Adam Deppe

1
IMPROVEMENTS IN NUMERICAL PREDICTION OF LOW LEVEL
WINDS

• Adam Deppe

2
Motivation

• U.S. Department of Energy (DOE) goal of having
  20 of the nations electrical energy from wind
  by 2030
• DOE workshop report states  1 error in wind
  speed estimates for a 100-MW wind generation
  facility can lead to losses approaching
  12,000,000 over the lifetime of that plant ..."
   
• To optimize wind for power generation, accurate
  weather forecasts are needed
• Better weather forecasts lead to greater
  confidence and more reliance on wind energy as a
  reliable energy source

3
Wind Speed Forecasting Background

• Typically, weather forecasting done for
  precipitation and temperature, not wind until
  recently
• Meteorologists traditionally have focused wind
  forecasts at the 10 m level, a height strongly
  influenced by surface friction
• Prior wind forecasting research in the western
  United States has focused on flow in complex
  terrain (e.g. Wood 2000, Ayotte et al. 2001)
• Not applicable in Iowa where low-level jets and
  changing surface conditions are likely to be the
  dominant factors
• Statistical approach to predict wind speed at
  different levels (Huang et al. 1996, Kamal et al.
  1997) time series analysis

4
Problem Statement

• With the increased growth in the wind energy
  sector, wind speed forecasts at turbine hub
  height (80 m) are now needed
• Due to the lack of observations, validating
  forecasts at this height has been difficult and
  little attention has been paid to wind forecasts
  at 80 m in the meteorological community
• In this study, an ensemble was created based on
  six different ways to represent drag as well as
  other forecasting techniques to improve wind
  speed forecasting at 80 m

5
PBL Scheme
Fd(z,t)
V
6
PBL Parameterizations Different Ways to
Represent Drag

• PBL schemes were developed to help resolve the
  turbulent fluxes in the boundary layer aka the
  drag force
• Due to the complex nature of boundary layer, very
  difficult to model changes diurnally and
  seasonally
• Smaller BL in winter snow pack
• To parameterize the planetary boundary layer -
  both local and non-local parameterizations are
  used
• Local closure - estimates unknown fluxes using
  known values and/or gradients at the same point
  (Stull 1988)
• Non-local closure - estimates unknown fluxes
  using known values and/or gradients at many
  points in space (Stull 1988)

7
Model Domain

• Weather forecast model tested
• Weather Research and Forecasting (WRF) model
• Model Forecast Period
• 54 hour forecasts first 6 hours not used -
  model spin-up
• Six different ways to Represent Drag Force
• Yonsei University Scheme (YSU) - WRF
• Mellor-Yamada-Janjic (MYJ) - WRF
• Quasi-Normal Scale Elimination PBL (QNSE) -
  WRF
• Mellor-Yamada Nakanishi and Niino Level 2.5
  PBL (MYNN2.5) - WRF
• Mellor-Yamada Nakanishi and Niino Level 3.0
  PBL (MYNN3.0) - WRF
• Pleim PBL scheme (also called Asymmetric
  Convective Model (ACM2)) WRF

Pomeroy Iowa Wind Farm
8
Model Specifics

• Driving model (Initial and lateral boundary
  conditions) - Regional and Global models that
  provide data to limited domain used in the WRF
• Global Forecast System (GFS)
• North American Model (NAM)
• Observation data
• 80 m meteorological tower on the southwest side
  of the Pomeroy, Iowa wind farm
• Data was taken at 10 minute increments and
  averaged over one hour periods centered on each
  hour to match model output
• Evaluation period
• From June 2008 through September 2010, excluding
  periods where missing data was observed

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10
Pre-Run Modification
MAE of three different GFS perturbations using
the YSU and MYNN3.0 PBL schemes from 10 cases in
January 2010
Perturbation Number GFS MYNN 3.0 MAE ( ms-1) GFS YSU MAE ( ms-1) Ensemble MAE ( ms-1)
2 2.34 2.06 2.05
4 2.18 2.04 1.98
15 2.27 2.18 2.08
Ensemble - best model skill
MAE associated with the wind speed at 80 m from
three different initialization times from 10
cases in January 2010
Time Initialization GFS MYNN 3.0 MAE ( ms-1) GFS YSU MAE ( ms-1) Ensemble MAE ( ms-1)
18 UTC 1.88 1.78 1.69
00 UTC 1.82 1.74 1.63
06 UTC 1.83 2.07 1.73
Time Initialization - Higher model skill (lower
MAE) than Perturbations
11
Day Ahead Market
Midnight
Noon
Noon
12
Day Ahead Market
Midnight
Noon
Noon
13
Larger the model spread less confidence in
forecast
Day Ahead Market
Midnight
Noon
Noon
14
Day Ahead Market
Midnight
Noon
Noon
15
Day Ahead Market
Midnight
Noon
Noon
16
Day Ahead Market
Noon
Noon
Midnight
17
Pre-Run Modification Cont.
Highest Model skill associated with 10 km grid
spacing
Computing power limited in most private
companies, running 10 km model runs are much more
feasible than 4 km runs
Grid Spacing GFS MYNN 3.0 MAE ( ms-1) GFS YSU MAE ( ms-1) Ensemble MAE ( ms-1)
10 km 1.82 1.74 1.63
4 km 2.16 1.79 1.73
MAE of wind speed at 80 m from two different grid
spacings (4 km and 10 km) from 10 cases in
January 2010
18
Day Ahead Market
Midnight
Noon
Noon
19
Similar model skill with terrain effects, like
mountains results would be much different
Day Ahead Market
Midnight
Noon
Noon
20
Post-Processing
Training of the model based on day 1 results 15
cases from June 2008 to May 2009
Model Number
00 UTC MYJ GFS - 10 km grid spacing
00 UTC MYJ NAM - 10 km grid spacing
00 UTC Pleim NAM - 10 km grid spacing
00 UTC Pleim GFS - 10 km grid spacing
00 UTC YSU NAM - 10 km grid spacing
00 UTC YSU GFS - 10 km grid spacing
Day 2 Picked ensemble best MAE Day 2 Non-Picked ensemble best MAE Day 2 All Member Ensemble best MAE
5/15 4/15 6/15
Training approach was not a reliable method to
predict wind speed as conditions change from day
to day
Picked Ensemble showed best model skill only
33 of time
Non-Picked Ensemble showed best model skill 27
of time
21
Bias Correction of the Model

• Bias corrections developed from 30 cases (all
  seasons) from June 2008 to Jan. 2010
• Applied to Case study from Oct. 11, 2009 to Nov.
  11, 2009
• Wind Speed bias correction seen as best way to
  improve forecast (green box)
• Non bias correction showed worst results (red box)

22
Day Ahead Market
Model over-prediction Nighttime
Model under-prediction Daytime
Noon
Noon
Midnight
23
Operational Model Development
MAE  MYJ (m/s) MYNN 2.5 (m/s) MYNN 3.0 (m/s) Pleim (m/s) QNSE (m/s) YSU (m/s) Ensemble (m/s)
GFS 00Z 1.59 1.66 1.66 1.52 1.65 1.57 1.48
GFS 18Z 1.68 1.81 1.72 1.61 1.77 1.63 1.58
NAM 00Z 1.67 1.71 1.69 1.63 1.71 1.57 1.56
NAM 18Z 1.66 1.75 1.74 1.60 1.70 1.63 1.57
Green boxes show highest model skill

• Bias corrections for 00Z and 18Z time
  initializations and NAM and GFS initial boundary
  conditions over a period from Aug. 14-28, 2009
• Six schemes that showed best model skill (lowest
  MAE) formed operational model

Member Number PBL Scheme Time Initialization Land Surface Scheme Land Layer Scheme Initial Boundary Conditions
1 ACM2 18 UTC Pleim-Xiu Pleim-Xiu GFS
2 ACM2 18 UTC Pleim-Xiu Pleim-Xiu NAM
3 ACM2 00 UTC Pleim-Xiu Pleim-Xiu GFS
4 YSU 00 UTC Noah Monin-Obukhov NAM
5 YSU 00 UTC Noah Monin-Obukhov GFS
6 MYJ 00 UTC Noah Janjic Eta Monin-Obukhov GFS

• Five out of six scheme either YSU or Pleim both
  non-local turbulent closure schemes
• Four out of six scheme use the 00Z time
  initialization
• Four out of six scheme use the GFS initial
  boundary conditions

24
Operational Model Results
Ensemble MAE after Bias Correction (m/s) MAE Prior to Bias Correction (m/s) Standard Deviation after Correction (m/s)
GFS 00Z 1.67 1.99 0.74
GFS 18Z 1.66 2.05 0.80
NAM 00Z 1.68 1.91 0.67
NAM 18Z 1.70 1.93 0.73
Deterministic Forecast 1.70 1.77 - - -
Operational Model 1.52 1.67 0.98

• Tested over 25 cases during the summer and fall
  of 2010
• Best model skill seen in Operational Model after
  wind speed bias correction (Green Box)
• Largest standard deviation (measure of model
  spread) in operation model ensemble
• Deterministic forecast is the best individual
  model found from the period studied

25
Ramp Events
Sensitive Area Ramp events not important above
or below this area

• Ramp event - changes in wind power of 50 or
  more of total capacity in four hours or less
  (Greaves et al. 2009)
• Approximated using a typical wind turbine power
  curve such that any wind speed increase or
  decrease of more than 3 ms-1 within the 6-12 ms-1
  window in four hours or less was considered a
  ramp
• Fifty eight cases spanning 116 days from June
  2008 through June 2009 were validated Models
  all used GFS initial boundary conditions

26
Ramp Event Results
Event was considered a ramp event if change in
wind power was 50 or more of total capacity in
four hours or less - wind speed increase or
decrease of more than 3 m/s within the 6-12 m/s
PBL Scheme MYJ MYNN 2.5 MYNN 3.0 Pleim QNSE YSU Obs
Ramp-up 23 29 27 19 26 16 35
Ramp-down 23 28 21 14 28 13 31
Total Ramp Events 46 57 48 33 54 29 66
All PBL schemes under-predict number of ramp
events
Number of ramp events during Day 1 for GFS
initial boundary conditions (06-30 hours after
model start up)  
PBL Scheme MYJ MYNN 2.5 MYNN 3.0 Pleim QNSE YSU Obs
Ramp-up 17 25 24 17 26 11 37
Ramp-down 19 22 16 20 23 11 35
Total Ramp Events 36 47 40 37 49 22 72
Fewer Ramp events forecasted on Day 2
Number of ramp events during Day 2 for GFS
initial boundary conditions (30-54 hours after
model start up)
27
Ramp Events Results Cont.
Average amplitude of ramp up and ramp down events
for GFS initial boundary conditions
PBL Scheme MYJ (ms-1) MYNN 2.5 (ms-1) MYNN 3.0 (ms-1) Pleim (ms-1) QNSE (ms-1) YSU (ms-1) Obs (ms-1)
Ramp-up (Day 1) 4.50 4.62 4.75 4.85 4.60 4.67 4.53
Ramp-up (Day 2) 4.54 5.16 5.2 4.56 4.69 4.73 4.01
Ramp-down (Day 1) 3.74 4.62 4.20 4.60 4.31 4.17 4.34
Ramp-down (Day 2) 3.83 4.28 4.46 4.27 4.59 4.43 4.21

• Amplitude was over-predicted by all six PBL
  schemes for ramp-up events
• Obs. show on average over 4m/s ramp event
• If ramp-up event occurred at 6m/s and went to
  10m/s within 4hrs
• Power increase from 216 kW to 1000 kW

28
Sensitive Area Ramp events not important above
or below this area
29

• Three hour averaged diurnal cycle of ramp-up
  events using the midpoint of the ramp event
• Peak at 01Z LLJ related
• Peak at 16Z Growth of BL

• Three hour averaged diurnal cycle of ramp-down
  events using the midpoint of the ramp event
• Less noticeable trend

30
Summary of Results

• To forecast winds, we have focused on drag (six
  different schemes)
• However, explored other methods to improve
  forecasts
• Perturbations of GFS model
• Low skill
• Varied Time Initializations
• High skill
• Grid Spacing
• Little difference as terrain is flat less
  computing power with 10km
• Training of the Model
• Not a reliable method as conditions change from
  day to day
• Bias Correction
• Noticed a diurnal bias in the model data
• Investigated whether other biases existed wind
  speed bias correction
• Combination of techniques yields a model that is
  significantly more skillful

31
Summary of Results

• All six PBL schemes tested underestimated the
  number of ramp-up and ramp-down events
• Average ramp-up events around 4m/s increase
• Increase in power produced from 216 kW to 1000 kW
• For example, caused a blackout in Texas
• Modeled ramp-up events occurred most often
  between 22 UTC and 01 UTC - closely matched
  observed ramp-up events (occurred most frequently
  around 01 UTC)

32
Great Plains Low Level Jet (LLJ)

• LLJs - first described in the late 1930s by
  Goualt (1938) and Farquharson (1939)
• Areas of relatively fast-moving winds in the
  lower atmosphere, LLJs were first studied because
  of their roll in transporting warm, moist air
  from the Gulf of Mexico into the Great Plains,
  leading to convective events (Stensrud 1996)
• Most well-known LLJs occur over the Great Plains
  of the United States, although found around the
  world - Europe, Africa, and Australia (Stensrud
  1996)
• Maximum winds during nocturnal LLJ events over
  the Great Plains are between 10 ms-1 and 30 ms-1
  (Whiteman et al. 1997)

33
Great Plains LLJ Cont.

• Whiteman (1997) classified two years of LLJs in
  northern Oklahoma and discovered that LLJs occur
• 47 of the time during the warm season
• 45 of the time during the cold season
• Whiteman (1997) found approximately 50 of the
  maximum wind speeds during LLJ events occurred
  less than 500 m above the surface
• With the potential for wind turbine hub heights
  to increase from 80 m to 120 m or higher, LLJ
  interaction with wind turbines could largely
  affect the power performance of wind farms
  (Schwartz and Elliot, 2005).

34
Problem Statement

• Few studies have examined the performance of
  forecasting models during LLJ events, or the
  sensitivity to how surface drag is represented in
  models
• In this study, the ability of the WRF model to
  accurately reproduce vertical wind structure
  during LLJ events was evaluated using six
  different drag schemes in the WRF model to
  observations from the Lamont, OK wind profiler
  site

35
Observed Data Location

• Observed data from the U. S. Department of Energy
  ARM project located at Lamont, OK.
• 915-MHz wind profiler - measure wind speeds below
  500 m, unlike the NOAA 404-MHz profilers
• Observed data ranged from 96 m - 2462 m above the
  surface vertical resolution of 60 m
• 30 LLJ cases and 30 non-LLJ cases were chosen
  between June 2008 and May 2010
• Same forecasts model was used as in Pomeroy
  different dates, different heights evaluated,
  different location

36
LLJ Maximum Wind Speeds
All PBL schemes under-predict max LLJ wind speed
Average Maximum LLJ Wind Speed over 30 LLJ events
for GFS initial boundary conditions
  MYJ (ms-1) Pleim (ms-1) YSU (ms-1) QNSE (ms-1) MYNN 2.5 (ms-1) MYNN 3.0 (ms-1) OBS (ms-1)
Maximum LLJ Wind Speed 19.0 18.2 16.3 19.1 18.2 17.9 22.7
Lowest predicted max LLJ wind speed occurred in
YSU scheme
P-values of the YSU PBL scheme vs. other PBL
schemes for maximum LLJ wind speed
Null hypothesis was rejected in favor of the
alternative hypothesis, indicating that the
under-prediction of the wind speed in the YSU PBL
scheme was highly significant
Ho Ha Ho Ha Ho Ha Ho Ha Ho Ha Ho Ha
  MYJ MYNN 2.5 MYNN 3.0 Pleim QNSE
P-value lt 0.0001 lt 0.0001 0.0002 lt 0.0001 lt 0.0001
Null hypothesis - difference between the maximum
LLJ wind speed of the YSU scheme (u1) will be
equal to the maximum LLJ wind speed of the other
PBL schemes (u2)
37
Height of LLJ Maximum Wind Speed
All PBL schemes under-predict height of LLJ max
Average Maximum LLJ Wind Speed over 30 LLJ events
for GFS initial boundary conditions
  MYJ (m) Pleim (m) YSU (m) QNSE (m) MYNN 2.5 (m) MYNN 3.0 (m) OBS (m)
Height of LLJ Maximum 371.2 427.0 538.3 344.5 365.3 340.3 553.0
Highest predicted height of LLJ max occurred in
YSU scheme
P-values of the YSU PBL scheme vs. other PBL
schemes for height of LLJ maximum
Null hypothesis was rejected in favor of the
alternative hypothesis, indicating that the
higher height predicted by the YSU PBL scheme was
highly significant
Ho Ha Ho Ha Ho Ha Ho Ha Ho Ha Ho Ha
  MYJ MYNN 2.5 MYNN 3.0 Pleim QNSE
P-value lt 0.0001 0.0005 lt 0.0001 0.0007 0.0038
Null hypothesis - difference between the height
of LLJ maximum of the YSU scheme (u1) will be
equal to the height of the LLJ maximum of the
other PBL schemes (u2)
38
June 26, 2008 LLJ Maximum
39
LLJ Case Study March 24, 2009Wind Speed
Under-prediction of wind speed maximum in YSU
Scheme
LLJ structure not present in YSU scheme
LLJ feature present in all other PBL schemes
40
Eddy Viscosity
YSU scheme - eddy viscosity value five times
larger than any other scheme
Larger eddy viscosity - more mixing and turbulence
Higher speeds occurred above and below the jet
core, with higher momentum air being mixed closer
to the surface resulting in substantially
weaker LLJ with a higher elevation of the maximum
41
Occurrence of LLJ Maximum

• All six PBL schemes showed the maximum LLJ wind
  speed occurring near or just after midnight
• Observed maximum LLJ wind speeds occurred a
  little later, with dual peaks at 08 UTC (2 am
  LST) and 10 UTC (4 am LST)
• Overall, the PBL schemes appeared to predict the
  timing of the peak LLJ occurrence reasonably well
  with perhaps a small early bias

42
Influences on Wind Energy
96 m Wind speed 1.2 m/s stronger during LLJ
events power increase of 117 kW
LLJ vs. Non-LLJ event comparison
  LLJ (ms-1) Non-LLJ (ms-1)
96 m Wind Speed 6.3 5.1
157 m Wind Speed 9.9 7.8
Speed Shear 5.58 2.91
157 m Wind speed 2.1 m/s stronger during LLJ
events power increase of 496 kW
Speed shear difference between 157 m and 96 m
wind speed - is almost double during LLJ events

• Number of kW to power an average home per day
  50-70 kW
• Speed shear is present at current hub height
  (80m)
• Project this summer will focus on speed shear
  from 10m to 80m and from 40m to 120m (entire
  reach of wind turbine blades)

43
96 and 157 m Wind Speed
All six PBL schemes over-predicted the wind speed
during LLJ events
Bias and MAE associated with 96 m wind speed
forecasts during LLJ events for GFS initial
boundary conditions
96 m MYJ (ms-1) MYNN 2.5 (ms-1) MYNN 3.0 (ms-1) Pleim (ms-1) QNSE (ms-1) YSU (ms-1)
Wind Speed Bias 3.26 4.38 4.14 2.45 3.15 1.80
Wind Speed MAE 4.52 5.48 5.28 4.04 4.38 3.34
YSU scheme showed the lowest MAE, while the
highest was observed with the MYNN 2.5 scheme
All six PBL schemes over-predicted the wind speed
during LLJ events although YSU small positive
bias
Bias and MAE associated with 157 m wind speed
forecasts during LLJ events for GFS initial
boundary conditions
157 m MYJ (ms-1) MYNN 2.5 (ms-1) MYNN 3.0 (ms-1) Pleim (ms-1) QNSE (ms-1) YSU (ms-1)
Wind Speed Bias 3.20 3.66 3.40 1.87 3.15 0.15
Wind Speed MAE 3.80 3.94 3.69 3.14 3.72 2.09
YSU scheme showed the lowest MAE, while the
highest was observed with the MYNN 2.5 scheme
44
Summary of Results - LLJ

• LLJ maximum wind speeds were under-predicted by
  all PBL schemes - largest under-prediction
  occurred with the YSU scheme larger drag
  present
• All the PBL schemes except the YSU scheme
  under-predicted the height of the LLJ maximum by
  more than 125 m
• YSU scheme - likely cause of the under-predicted
  wind speed and higher jet elevation - result of
  the strong eddy viscosity occurring during stable
  conditions
• Increased mixing - LLJs in the YSU scheme were
  substantially under-predicted and momentum was
  spread out over a deeper layer of the atmosphere
• Timing or temporal trends of the LLJ maximum -
  models had wind speed maxima occurring near or
  just after midnight (06-08 UTC), typically a few
  hours before observed LLJs (08-10 UTC)

45
Summary of Results - LLJ

• LLJ impacts at 96 m and 157 m - increased wind
  speeds and speed shear during LLJ events compared
  to non-LLJ events
• Implies that wind production would increase
  during LLJ events however, wind turbine
  durability would need to be improved to
  accommodate the increased shear
• At 96 and 157 m, the YSU PBL scheme showed
  significantly better skill (lower MAE) than the
  other schemes

46
General Conclusions

• Non-local PBL schemes appear to show better model
  skill overall, however no one scheme is the
  answer for predicting low level winds
• Example - YSU
• High model skill at predicting 80 m wind speeds
• High model skill during LLJ events at 96 m and
  157 m
• Ramp events are poorly predicted
• LLJ max wind speeds are significantly
  under-predicted
• As a result, no one scheme performed
  considerably better than any other and all showed
  room for improvement.

47
Questions?
48
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