7


Wind Speed Difference Between SCM Simulations with YSU and QNSE are Small

Hei

ght [

m]

March 2010

1.0

0.5

0.0

-0.5

-1.0

800

600

400

200

03 4 5 6 7

m/s


GFS Analysis Does Not Include Snow Cower Over the Domain of Interest at the Beginning of March

During the time of interest near Lake Benton snow cover was estimated at ~20in.However, without snow cover the grassland surface roughness, z0=0.5, while for snow cover it is significantly lower, z0=0.01.We modified input data to account for snow cover and rerun SCM simulations.

Wind Speed Difference Between SCM Simulations with YSU Scheme with and without Snow Cover

Hei

ght [

m]

March 2010

2.0

1.0

0.0

-1.0

-2.0

800

600

400

200

03 4 5 6 7


Wind Speed Difference Between SCM Simulations with High-Resolution YSU with and YSU

Hei

ght [

m]

March 2010

800

600

400

200

03 4 5 6 7

3.0

1.5

0.0

-1.5

-3.0

m/s


Win

d S

peed

[m

/s]

Comparison of Measured Wind Speed at XXXX on with SCM Prediction with YSU Scheme

3 4 5 6 7March 2010

Solid line – measurementsDotted line – SCM 14

12

10

6

0

8

2

4


March 2010

Hei

ght [

m]

Temperature from SCM with YSU Parameterization and 56 Grid Points at XXXX Wind Farm

800

600

400

200

03 4 5 6 7


March 2010

Hei

ght [

m]

Wind Speed from SCM with YSU Parameterization and 56 Grid Points at XXXX Wind Farm

800

600

400

200

03 4 5 6 7


March 2010

Hei

ght [

m]

Wind Speed Difference Between SCM Simulations with YSU and MYJ at XXXX Wind Farm800

600

400

200

03 4 5 6 7

m/s1.0

0.5

0.0

-0.5

-1.0


Win

d S

peed

[m/s

]

Solid line – measurementsDotted line – SCM 14

12

10

6

0

8

2

4

3 4 5 6 7March 2010

Comparison of Measured Wind Speed at XXXX with SCM Prediction with YSU Scheme


Summary and Next Steps• SCM captures southerly LLJ but the magnitude of the wind at turbine

hub is significantly underestimated

• Little difference between SCM simulations with different PBL schemes

• SCMs over predict 10m winds while under predicting hub-height winds

• It is important to assimilate reliable, quality controlled local data not assimilated in large scale forecasts (analysis)

• By correctly accounting for surface roughness and by increasing vertical resolution we reduced the error in wind speed prediction by 1.5 m/s

• More and better data are needed to further study and better understand PBL processes that affect LLJ

• We will further analyze surface layer parameterizations and PBL parameterizations and their interaction© 2009, University Corporation for Atmospheric Research. All rights reserved.

Hypothesis: Accurate representation of internal PBL processes and PBL interaction with surface and upper troposphere is important for accurate wind forecasting in PBL

Objective: Improved wind forecasting in PBL

Steps:• Analyze simple PBL parameterizations w.r.t. wind

forecasting below 300m • Analyze PBL parameterizations in WRF• Analyze how PBL parameterization(s) affect wind

forecasting in WRF

For Wind Energy Applications We Need to Improve Wind Forecasting in Planetary Boundary Layer (PBL)


During Several Days at the Beginning of March We Observed Significant Under Prediction of Wind Speed


A number of reasons exists to have a realistic representation of the boundary layer in a large scale model:

From A. Beljaars’ ECMWF training course on BLs

In the Past PBL Parameterizations Have Not Been Evaluated with Respect to Wind Forecasting at 80m

• The large-scale budgets of momentum heat and moisture are considerably affected by the surface fluxes on time scales of a few days.

• Model variables in the boundary layer are important model products.

• The boundary layer interacts with other processes e.g. clouds and convection.

To Improve PBL Parameterizations We Need to Answer Some of Following Questions

What is the level of uncertainty in external forcing?

What is the level of uncertainty in representation of internal processes (or parameters)?

Are there processes that are not represented at all or that are not represented accurately?

Which processes and parameters affect wind forecasting the most?

What can we do to improve representation of these processes or reduce uncertainty in parameters toward improving wind forecasting in PBL?


Surface Weather Map March 4, 2010

Lake Benton

Wilderado


March 2010

Wind Speed Difference Between SCM Simulations with YSU and QNSE at XXXX Wind Farm

800

600

400

200

0

Hei

ght [

m]


assessing wrf pbl schemes for wind energy...

Documents