mesoscale-microscale coupling: a new time for the atmospheric modeling · 2018. 3. 9. · 6th...

Mesoscale-Microscale coupling:

A new time for the atmospheric modeling

A. Montornes

6th International Conference on Meteorology and Climatology of the Mediterranean 2017

80 m above ground

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Vortex−PBL 3 km

Montornes et. al, [email protected] Vortex

Mesoscale-Microscale coupling: a new time for the atmospheric modeling


80 m above ground

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Measured Vortex−PBL 3 km




80 m above ground: 1 year analysis

1e−02

1e+00

1e+02

1e−05 1e−03Frequency (Hz)

Spectr

al density [−

]

label Real Vortex−PBL 3 km

1y

10min

3h

1d1m

Mesoscale Microscale




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




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

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


Characteristictime




Large eddies Small eddies

Energy flux

Inputenergy

Dissipation





ResolvedRANS

Modeled

Average





Resolved

RANS

LES

DNS

Avg.

Dyn.

Dynamic





RANS

DNS

LES

Avg.

Dyn.

Dyn.




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.




LES RANSDNS

Dyn.Dyn. Avg.

MinutesSeconds Distributions

Different tools for different applications




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


Characteristictime




Coupling mesoscale-LES: Challenges

◮ Lateral boundary conditions

◮ Surface layer and Land Surface Model

◮ Terra-Incognita




Lateral boundary conditions

Mesoscale PBL

BC

Microscale LES





Mesoscale PBL

BC





Mesoscale PBL

BC

θnew = θold + θ'

MuñozEsparza (2014)

+Vortex inhouse R+D




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


Characteristictime




Vortex approach

Reanalysis

VortexWRFLESVortex

WRFLES

Mesoscale PBL




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




80 m above ground

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Measured Vortex−PBL 3 km




80 m above ground

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Measured Vortex−LES 111 m Vortex−PBL 3 km




80 m above ground: 1 year analysis

1e−02

1e+00

1e+02

1e−05 1e−03Frequency (Hz)

Spectr

al density [−

]

label Real Vortex−LES 111 m Vortex−PBL 3 km

1y

10min

3h

1d1m

Mesoscale Microscale

Terra Incognita




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




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




Wind speed: WeibullVortex-LES: white paper


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


(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.



for Site087.

Off-shore

Flat terrain
















the site 87th.















1 .


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


(25.0%)8.0

10-min -2.9 0.014

Complex

Forest

(30.2%)

6.8


(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



for Site018.



for Site087.

Off-shore

Flat terrain
















the site 87th.















1 .


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


(25.0%)8.0

10-min -2.9 0.014

Complex

Forest

(30.2%)

6.8


(3.1%)0.3



for Site018.

0.000

0.025

0.050

0.075

0 10 20

Wind speed [m/s]

Pro

babili

ty D

ensity [−

]

Site018



for Site087.

Off-shore

Flat terrain
















the site 87th.















1 .


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


(25.0%)8.0

10-min -2.9 0.014

Complex

Forest

(30.2%)

6.8


(3.1%)0.3

Real Vortex−LES



for Site018.



for Site087.

Off-shore

Flat terrain

f (x) = kA

(

xA

)k−1

exp(

−

(

xA

)k)




Wind direction: Metrics and roseVortex-LES: white paper


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·



for Site018.

Complex terrain



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.


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
















for Site018.

Complex terrain



for Site018.

Complex forest


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]


Site016

Site078


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
















for Site018.

Complex terrain



for Site018.

Complex forest


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



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
















for Site018.

Complex terrain



for Site018.

Complex forest


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



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
















for Site018.

Complex terrain



for Site018.

Complex forest


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



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




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



● ●

●●

●●

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 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



● ●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 (real): 0.224


Shear error: -12.7% Shear error: -27.8%




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


0.5

0.6

0.7

0.8

0 500 1000 1500 2000

Time

SD

[m

/s]

lab Real Vortex−LES

mirceavoda.100




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>




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



Mesoscale-Microscale coupling:

A new time for the atmospheric modeling

A. Montornes

mesoscale-microscale coupling: a new time for the atmospheric modeling · 2018. 3. 9. · 6th...

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