Jordan G. Powers and Kevin W. Manning Mesoscale and Microscale Meteorology Division
Mesoscale-Microscale coupling: A new time for the atmospheric … · 2018. 3. 9. · Vortex−PBL 3...
Transcript of Mesoscale-Microscale coupling: A new time for the atmospheric … · 2018. 3. 9. · Vortex−PBL 3...
Mesoscale-Microscale coupling:
A new time for the atmospheric modeling
A. Montornes
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
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Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
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Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
80 m above ground: 1 year analysis
1e−02
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label Real Vortex−PBL 3 km
1y
10min
3h
1d1m
Mesoscale Microscale
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Wind resource assessment approach: mesoscale models
1 m 10 m100 m
1 km10 km
100 km
1.000 km
10.000 km
1 h
1 day
1 week
1 Month
1 Year
10 Years
100 Years
1000 Years
10 min
1 min
10 s
1 s
<0.1 s
Synoptic scale
Mesoscale
Planetary waves
Seasonal
ENSO
100.000 km
Climate variations
Microscale
Climate
models
Global
models
Mesoscale
models
Wind industry
interest
Small eddies
Molecular diffusion
ClimateMeteorology
Fluid dynamics
10 cm1 cm
<1 mm
Characteristic length
Characteristictime
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
A new age: CFD
1 m 10 m100 m
1 km10 km
100 km
1.000 km
10.000 km
1 h
1 day
1 week
1 Month
1 Year
10 Years
100 Years
1000 Years
10 min
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10 s
1 s
<0.1 s
Synoptic scale
Mesoscale
Planetary waves
Seasonal
ENSO
100.000 km
Climate variations
Microscale
Climate
models
Global
models
Mesoscale
models
Wind industry
interest
Small eddies
Molecular diffusion
ClimateMeteorology
Fluid dynamics
10 cm1 cm
<1 mm
CFD
models
Characteristic length
Characteristictime
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Large eddies Small eddies
Energy flux
Inputenergy
Dissipation
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Large eddies Small eddies
ResolvedRANS
Modeled
Average
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Large eddies Small eddies
Resolved
RANS
LES
DNS
Avg.
Dyn.
Dynamic
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Large eddies Small eddies
RANS
DNS
LES
Avg.
Dyn.
Dyn.
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
LES RANSDNS
Adapted from Maries, A., Haque, M. A., Yilmaz, S. L., Nik, M. B., Marai, G. E.: New Developments in
the Visualization and Processing of Tensor Fields, Springer, pp. 137156, D. Laidlaw, A. Villanova.
2012
Dyn.Dyn. Avg.
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
LES RANSDNS
Dyn.Dyn. Avg.
MinutesSeconds Distributions
Different tools for different applications
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
A new age: Mesoscale-Microscale coupling
1 m 10 m100 m
1 km10 km
100 km
1.000 km
10.000 km
1 h
1 day
1 week
1 Month
1 Year
10 Years
100 Years
1000 Years
10 min
1 min
10 s
1 s
<0.1 s
Synoptic scale
Mesoscale
Planetary waves
Seasonal
ENSO
100.000 km
Climate variations
Microscale
Climate
models
Global
models
Mesoscale
models
Wind industry
interest
Small eddies
Molecular diffusion
ClimateMeteorology
Fluid dynamics
10 cm1 cm
<1 mm
CFD
models
Characteristic length
Characteristictime
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Coupling mesoscale-LES: Challenges
◮ Lateral boundary conditions
◮ Surface layer and Land Surface Model
◮ Terra-Incognita
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Coupling mesoscale-LES: Challenges
◮ Lateral boundary conditions
◮ Surface layer and Land Surface Model
◮ Terra-Incognita
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Lateral boundary conditions
Mesoscale PBL
BC
Microscale LES
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Lateral boundary conditions
Mesoscale PBL
BC
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Lateral boundary conditions
Mesoscale PBL
BC
θnew = θold + θ'
MuñozEsparza (2014)
+Vortex inhouse R+D
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
A new age: Mesoscale-Microscale coupling
1 m 10 m100 m
1 km10 km
100 km
1.000 km
10.000 km
1 h
1 day
1 week
1 Month
1 Year
10 Years
100 Years
1000 Years
10 min
1 min
10 s
1 s
<0.1 s
Synoptic scale
Mesoscale
Planetary waves
Seasonal
ENSO
100.000 km
Climate variations
Microscale
Climate
models
Global
models
Mesoscale
models
Wind industry
interest
Small eddies
Molecular diffusion
ClimateMeteorology
Fluid dynamics
10 cm1 cm
<1 mm
Large Eddy
Simulation
SGS
Characteristic length
Characteristictime
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Vortex approach
Reanalysis
VortexWRFLESVortex
WRFLES
Mesoscale PBL
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Vortex approach
Reanalysis
VortexWRFLES
VortexWRFLES
Mesoscale PBL
Microscale LES
Postprocessing
4 Hz output 10' averages
10' standard deviation
20150 meters above ground
Wind speed, Wind direction,Temperature, Pressure, RichardsonNumber, PBL Fluxes, Wind Veer,
Inflow angle, ...
3'' gust
1year timeseries (10min)110 m gridsize
Region 2.5x2.5 km
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
80 m above ground
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Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
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Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
80 m above ground: 1 year analysis
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10min
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Mesoscale Microscale
Terra Incognita
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Validation exercise
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1year validationWind speed validated at 96 sites at different mestmast heightsTurbulence validated at 56 sites at different mestmast heights
3.1% Offshore 41.7% Flat terrain 25.0% Complex terrain 30.2% Forest
Anemometers: 18.1% 2050 m 68.1% 50100 m 13.8% 100150 m
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Wind speed: Metrics
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The validation of the wind speed time-series show a
high dependence with the geographical features.
The analysis of the 96 sites used in this validation
shows a mean bias of +2.4% with respect to the mean
wind speed (Table 1). This metric is minimum for the
off-shore sites with a +0.4%, followed by the complex
terrain with/or forest and the complex terrain with a
mean bias around +0.4% and +0.5%, respectively. The
sites located in flat terrain experience a higher
understimation of -3.4% with respect to the mean
wind speed.
The study of the RMSE show a mean value for all the
sites of 2.6 ms-1 for the 10-min time-series that
decreases slightly when values are hourly
aggregated. The daily averaged values show a mean
RMSE of 1.7 ms-1 . By groups, the RMSE is minimum
in the off-shore sites with a value of 1 .9 ms-1 , 1 .8 ms-1
and 1.1 ms-1 for the 10-min, hourly and daily scales,
respectively. This metric increases slightly for the
site located in flat terrain showing a mean RMSE of
2.5 ms-1 for the 10-min values and decreasing to 1.6
ms-1 for the daily data-set. The Vortex-LES
simulations in complex terrain or complex terrain
with/or forest experience higher RMSEs with 7.1 ms-
1 and 6.6 ms-1 , respectively. In these cases, the
variation of the RMSE with the temporal scale
becomes negligible.
The results of the R2 reveals a similar behavior that
the one observed for the bias and the RMSE, as
expected. The averaged value for all the sites is 0.605
for the 10-min time-series, 0.637 for the hourly data-
sets and 0.807 for the daily simulations. Put in
plainly, the Vortex-LES simulations can reproduce
the around the 60% of the intra-day variability of the
site, reaching the 80% for the daily values.
By categories, the off-shore site show the highest R2,
with 0.837, 0.855 and 0.937 for the different time-
scales. The other groups produce similar results for
the 10-min and hourly data-sets. The larger 10-min
R2 values are produced in the complex terrain
with/or forest with 0.610, followed by the flat terrain
sites with 0.593 and the complex terrain simulations
with 0.590. The hourly values reproduce the same
pattern. The larger R2 is shown by the complex
terrain with/or forest with 0.646, while the other two
show values around 0.623 - 0.625 (Table 1).
The daily values experience a different behavior. The
highest R2 is reached by the flat terrain simulation
while the lowest values are produced by the complex-
terrain sites with 0.811 and 0.790, respectively. The
complex terrain with/or forest is an intermediate
case with 0.805.
Wind speed
Table 1 : Summary of the wind speed metrics
10-min 2.6 0.605
Bias
(%)
RMSE
(ms-1)
R2
All
(100%)Hourly 2.4 2.5 0.637
Daily 1.7 0.807
10-min 2.5 0.593
Flat
(41.7%)Hourly -3.4 2.4 0.625
Daily 1.6 0.811
10-min 7.1 0.590
Complex
(25.0%)Hourly 0.5 7.2 0.623
Daily 7.1 0.790
10-min 6.6 0.610Complex
Forest
(30.2%)
Hourly 0.4 6.8 0.646
Daily 6.9 0.805
10-min 1.9 0.837
Off-shore
(3.1%)Hourly 0.4 1 .8 0.855
Daily 1.1 0.937
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Wind speed: WeibullVortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The analysis of the Weibull parameters shows a good
agreement between the real and model wind
distributions. Generally, the Vortex-LES simulations
reproduce all the wind regimes being the calms and
extreme events a high improvements with respect to
the mesoscale simulations.
The validation is focused on three parameters: i) the
relative error in the scale parameter A, relative error
in the shape parameter k and the mean differences of
both distributions (Appendix ?). Table ?? shows a
summary of the metrics for all the sites and by the
different categories aforementioned. Figs ?? and ??
shows two samples of the results for the site 18th and
the site 87th.
The averaged error for all the sites is +2.8% and
+2.3% for A and k, respectively, while the mean
difference between the real and the modeled wind
distributions is of around 0.015 sm-1 .
The off-shore sites simulate the best wind
disrtribution with a near-zero error in A and a mean
difference between both distributions of around
0.007 sm-1 . The shape parameter is overestimated
with an error of 8.3%.
The flat terrain sites tend to underestimate the scale
parameter with an averaged error of -3.2%. The shape
parameter is slightly overestimated with a mean
error of 1 .0%. The difference between the real and
modeled frequencies shows a mean error of 0.018 sm-
1 .
Table 1 : Summary of the Weibull metrics
10-min 2.3 0.015
A
(%)
k
(%)
Freq.
(sm-1)
All
(100%)2.8
10-min 1.0 0.018Flat
(41.7%)-3.2
10-min 7.7 0.014Complex
(25.0%)8.0
10-min -2.9 0.014
Complex
Forest
(30.2%)
6.8
10-min 8.3 0.007Off-shore
(3.1%)0.3
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site087.
Off-shore
Flat terrain
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The analysis of the Weibull parameters shows a good
agreement between the real and model wind
distributions. Generally, the Vortex-LES simulations
reproduce all the wind regimes being the calms and
extreme events a high improvements with respect to
the mesoscale simulations.
The validation is focused on three parameters: i) the
relative error in the scale parameter A, relative error
in the shape parameter k and the mean differences of
both distributions (Appendix ?). Table ?? shows a
summary of the metrics for all the sites and by the
different categories aforementioned. Figs ?? and ??
shows two samples of the results for the site 18th and
the site 87th.
The averaged error for all the sites is +2.8% and
+2.3% for A and k, respectively, while the mean
difference between the real and the modeled wind
distributions is of around 0.015 sm-1 .
The off-shore sites simulate the best wind
disrtribution with a near-zero error in A and a mean
difference between both distributions of around
0.007 sm-1 . The shape parameter is overestimated
with an error of 8.3%.
The flat terrain sites tend to underestimate the scale
parameter with an averaged error of -3.2%. The shape
parameter is slightly overestimated with a mean
error of 1 .0%. The difference between the real and
modeled frequencies shows a mean error of 0.018 sm-
1 .
Table 1 : Summary of the Weibull metrics
10-min 2.3 0.015
A
(%)
k
(%)
Freq.
(sm-1)
All
(100%)2.8
10-min 1.0 0.018Flat
(41.7%)-3.2
10-min 7.7 0.014Complex
(25.0%)8.0
10-min -2.9 0.014
Complex
Forest
(30.2%)
6.8
10-min 8.3 0.007Off-shore
(3.1%)0.3
0.000
0.025
0.050
0.075
0 10 20 30
Wind speed [m/s]
Pro
babili
ty D
ensity [−
]
Site030
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site087.
Off-shore
Flat terrain
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The analysis of the Weibull parameters shows a good
agreement between the real and model wind
distributions. Generally, the Vortex-LES simulations
reproduce all the wind regimes being the calms and
extreme events a high improvements with respect to
the mesoscale simulations.
The validation is focused on three parameters: i) the
relative error in the scale parameter A, relative error
in the shape parameter k and the mean differences of
both distributions (Appendix ?). Table ?? shows a
summary of the metrics for all the sites and by the
different categories aforementioned. Figs ?? and ??
shows two samples of the results for the site 18th and
the site 87th.
The averaged error for all the sites is +2.8% and
+2.3% for A and k, respectively, while the mean
difference between the real and the modeled wind
distributions is of around 0.015 sm-1 .
The off-shore sites simulate the best wind
disrtribution with a near-zero error in A and a mean
difference between both distributions of around
0.007 sm-1 . The shape parameter is overestimated
with an error of 8.3%.
The flat terrain sites tend to underestimate the scale
parameter with an averaged error of -3.2%. The shape
parameter is slightly overestimated with a mean
error of 1 .0%. The difference between the real and
modeled frequencies shows a mean error of 0.018 sm-
1 .
Table 1 : Summary of the Weibull metrics
10-min 2.3 0.015
A
(%)
k
(%)
Freq.
(sm-1)
All
(100%)2.8
10-min 1.0 0.018Flat
(41.7%)-3.2
10-min 7.7 0.014Complex
(25.0%)8.0
10-min -2.9 0.014
Complex
Forest
(30.2%)
6.8
10-min 8.3 0.007Off-shore
(3.1%)0.3
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
0.000
0.025
0.050
0.075
0 10 20
Wind speed [m/s]
Pro
babili
ty D
ensity [−
]
Site018
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site087.
Off-shore
Flat terrain
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The analysis of the Weibull parameters shows a good
agreement between the real and model wind
distributions. Generally, the Vortex-LES simulations
reproduce all the wind regimes being the calms and
extreme events a high improvements with respect to
the mesoscale simulations.
The validation is focused on three parameters: i) the
relative error in the scale parameter A, relative error
in the shape parameter k and the mean differences of
both distributions (Appendix ?). Table ?? shows a
summary of the metrics for all the sites and by the
different categories aforementioned. Figs ?? and ??
shows two samples of the results for the site 18th and
the site 87th.
The averaged error for all the sites is +2.8% and
+2.3% for A and k, respectively, while the mean
difference between the real and the modeled wind
distributions is of around 0.015 sm-1 .
The off-shore sites simulate the best wind
disrtribution with a near-zero error in A and a mean
difference between both distributions of around
0.007 sm-1 . The shape parameter is overestimated
with an error of 8.3%.
The flat terrain sites tend to underestimate the scale
parameter with an averaged error of -3.2%. The shape
parameter is slightly overestimated with a mean
error of 1 .0%. The difference between the real and
modeled frequencies shows a mean error of 0.018 sm-
1 .
Table 1 : Summary of the Weibull metrics
10-min 2.3 0.015
A
(%)
k
(%)
Freq.
(sm-1)
All
(100%)2.8
10-min 1.0 0.018Flat
(41.7%)-3.2
10-min 7.7 0.014Complex
(25.0%)8.0
10-min -2.9 0.014
Complex
Forest
(30.2%)
6.8
10-min 8.3 0.007Off-shore
(3.1%)0.3
Real Vortex−LES
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site087.
Off-shore
Flat terrain
f (x) = kA
(
xA
)k−1
exp(
−
(
xA
)k)
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Wind direction: Metrics and roseVortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The mean error in the scale parameter increasessignificantly in the complex terrain and forestcategories with +8.0% and +6.8%, respectively. Thebehavior for the shape parameter is completlydifferent. For complex terrain sites, the k isoverestimated with an averaged error of +7.7%. Incontrast, the scale parameter for complex terrainwith/or forest tends to be underestimated with amean error of around -2.9%. Both categories show asimilar mean error in the distributions of 0.014 sm-1 .
The validation of the wind direction shows an
averaged bias of 3º and a mean MAE of 18º, less than
one wind rose sector (Table ?). The preformance is
similar for the off-shore, flat terrain and complex
terrain sites with a mean MAE of around 34º. In the
off-shore sites, the wind direction is slightly
underestimated with -2º, while the complex terrain
sites show a slightly positive bias of +2º. Finally, the
flat terrain sites produce a near-zero bias.
The larger mean bias is observed in the complexterrain with/or forest sites with +10º. However, themean MAE is significantly lower with 31º·
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex terrain
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex forest
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Fig ??: Example of the wind roses for two sites.
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The mean error in the scale parameter increasessignificantly in the complex terrain and forestcategories with +8.0% and +6.8%, respectively. Thebehavior for the shape parameter is completlydifferent. For complex terrain sites, the k isoverestimated with an averaged error of +7.7%. Incontrast, the scale parameter for complex terrainwith/or forest tends to be underestimated with amean error of around -2.9%. Both categories show asimilar mean error in the distributions of 0.014 sm-1 .
The validation of the wind direction shows an
averaged bias of 3º and a mean MAE of 18º, less than
one wind rose sector (Table ?). The preformance is
similar for the off-shore, flat terrain and complex
terrain sites with a mean MAE of around 34º. In the
off-shore sites, the wind direction is slightly
underestimated with -2º, while the complex terrain
sites show a slightly positive bias of +2º. Finally, the
flat terrain sites produce a near-zero bias.
The larger mean bias is observed in the complexterrain with/or forest sites with +10º. However, themean MAE is significantly lower with 31º·
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex terrain
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex forest
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
N
E
S
W
N
E
S
W
Real Vortex−LES
0%
5%
10%
15%
20%
25%
Wind Speed(m/s)
(0,3]
(3,6]
(6,9]
(9,12]
(12,15]
(15,18]
(18,21]
(21,24]
(24,25.6]
N
E
S
W
N
E
S
W
Real Vortex−LES
0%
4%
8%
12%
16% Wind Speed(m/s)
(0,3]
(3,6]
(6,9]
(9,12]
(12,15]
(15,18]
(18,19.8]
Fig ??: Example of the wind roses for two sites.
Site016
Site078
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The mean error in the scale parameter increasessignificantly in the complex terrain and forestcategories with +8.0% and +6.8%, respectively. Thebehavior for the shape parameter is completlydifferent. For complex terrain sites, the k isoverestimated with an averaged error of +7.7%. Incontrast, the scale parameter for complex terrainwith/or forest tends to be underestimated with amean error of around -2.9%. Both categories show asimilar mean error in the distributions of 0.014 sm-1 .
The validation of the wind direction shows an
averaged bias of 3º and a mean MAE of 18º, less than
one wind rose sector (Table ?). The preformance is
similar for the off-shore, flat terrain and complex
terrain sites with a mean MAE of around 34º. In the
off-shore sites, the wind direction is slightly
underestimated with -2º, while the complex terrain
sites show a slightly positive bias of +2º. Finally, the
flat terrain sites produce a near-zero bias.
The larger mean bias is observed in the complexterrain with/or forest sites with +10º. However, themean MAE is significantly lower with 31º·
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex terrain
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex forest
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Fig ??: Example of the wind roses for two sites.
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The mean error in the scale parameter increasessignificantly in the complex terrain and forestcategories with +8.0% and +6.8%, respectively. Thebehavior for the shape parameter is completlydifferent. For complex terrain sites, the k isoverestimated with an averaged error of +7.7%. Incontrast, the scale parameter for complex terrainwith/or forest tends to be underestimated with amean error of around -2.9%. Both categories show asimilar mean error in the distributions of 0.014 sm-1 .
The validation of the wind direction shows an
averaged bias of 3º and a mean MAE of 18º, less than
one wind rose sector (Table ?). The preformance is
similar for the off-shore, flat terrain and complex
terrain sites with a mean MAE of around 34º. In the
off-shore sites, the wind direction is slightly
underestimated with -2º, while the complex terrain
sites show a slightly positive bias of +2º. Finally, the
flat terrain sites produce a near-zero bias.
The larger mean bias is observed in the complexterrain with/or forest sites with +10º. However, themean MAE is significantly lower with 31º·
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex terrain
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex forest
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Fig ??: Example of the wind roses for two sites.
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
The mean error in the scale parameter increasessignificantly in the complex terrain and forestcategories with +8.0% and +6.8%, respectively. Thebehavior for the shape parameter is completlydifferent. For complex terrain sites, the k isoverestimated with an averaged error of +7.7%. Incontrast, the scale parameter for complex terrainwith/or forest tends to be underestimated with amean error of around -2.9%. Both categories show asimilar mean error in the distributions of 0.014 sm-1 .
The validation of the wind direction shows an
averaged bias of 3º and a mean MAE of 18º, less than
one wind rose sector (Table ?). The preformance is
similar for the off-shore, flat terrain and complex
terrain sites with a mean MAE of around 34º. In the
off-shore sites, the wind direction is slightly
underestimated with -2º, while the complex terrain
sites show a slightly positive bias of +2º. Finally, the
flat terrain sites produce a near-zero bias.
The larger mean bias is observed in the complexterrain with/or forest sites with +10º. However, themean MAE is significantly lower with 31º·
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex terrain
Fig ??: Example of a comparison between the Weibull
distribution for the measurements and Vortex-LES
for Site018.
Complex forest
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Fig ??: Example of the wind roses for two sites.
Table 1 : Summary of the wind direction metrics
10-min 18
Bias
(deg)
MAE
(deg)
All
(100%)
Flat
(41.7%)
Complex
(25.0%)
Complex
Forest
(30.2%)
Off-shore
(3.1%)
3
10-min 35-2
10-min 340
10-min 342
10-min 3110
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Wind speed: Vertical profiles
Generally, the number of sites with measurements atmore than one height is reduced or the customers donot share the data-sets with us. Consequently, theanalysis of the vertical profiles is reduced to 7 of the96 sites used in this validation. Particularly, thesesites are Site002, Site007, Site028, Site030, Site038,Site045 and Site050 (Table ??).
The analysis of the vertical profiles is performed by
comparing the real and modeled wind shears based
on a log fit, as
being V1 and V2 the anual mean wind speed at two
different heights, z1 and z2, respectively, and the
shear coefficient.
An example of this fit is ilustrated with two examples
in complex-terrain and in off-shore in Fig. ??.
All sites show a similar behavior, the bias tend to be
positive near to the surface drifting to near-zero of
slightly negative values at the upper levels.
The physical reason of these patterns is in the
surface features. The first levels of the real
atmosphere is highly perturbed on the local features
(trees, buildings or waves, among others), that cannot
be resolved by the Vortex-LES at 100 m. The model
parameterizes these effects by assuming some
preevaluated roughnesses, enough for mesoscale
applications but more important in the case of
microscale simulations. Consequently, the model
show a tendency to overestimate the lower levels.
Nevertheless, these levels have a minor impact in the
wind industry and it is an issue that will be solved
during the following years, as this new approach
penetrates to the market.
The higher levels (i.e. between 50 and 100 m above
the ground) are less affected by the surface
characteristics, and thus, the bias decreases
significantly.
Below 50 m, the bias fluctuates between +5 and +20%
with respect to the mean wind speed, while between
50 and 100 m the bias ranges between -5% and +5% ,
although in some cases reaches values around the
10%.
Table 1 : Summary of the vertical levels used for the
validation of the vertical profiles
10, 20, 40, 80 m
Site Heights with available real
measurements
Site002
20, 30, 40, 50, 60 mSite007
60, 80, 100 mSite028
33, 40, 50, 60, 70, 80, 90, 100 mSite030
20, 30, 50, 70 mSite038
40, 50, 60 mSite045
30, 35, 50, 100 mSite050
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
● ●
●●
●●
60
70
80
90
100
7 8 9
Wind speed [m/s]
He
igh
t [m
]
Site028
● ●
● ●
● ●
● ●
● ●
● ●
● ●
40
60
80
100
8 9 10
Wind speed [m/s]H
eig
ht
[m]
Site030b) Off-shore
+2.9%
+2.6%
+0.5%
+0.4%
+0.5%
+0.8%
+0.5%
a) Complex-terrain
+0.9%
-0.4%
-0.4%
Fig. 2: Analysis of the wind profile for a site in complex-terrain (left-hand plot) and an off-shore site (right-hand plot). The numbers indicate the anual bias at each height normalized with respect to the real mean windspeed.
Shear (real): 0.103
Shear (model): 0.075
Shear (real): 0.224
Shear (model): 0.195
Shear error: -12.7% Shear error: -27.8%
Generally, the number of sites with measurements atmore than one height is reduced or the customers donot share the data-sets with us. Consequently, theanalysis of the vertical profiles is reduced to 7 of the96 sites used in this validation. Particularly, thesesites are Site002, Site007, Site028, Site030, Site038,Site045 and Site050 (Table ??).
The analysis of the vertical profiles is performed by
comparing the real and modeled wind shears based
on a log fit, as
being V1 and V2 the anual mean wind speed at two
different heights, z1 and z2, respectively, and the
shear coefficient.
An example of this fit is ilustrated with two examples
in complex-terrain and in off-shore in Fig. ??.
All sites show a similar behavior, the bias tend to be
positive near to the surface drifting to near-zero of
slightly negative values at the upper levels.
The physical reason of these patterns is in the
surface features. The first levels of the real
atmosphere is highly perturbed on the local features
(trees, buildings or waves, among others), that cannot
be resolved by the Vortex-LES at 100 m. The model
parameterizes these effects by assuming some
preevaluated roughnesses, enough for mesoscale
applications but more important in the case of
microscale simulations. Consequently, the model
show a tendency to overestimate the lower levels.
Nevertheless, these levels have a minor impact in the
wind industry and it is an issue that will be solved
during the following years, as this new approach
penetrates to the market.
The higher levels (i.e. between 50 and 100 m above
the ground) are less affected by the surface
characteristics, and thus, the bias decreases
significantly.
Below 50 m, the bias fluctuates between +5 and +20%
with respect to the mean wind speed, while between
50 and 100 m the bias ranges between -5% and +5% ,
although in some cases reaches values around the
10%.
Table 1 : Summary of the vertical levels used for the
validation of the vertical profiles
10, 20, 40, 80 m
Site Heights with available real
measurements
Site002
20, 30, 40, 50, 60 mSite007
60, 80, 100 mSite028
33, 40, 50, 60, 70, 80, 90, 100 mSite030
20, 30, 50, 70 mSite038
40, 50, 60 mSite045
30, 35, 50, 100 mSite050
Vortex-LES: white paper
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
● ●Real Vortex−LES
b) Off-shore
+2.9%
+2.6%
+0.5%
+0.4%
+0.5%
+0.8%
+0.5%
a) Complex-terrain
+0.9%
-0.4%
-0.4%
Fig. 2: Analysis of the wind profile for a site in complex-terrain (left-hand plot) and an off-shore site (right-hand plot). The numbers indicate the anual bias at each height normalized with respect to the real mean windspeed.
Shear (real): 0.103
Shear (model): 0.075
Shear (real): 0.224
Shear (model): 0.195
Shear error: -12.7% Shear error: -27.8%
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Wind standard deviation: MetricsTable 1 : Summary of the wind direction metrics
0.278
RMSE
(ms-1)
R2
(-)
All
(100%)0.5
Bias
(ms-1)
-0.05
0.223Rainfed croplands
(16.4%)0.4-0.1
0.171
Cropland (50-70%)
Vegetation (20-50%)
(10.9%)
0.50.0
0.251Vegetation (50-70%)
Cropland (20-50%)
(10.9%)
0.40.0
0.261Deciduous forest
(40%, >5m)
(9.1%)
0.5-0.1
0.309Evergreen forest
(40%, >5m)
(10.9%)
0.6-0.1
0.416Forest (50-70%)
Grassland (20-50%)
(7.3%)
0.5-0.1
0.260Grassland (50-70%)
Forest (20-50%)
(1 .8%)
0.6-0.1
0.271Shrubland
(>15%, <5 m)
(1 .8%)
0.40.0
0.246Herbaceous
vegetation (>15%)
(7.3%)
0.40.0
0.256Sparse
vegetation (<15%)
(7.3%)
0.6-0.1
0.372Grassland, woody,
flooded (>15%)
(1 .8%)
0.40.0
0.261Bared areas
(9.1%) 0.30.0
0.321Off-shore
(5.5%) 0.40.0
VORTEX FdC S.L. Parc Tecnologic Barcelona / Carrer Marie Curie 8-1 / 08042 Barcelona Spain / vortexfdc.com
0.5
0.6
0.7
0.8
0 500 1000 1500 2000
Time
SD
[m
/s]
lab Real Vortex−LES
mirceavoda.100
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Turbulence intensity
0
10
20
30
5 10 15 20 25
10−min wind speed [ms−1]
Tu
rbu
len
ce
in
ten
sity [
%]
Site008
0
10
20
30
5 10 15 20 25
10−min wind speed [ms−1]
Tu
rbu
len
ce
in
ten
sity [
%]
Site026
Real
Vortex−LES
TI = 100δws
<ws>
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
6th International Conference on Meteorology and Climatology of the Mediterranean 2017
Turbulence Intensity
TI(%) validated at 58 sites
Which metric to use?
1. MAE between TI-modelagainst TI-obs weighted bybin-ocurrence
2. MAE at 15 m/s bin
Average Std Dev
MAE 1.8 0.9MAE-15 1.9 1.1
Montornes et. al, [email protected] Vortex
Mesoscale-Microscale coupling: a new time for the atmospheric modeling
Mesoscale-Microscale coupling:
A new time for the atmospheric modeling
A. Montornes