Transcript DESIGN OF A METEOROGOLOGICAL ENSEMBLE FOR …

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U.S. Department of Energy (DOE) goal of having 20% of the nation’s
electrical energy from wind by 2030
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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 ..."
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To optimize wind for power generation, accurate weather forecasts
are needed
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Better weather forecasts lead to greater confidence and more
reliance on wind energy as a reliable energy source
• 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
• 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
¶V
1
= -(V ·Ñ)V - Ñp - g - 2W´V + Ñ·(kÑV ) - Fd
¶t
r
“PBL Scheme”
Fd(z,t)
V
• 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)
Weather forecast model tested
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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
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Yonsei University Scheme (YSU) - WRF
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Mellor-Yamada-Janjic (MYJ) - WRF
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Quasi-Normal Scale Elimination PBL (QNSE) - WRF
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Mellor-Yamada Nakanishi and Niino Level 2.5 PBL (MYNN2.5) - WRF
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Mellor-Yamada Nakanishi and Niino Level 3.0 PBL (MYNN3.0) - WRF
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Pleim PBL scheme (also called Asymmetric Convective Model (ACM2)) – WRF
Pomeroy Iowa Wind Farm
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Driving model (Initial and lateral boundary conditions) - Regional and
Global models that provide data to limited domain used in the WRF
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Observation data
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Global Forecast System (GFS)
North American Model (NAM)
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
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From June 2008 through September 2010, excluding periods where missing
data was observed
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
Day Ahead Market
Noon
Midnight
Noon
Day Ahead Market
Noon
Midnight
Noon
Larger the model spread – less
confidence in forecast
Day Ahead Market
Noon
Midnight
Noon
Day Ahead Market
Noon
Midnight
Noon
Day Ahead Market
Noon
Midnight
Noon
Day Ahead Market
Noon
Midnight
Noon
Highest Model skill
associated with 10
km grid spacing
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
Computing power
limited in most
private companies,
running 10 km
model runs are
much more feasible
than 4 km runs
Day Ahead Market
Noon
Midnight
Noon
Similar model skill – with terrain
effects, like mountains – results would
be much different
Day Ahead Market
Noon
Midnight
Noon
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
Picked Ensemble –
showed best model skill
only 33% of time
Non-Picked Ensemble –
showed best model skill
27% of time
Training approach was not a
reliable method to predict
wind speed as conditions
change from day to day
Bias
Corrections using
GFS driving
Model (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)
No Bias
2.34
2.49
2.41
2.36
2.45
2.28
2.27
Diurnal Cycle
2.29
2.33
2.28
2.27
2.30
2.21
2.18
Wind Direction
2.27
2.27
2.26
2.29
2.28
2.24
2.17
Wind Speed and
Direction
2.15
2.16
2.14
2.17
2.17
2.10
2.05
Wind Speed
2.05
2.04
2.01
2.09
2.07
1.99
1.97
Best
Improvement
.29 m/s
.45 m/s
.40 m/s
.27 m/s
.38 m/s
.29 m/s
.30 m/s
% of
Improvement
14.1%
22.1%
20.0%
13.0%
18.4%
14.6%
15.2%
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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)
Day Ahead Market
Model over-prediction
Nighttime
Model under-prediction
Daytime
Noon
Midnight
Noon
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
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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 MoninObukhov
GFS
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Green boxes show
highest model skill
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
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
1.70
1.77
---
1.52
1.67
0.98
Deterministic
Forecast
Operational
Model
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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
Sensitive Area – Ramp events
not important above or
below this area
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Ramp event - changes in wind power of 50% or more of total capacity in four
hours or less (Greaves et al. 2009)
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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
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Fifty eight cases spanning 116 days from June 2008 through June 2009 were
validated – Models all used GFS initial boundary conditions
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
Rampdown
19
22
16
20
23
11
35
Total Ramp
Events
36
47
40
37
49
22
72
Number of ramp events during Day 2 for GFS initial
boundary conditions (30-54 hours after model start up)
Fewer Ramp events
forecasted on Day 2
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
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– Power increase from 216 kW to 1000 kW
Sensitive Area – Ramp events
not important above or below
this area
Diurnal Cycle (Midpoint of Ramp-Up)
9
• 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
8
Frequency
7
MYJ
6
MYNN2.5
5
MYNN3.0
4
PLEIM
3
2
QNSE
1
YSU
OBS
0
1
4
7
10
13
16
19
22
Time (Z)
Diurnal Cycle (Midpoint of Ramp-Down)
9
8
MYJ
6
Frequency
• Three hour averaged
diurnal cycle of ramp-down
events using the midpoint
of the ramp event
• Less noticeable trend
7
MYNN2.5
5
MYNN3.0
4
PLEIM
3
QNSE
2
YSU
1
OBS
0
1
4
7
10
13
Time (Z)
16
19
22
• 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
• 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)
• LLJs - first described in the late 1930’s 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)
• 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).
• 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
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Observed data from the U. S.
Department of Energy ARM project
located at Lamont, OK.
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915-MHz wind profiler - measure wind
speeds below 500 m, unlike the NOAA
404-MHz profilers
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Observed data ranged from 96 m 2462 m above the surface; vertical
resolution of 60 m
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30 LLJ cases and 30 non-LLJ cases were
chosen between June 2008 and May
2010
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Same forecasts model was used as in
Pomeroy – different dates, different
heights evaluated, different location
Average Maximum LLJ Wind Speed over 30 LLJ events
for GFS initial boundary conditions
Maximum
LLJ Wind
Speed
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)
19.0
18.2
16.3
19.1
18.2
17.9
22.7
P-values of the YSU PBL scheme vs. other PBL schemes for
maximum LLJ wind speed
Ho: ; Ha:
P-value
MYJ
MYNN 2.5
MYNN 3.0
< 0.0001
< 0.0001
0.0002
Pleim
QNSE
< 0.0001
< 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)
All PBL schemes
under-predict max
LLJ wind speed
Lowest predicted
max LLJ wind speed
occurred in YSU
scheme
Null hypothesis was
rejected in favor of the
alternative hypothesis,
indicating that the underprediction of the wind
speed in the YSU PBL
scheme was highly
significant
Average Maximum LLJ Wind Speed over 30 LLJ events
for GFS initial boundary conditions
Height of LLJ
Maximum
MYJ
(m)
Pleim
(m)
YSU
(m)
QNSE
(m)
MYNN
2.5 (m)
MYNN
3.0 (m)
OBS
(m)
371.2
427.0
538.3
344.5
365.3
340.3
553.0
P-values of the YSU PBL scheme vs. other PBL schemes for
height of LLJ maximum
Ho: ; Ha:
P-value
MYJ
MYNN 2.5
< 0.0001
0.0005
MYNN 3.0
< 0.0001
Pleim
QNSE
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)
All PBL schemes
under-predict height
of LLJ max
Highest predicted
height of LLJ max
occurred in YSU
scheme
Null hypothesis was
rejected in favor of the
alternative hypothesis,
indicating that the higher
height predicted by the
YSU PBL scheme was
highly significant
Under-prediction of
wind speed maximum
in YSU Scheme
LLJ structure not
present in YSU
scheme
LLJ feature present in
all other PBL schemes
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
LLJ Peak Wind Speed Hour Occurrence
6
5
MYJ
Frequency
4
YSU
Pleim
3
QNSE
2
MYNN 2.5
MYNN 3.0
1
OBS
0
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Time (UTC)
• 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
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
• 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)
96 m: Wind speed 1.2
m/s stronger during LLJ
events – power increase
of 117 kW
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
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
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
All six PBL schemes overpredicted the wind speed
during LLJ events
YSU scheme showed the
lowest MAE, while the
highest was observed with
the MYNN 2.5 scheme
All six PBL schemes overpredicted the wind speed
during LLJ events –
although YSU small
positive bias
YSU scheme showed the
lowest MAE, while the
highest was observed with
the MYNN 2.5 scheme
• 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)
• 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
• 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
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As a result, no one scheme performed considerably better than any other
and all showed room for improvement.
Questions?
References
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