ASSESSMENT OF URBAN PARAMETERIZATIONS IN THE WRF … · 2018. 2. 11. · BEP-3.2-35.3 45.4 5.4...
1
Inmisión Deposition Visibility CMAQ 4.6 Initial conditions Boundary conditions Photolysis SMOKE 2.4 Emissions Inventory Surrogates temporal spacial chemical Point emissions WRFV3.3.1 Terrain Meteorology Skamarock & Klemp, 2008 Byun and Ching, 1999 Institute for the Environment, 2009 Inmisión Deposition Visibility BEP BULK •WRF outputs were used to feed the chemical-transport model CMAQ. To assess the implications of the WRF configuration in air quality, the corresponding outputs were compared with ground-level observations of NO 2 ,O 3 and PM 2.5 from 26 monitoring stations throughout the modelling domain. • Figure 4 shows the differences of the results coming from BULK and BEP runs for policy-relevant air quality metrics: NO 2 BEP brings about an increase of 10-18 μg/m 3 in the annual mean within the city. Nonetheless, the predictions of BEP for non-urban areas in the SW area are lower. O 3 Ground-level ozone concentrations predicted in the BEP run are lower all over the domain, reaching a maximum annual difference of 14 μg/m 3 in the city centre. PM 2.5 CMAQ predictions for PM 2.5 are also increased when the urban parameterization is applied. This is specially evident in winter. Differences up to 2 μg/m 3 in the annual mean are observed in the high-density innermost part of the city . PBL Height, BEP increases PBLH predictions in general (40 - 60 m as an average), mainly in winter. • Beside this comparison, predicted PBLH were scrutinized (YSU - BULK and BouLac– BEP). • Two annual runs were made with the BEP urban parameterization and the BULK scheme, used as a reference. Some of the most influential variables from the air quality point of view were compared with observations from 6 urban meteorological stations: temperature (K), wind direction (º) and wind speed (m/s). T2. BEP yields higher ground-level temperature, mainly in High residential density areas (also in low residential density areas not considered as urban surface in the standard land cover used in BULK). The average temperature differences ranges from 0.5 to 1.2 K, mostly due to higher temperature predictions in winter. Wind Speed. The urban parametrization has a strong effect in wind speed predicted by WRF. Wind intensity is reduced by 0.8 m/s as an average in the city center and up to 3 m/s in the city outskirts. Comparison with surface observations Comparison with surface observations Statistics Statistics ASSESSMENT OF URBAN PARAMETERIZATIONS IN THE WRF MODEL FOR AIR QUALITY MODELLING PURPOSES IN MADRID (SPAIN) D. de la Paz 1 , R. Borge 1 , J.M. de Andrés 1 , J. Pérez 1 J. Lumbreras 1 , M.E. Rodríguez 1 1 Laboratory of Environmental Modelling. Technical University of Madrid (UPM). Madrid, SPAIN [email protected] 9th International Conference on Air Quality – Science and Application 24-28 March, 2014 Garmisch-Partenkirchen, Germany INTRODUCTION METHODOLOGY WRF MODEL CONFIGURATION METEOROLOGY AIR QUALITY CONCLUSIONS References Next steps Table 3. Statistics for temperature Table 4. Statistics for wind URBAN [BEP]– BASE [BULK] ANNUAL MEAN Temperature WINTER SUMMER PBLH Wind Speed ∆T (2m) PBLH (m) WSpeed (m/s) URBAN [BEP]– BASE [BULK] 260 270 280 290 300 310 320 260 270 280 290 300 310 320 T 2m - BULK (ºK) T 2m - BEP (ºK) 260 270 280 290 300 310 320 260 270 280 290 300 310 320 T 2m - BULK (ºK) T 2m - BEP (ºK) r = 0.981 MB = 1.5 r = 0.976 MB = 0.1 r = 0.983 MB = 0.8 r = 0.978 MB = 0.2 High Intensity Residential Low Intensity Residential Modelled T (Kº) Observed T (Kº) Modelled T (Kº) Observed T (Kº) 0 4 8 12 16 20 0 4 8 12 16 20 Wind Speed - BULK (m/s) Wind Speed - BEP (m/s) r = 0.733 MB = 1.9 m/s r = 0.734 MB = -0.1 m/s Observed Wind Speed (m/s) Modelled Wind Speed (m/s) High Intensity Residential 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Wind Speed - BULK (m/s) Wind Speed - BEP (m/s) r = 0.691 MB = 1.6 m/s r = 0.647 MB = 0.4 m/s Observed Wind Speed (m/s) Modelled Wind Speed (m/s) Low Intensity Residential 0 500 1000 1500 2000 2500 3000 1 3 5 7 9 11 13 15 17 19 21 23 PBLH-BULK Winter (m) PBLH-BEP Winter (m) PBLH-BULK Summer (m) PBLH-BEP Summer (m) Low Intensity Residential PBL Height (m) hour 0 500 1000 1500 2000 2500 3000 1 3 5 7 9 11 13 15 17 19 21 23 PBLH-BULK Winter (m) PBLH-BEP Winter (m) PBLH-BULK Summer (m) PBLH-BEP Summer (m) High Intensity Residential hour PBL Height (m) nº stations nº Observations Canopy model MB (º) BEP -19.8 BULK -5.9 Aggregated statistics for Wind Direction Error (º) 60.1 65.2 6 stations 46513 nº stations nº Observations Canopy model MB (m/s) RMSE (m/s) IOA BEP 0.0 0.6 0.732 BULK 1.6 2.0 0.611 Aggregated statistics for Wind Speed 5 stations 39075 nº stations nº Observations Canopy model MB (º) Error (º) IOA BEP 1.7 0.6 0.980 BULK 0.7 0.4 0.990 Aggregated statistics for Temperature 6 stations 48936 Table 5. Statistics for NO 2 Table 6. Statistics for O 3 Table 7. Statistics for PM 2.5 ANNUAL MEAN NO 2 WINTER SUMMER O 3 PM 2.5 PM2,5 (μg/m3) NO2 (μg/m3) O3 (μg/m3) 0 40 80 120 160 200 240 280 320 360 0 40 80 120 160 200 240 280 320 360 0 40 80 120 160 200 240 280 320 0 40 80 120 160 200 240 280 320 r = 0.601 MB = -7.1 r = 0.433 MB = -14.6 r = 0.510 MB = -0.6 r = 0.602 MB = -23.4 High Intensity Residential Low Intensity Residential Modelled NO 2 (μg/m 3 ) Modelled NO 2 (μg/m 3 ) Observed NO2 (μg/m3) Observed NO2 (μg/m3) NO2 – BULK (μg/m3) NO2 – BEP (μg/m3) NO2 – BULK (μg/m3) NO2 – BEP (μg/m3) 0 40 80 120 160 200 0 40 80 120 160 200 0 40 80 120 160 200 0 40 80 120 160 200 r = 0.704 MB = 26.1 r = 0.714 MB = 38.6 r = 0.655 MB = 24.2 r = 0.703 MB = 42.9 High Intensity Residential Low Intensity Residential Modelled O 3 (μg/m 3 ) Modelled O 3 (μg/m 3 ) Observed O3 (μg/m3) Observed O3 (μg/m3) NO2 – BULK (μg/m3) NO2 – BEP (μg/m3) NO2 – BULK (μg/m3) NO2 – BEP (μg/m3) 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 100 0 20 40 60 80 100 r = 0.414 MB = -10.3 r = 0.392 MB = -6.2 r = 0.467 MB = -5.7 r = 0.306 MB = -10.9 High Intensity Residential Low Intensity Residential Modelled PM 2.5 (μg/m 3 ) Modelled PM 2.5 (μg/m 3 ) Observed PM2.5 (μg/m3) Observed PM2.5 (μg/m3) NO2 – BULK (μg/m3) NO2 – BEP (μg/m3) NO2 – BULK (μg/m3) NO2 – BEP (μg/m3) nº stations nº Observations Type station Canopy model MB (μg/m 3 ) MFB (%) MFE (%) RMSE (μg/m 3 ) r IOA BEP -6.8 -25.3 64.7 40.0 0.534 0.691 BULK -17.5 -45.6 70.5 40.4 0.529 0.663 BEP -1.0 -11.7 61.6 23.9 0.639 0.767 BULK -3.8 -20.9 64.8 23.8 0.604 0.744 Urban station 58594 7 stations Aggregated statistics for NO2 26 stations 220637 Traffic station nº stations nº Observations Type station Canopy model MB (μg/m 3 ) MFB (%) MFE (%) RMSE (μg/m 3 ) r IOA BEP 27.3 50.2 82.0 38.8 0.610 0.647 BULK 39.7 79.2 87.8 46.3 0.653 0.583 BEP 27.3 55.7 64.1 37.0 0.702 0.707 BULK 32.7 63.4 67.7 41.4 0.682 0.661 26 stations 5 stations 219078 41051 Traffic station Urban station Aggregated statistics for O3 nº stations nº Observations Type station Canopy model MB (μg/m 3 ) MFB (%) MFE (%) RMSE (μg/m 3 ) r IOA BEP -8.7 -63.5 84.1 15.1 0.346 0.503 BULK -9.7 -69.8 88.0 15.8 0.264 0.459 BEP -3.2 -35.3 45.4 5.4 0.213 0.321 BULK -3.3 -34.9 45.5 5.6 0.169 0.295 3 stations Traffic station Urban station 26089 15058 Aggregated statistics for PM2.5 2 stations Skamarock, W.C. and Klemp, J.B., 2008. A time-split non-hydrostatic atmospheric model. Journal of Computational Physics 227, 3465–3485. Byun, D.W., Schere, K.L., 2006. Review of the governing equations, computational algorithms and other components of the models-3 community multiscale air quality (CMAQ) modeling systems. Applied Mechanics Review 59 (2), 51–77. Ching, J.K.S., 2013. A perspective on urban canopy layer modelling for weather, climate and air quality applications. Urban Climate 3, 13-39. Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorology 104, 261-304. Salamanca, F., Martilli, A., 2010. A new building energy model coupled with an urban canopy parameterization for urban climate simulations—Part II. Validation with one dimension off-line simulations. Theoretical and Applied Climatology 99, 345–356. Geographic scope X-Y dimensions (km) Horizontal Europe 6720 x 5952 ∆x =48 km Iberian Peninsula 1536 x 1248 ∆x =16 km Greater Madrid area 256 x 256 ∆x =4 km Madrid Metropolitan area 56 x 60 ∆x =1 km Domains for the WRF simulations BULK BEP. Urban scheme. (Martilli, 2002) Shortwave radiation Dudhia scheme Dudhia scheme Longwave radiation Eta Geophysical Fluid Dynamics Laboratory (GFDL) Eta Geophysical Fluid Dynamics Laboratory (GFDL) Land-surface model Noah LSM Noah LSM PBL scheme Yonsei University (YSU) Bougeault and Lacarrère Horizontal resolution ∆x =1 km (56×60) ∆x =1 km (56×60) Vertical resolution 30 sigma levels (Lowest level ≈ 7 m) 30 sigma levels (Lowest level ≈ 7 m) Eulerian 3D mesoscale models can consistently describe a wide range of spatial scales, from continental to urban scale. However, urban areas present features that are usually missed by land- surface and PBL modules commonly implemented in such models. As environmental issues in urban areas grow, canopy layer modelling becomes increasingly important (Ching, 2013). Models such as the Weather Research and Forecasting model (WRF) (Skamarock and Klemp, 2008) incorporate urban parameterizations to take into account changes in albedo, roughness length and thermal properties imposed by buildings. However, their application is not straightforward and should be specifically tested in the domain of interest before they can be integrated in air quality modelling activities. In this study, four urban parameterizations were tested for the Madrid urban area (Spain). Summer and winter, 1 km 2 resolution runs were performed for the following parameterizations: • Urban parameterization within the Noah Land Surface Model (Bulk scheme) (Liu et al., 2006) • Building Energy Parameterization (BEP) (Martilli et al., 2002) considering two different setups for street parameters • Building Energy Model (BEP+BEM) (Salamanca & Martilli, 2010) Model outputs were compared with observations from five meteorological stations throughout the Madrid city, representative of different urban morphologies in two 1- month periods (winter and summer). Multi-layer, more complex parameterizations brought about a reduction of wind speed overestimation of the reference option (bulk) (Table.1) and slight improvements on surface temperature predictions. Table 1. Basic statistics results for wind (summer-winter average) MB (º) Error (º) MB (m/s) RMSE (m/s) IOA BULK 13.4 49.4 0.4 0.6 0.502 BEP 10.7 43.3 0.2 0.6 0.665 BEP+BEM 5.9 46.1 0.2 0.5 0.695 Speed Direction Canopy model Table 2. Model configuration for the WRF simulations Wind statistics. BEP was identified as the optimal setting as a compromise of model performance, input data requirements and CPU time. Finally, the selected parameterization was used to produce annual meteorological inputs to feed the CMAQ to understand the impact of incorporating WRF+BEP instead of standard WRF (bulk scheme) Figure 1. Air Quality System. Three urban classes were defined to use urban scheme (from land use database CORINE ) Figure 2. Domains WRF model and three urban classes WRF simulations were driven by the NCEP (1º x 1º - 6h) spatial – temporal resolution. Madrid metropolitan area with horizontal resolution 1 x 1 km and 30 layers (Lowest level = 7 meters) Four nested domains were used for the WRF simulations Figure 3. Differences T2, PBLH and Wind Speed • Wind BEP brings about a clear improvement of wind fields. Average bias for wind speed is practically null, in constrast with the 1.6 m/s overestimation of the BULK scheme. • T2. Both, the reference configuration and the urban parameterization overestimate ground-level temperature. Although the temporal variation patterns are perfectly described in both cases, the urban parametrization predictions are worse in all cases with an overal bias of 1.7 K (Vs. 0.7 for the reference model) • PBL Height. BEP produced higher values of PBLH (mainly in winter) in urban areas, both high and low intensity although usually the mixing height predicted by BouLac is lower during night- time. The result for non-urban land uses is basically the opposite. • The application of the BEP urban parameterization brings about a clear improvement of CMAQ predictions for all the main pollutants. It reduces the underestimation of NO 2 and PM 2.5 and the overestimation of O 3 . Despite improving biases and errors, also a better index of agreement is achieved for most of the monitoring stations. • The average NO 2 bias of the model is reduced to 1 μg/m 3 (less than 12%) for the urban stations (that provide the most relevant reference for assessment with this spatial and temporal model resolution) • The BEP (Building Effect Parametrization, Martilli et al., 2002) was selected as the option to perform WRF simulations over the Madrid city. • This urban parameterization overestimates the ground-level temperature. Wind direction bias is also worse than that of the reference configuration (Bulk scheme included in the Noah L-S model) although errors are diminished. BEP application significantly improves wind speed predictions in the city. • Wind speed can be pointed out as a key variable for AQ modelling in urban areas, since this improvement has a strong positive effect on CMAQ predictions. •The influence of the representation of mixing height is difficult to identify due to the lack of reliable observational data for this case study • Model outputs will be further scrutinized to gain a better understanting of the effects of the parameterizations used and the influence of particular schemes and input data, mainly the PBL scheme and the land uses considered in each case. • The incorporation of finer, more up-to-date land uses and urban features may further improve the results and should be tested. Figure 4. Differences NO 2 , O 3 and PM 2.5 ACKNOWLEDGEMENTS Madrid City Council for providing the meteorological and air quality data
Transcript of ASSESSMENT OF URBAN PARAMETERIZATIONS IN THE WRF … · 2018. 2. 11. · BEP-3.2-35.3 45.4 5.4...
CMAQ 4.6
Initial conditions
Boundary conditions
Photolysis
SMOKE 2.4
EmissionsInventory Surrogates
temporalspacialchemical
Point emissions
WRFV3.3.1Terrain
Meteorology
Skamarock & Klemp, 2008
Byun and Ching, 1999
Institute for the Environment, 2009
InmisiónDepositionVisibility
BEP
BULK
CMAQ 4.6
Initial conditions
Boundary conditions
Photolysis
SMOKE 2.4
EmissionsInventory Surrogates
temporalspacialchemical
Point emissions
WRFV3.3.1Terrain
Meteorology
Skamarock & Klemp, 2008
Byun and Ching, 1999
Institute for the Environment, 2009
InmisiónDepositionVisibility
BEP
BULK
•WRF outputs were used to feed the chemical-transportmodel CMAQ. To assess the implications of the WRFconfiguration in air quality, the corresponding outputs werecompared with ground-level observations of NO2, O3 andPM2.5 from 26 monitoring stations throughout the modellingdomain.• Figure 4 shows the differences of the results coming fromBULK and BEP runs for policy-relevant air quality metrics:
NO2 BEP brings about an increase of 10-18 µg/m3
in the annual mean within the city. Nonetheless, thepredictions of BEP for non-urban areas in the SW area arelower.
O3 Ground-level ozone concentrations predicted inthe BEP run are lower all over the domain, reaching amaximum annual difference of 14 µg/m3 in the city centre.
PM2.5 CMAQ predictions for PM2.5 are alsoincreased when the urban parameterization is applied.This is specially evident in winter. Differences up to 2µg/m3 in the annual mean are observed in the high-densityinnermost part of the city .
PBL Height, BEP increases PBLH predictionsin general (40 - 60 m as an average), mainly in winter.
• Beside this comparison, predicted PBLH werescrutinized (YSU - BULK and BouLac– BEP).
• Two annual runs were made with the BEP urbanparameterization and the BULK scheme, used as areference. Some of the most influential variables from theair quality point of view were compared with observationsfrom 6 urban meteorological stations: temperature (K),wind direction (º) and wind speed (m/s).
T2. BEP yields higher ground-level temperature,mainly in High residential density areas (also in lowresidential density areas not considered as urban surfacein the standard land cover used in BULK). The averagetemperature differences ranges from 0.5 to 1.2 K, mostlydue to higher temperature predictions in winter.
Wind Speed. The urban parametrization has astrong effect in wind speed predicted by WRF. Windintensity is reduced by 0.8 m/s as an average in the citycenter and up to 3 m/s in the city outskirts.
Comparison with surface observations
Comparison with surface observations Statistics
Statistics
ASSESSMENT OF URBAN PARAMETERIZATIONS IN THE WRF MODEL FOR AIR QUALITY MODELLING PURPOSES IN MADRID (SPAIN)
D. de la Paz1, R. Borge1, J.M. de Andrés1, J. Pérez1 J. Lumbreras1, M.E. Rodríguez1
1Laboratory of Environmental Modelling. Technical University of Madrid (UPM). Madrid, SPAIN
9th International Conference on Air Quality – Science and Application
24-28 March, 2014 Garmisch-Partenkirchen, Germany
INTRODUCTION METHODOLOGY WRF MODEL CONFIGURATION
METEOROLOGY
AIR QUALITY
CONCLUSIONS ReferencesNext steps
Table 3. Statistics for temperature
Table 4. Statistics for wind
URBAN [BEP]– BASE [BULK]ANNUAL MEAN
Tem
pe
ratu
re
WINTERSUMMER
PB
LHW
ind
Sp
ee
d
∆T (2m)
PBLH (m)
WSpeed (m/s)
URBAN [BEP]– BASE [BULK]
260
270
280
290
300
310
320
260 270 280 290 300 310 320
T 2m - BULK (ºK)
T 2m - BEP (ºK)
260
270
280
290
300
310
320
260 270 280 290 300 310 320
T 2m - BULK (ºK)
T 2m - BEP (ºK)
r = 0.981
MB = 1.5
r = 0.976
MB = 0.1
r = 0.983
MB = 0.8
r = 0.978
MB = 0.2
High Intensity Residential Low Intensity Residential
Mod
elle
d T
(K
º)
Observed T (Kº)M
odel
led
T (
Kº)
Observed T (Kº)
0
4
8
12
16
20
0 4 8 12 16 20
Wind Speed - BULK (m/s)
Wind Speed - BEP (m/s)
r = 0.733
MB = 1.9 m/s
r = 0.734
MB = -0.1 m/s
Observed Wind Speed (m/s)
Mod
elle
d W
ind
Spe
ed (
m/s
)
High Intensity Residential0
2
4
6
8
10
12
0 2 4 6 8 10 12
Wind Speed - BULK (m/s)
Wind Speed - BEP (m/s)
r = 0.691
MB = 1.6 m/s
r = 0.647
MB = 0.4 m/s
Observed Wind Speed (m/s)
Mod
elle
d W
ind
Spe
ed (
m/s
)
Low Intensity Residential
0
500
1000
1500
2000
2500
3000
1 3 5 7 9 11 13 15 17 19 21 23
PBLH-BULK Winter (m) PBLH-BEP Winter (m)
PBLH-BULK Summer (m) PBLH-BEP Summer (m)
Low Intensity Residential
PB
L H
eigh
t (m
)
hour
0
500
1000
1500
2000
2500
3000
1 3 5 7 9 11 13 15 17 19 21 23
PBLH-BULK Winter (m) PBLH-BEP Winter (m)
PBLH-BULK Summer (m) PBLH-BEP Summer (m)
High Intensity Residential
hour
PB
L H
eigh
t (m
)
nº stationsnº
ObservationsCanopy model MB (º)
BEP -19.8
BULK -5.9
Aggregated statistics for
Wind Direction
Error (º)
60.1
65.26 stations 46513
nº stationsnº
ObservationsCanopy model MB (m/s)
RMSE (m/s)
IOA
BEP 0.0 0.6 0.732
BULK 1.6 2.0 0.611
Aggregated statistics for Wind Speed 5 stations 39075
nº stationsnº
ObservationsCanopy model MB (º) Error (º) IOA
BEP 1.7 0.6 0.980
BULK 0.7 0.4 0.990
Aggregated statistics for Temperature 6 stations 48936
Table 5. Statistics for NO2
Table 6. Statistics for O3
Table 7. Statistics for PM2.5
ANNUAL MEAN
NO
2
WINTERSUMMER
O3
PM
2.5
PM2,5 (µg/m3)
NO2 (µg/m3)
O3 (µg/m3)
0
40
80
120
160
200
240
280
320
360
0 40 80 120 160 200 240 280 320 360
0
40
80
120
160
200
240
280
320
0 40 80 120 160 200 240 280 320
r = 0.601
MB = -7.1
r = 0.433
MB = -14.6
r = 0.510
MB = -0.6
r = 0.602
MB = -23.4
High Intensity Residential Low Intensity Residential
Mod
elle
d N
O2
(µg/
m3 )
Mod
elle
d N
O2
(µg/
m3 )
Observed NO2 (µg/m3) Observed NO2 (µg/m3)
NO2 – BULK (µg/m3)
NO2 – BEP (µg/m3)
NO2 – BULK (µg/m3)
NO2 – BEP (µg/m3)
0
40
80
120
160
200
0 40 80 120 160 200
0
40
80
120
160
200
0 40 80 120 160 200
r = 0.704
MB = 26.1
r = 0.714
MB = 38.6
r = 0.655
MB = 24.2
r = 0.703
MB = 42.9
High Intensity Residential Low Intensity Residential
Mod
elle
d O
3(µ
g/m
3 )
Mod
elle
d O
3(µ
g/m
3 )
Observed O3 (µg/m3) Observed O3 (µg/m3)
NO2 – BULK (µg/m3)
NO2 – BEP (µg/m3)
NO2 – BULK (µg/m3)
NO2 – BEP (µg/m3)
0
20
40
60
80
0 20 40 60 80
0
20
40
60
80
100
0 20 40 60 80 100
r = 0.414
MB = -10.3
r = 0.392
MB = -6.2
r = 0.467
MB = -5.7
r = 0.306
MB = -10.9
High Intensity Residential Low Intensity Residential
Mod
elle
d P
M2.
5(µ
g/m
3 )
Mod
elle
d P
M2.
5(µ
g/m
3 )
Observed PM2.5 (µg/m3) Observed PM2.5 (µg/m3)
NO2 – BULK (µg/m3)
NO2 – BEP (µg/m3)
NO2 – BULK (µg/m3)
NO2 – BEP (µg/m3)
nº stationsnº
ObservationsType station
Canopy model MB (µg/m3) MFB (%) MFE (%)
RMSE
(µg/m3)r IOA
BEP -6.8 -25.3 64.7 40.0 0.534 0.691
BULK -17.5 -45.6 70.5 40.4 0.529 0.663
BEP -1.0 -11.7 61.6 23.9 0.639 0.767
BULK -3.8 -20.9 64.8 23.8 0.604 0.744Urban station58594
7 stations
Aggregated statistics for
NO2
26 stations 220637 Traffic station
nº stationsnº
ObservationsType station
Canopy model MB (µg/m3) MFB (%) MFE (%)
RMSE
(µg/m3)r IOA
BEP 27.3 50.2 82.0 38.8 0.610 0.647
BULK 39.7 79.2 87.8 46.3 0.653 0.583
BEP 27.3 55.7 64.1 37.0 0.702 0.707
BULK 32.7 63.4 67.7 41.4 0.682 0.661
26 stations
5 stations
219078
41051
Traffic station
Urban station
Aggregated statistics for O3
nº stationsnº
ObservationsType station
Canopy model MB (µg/m3) MFB (%) MFE (%)
RMSE
(µg/m3)r IOA
BEP -8.7 -63.5 84.1 15.1 0.346 0.503
BULK -9.7 -69.8 88.0 15.8 0.264 0.459
BEP -3.2 -35.3 45.4 5.4 0.213 0.321
BULK -3.3 -34.9 45.5 5.6 0.169 0.295
3 stations
Traffic station
Urban station
26089
15058
Aggregated statistics for
PM2.5
2 stations
Skamarock, W.C. and Klemp, J.B., 2008. A time-split non-hydrostatic atmospheric model. Journal ofComputational Physics 227, 3465–3485. Byun, D.W., Schere, K.L., 2006. Review of the governing equations, computational algorithms and othercomponents of the models-3 community multiscale air quality (CMAQ) modeling systems. AppliedMechanics Review 59 (2), 51–77. Ching, J.K.S., 2013. A perspective on urban canopy layer modelling for weather, climate and air qualityapplications. Urban Climate 3, 13-39.Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscalemodels. Boundary Layer Meteorology 104, 261-304. Salamanca, F., Martilli, A., 2010. A new building energy model coupled with an urban canopyparameterization for urban climate simulations—Part II. Validation with one dimension off-line simulations.Theoretical and Applied Climatology 99, 345–356.
Geographic scope X-Y dimensions (km) Horizontal
Europe 6720 x 5952 ∆x =48 km
Iberian Peninsula 1536 x 1248 ∆x =16 km
Greater Madrid area 256 x 256 ∆x =4 km
Madrid Metropolitan area 56 x 60 ∆x =1 km
Domains for the WRF simulations
BULK BEP. Urban scheme. (Martilli, 2002)
Shortwave radiation Dudhia scheme Dudhia scheme
Longwave radiation Eta Geophysical Fluid Dynamics Laboratory (GFDL) Eta Geophysical Fluid Dynamics Laboratory (GFDL)
Land-surface model Noah LSM Noah LSM
PBL scheme Yonsei University (YSU) Bougeault and Lacarrère
Horizontal resolution ∆x =1 km (56×60) ∆x =1 km (56×60)
Vertical resolution 30 sigma levels (Lowest level ≈ 7 m) 30 sigma levels (Lowest level ≈ 7 m)
Eulerian 3D mesoscale models can consistently describe a widerange of spatial scales, from continental to urban scale. However,urban areas present features that are usually missed by land-surface and PBL modules commonly implemented in such models.As environmental issues in urban areas grow, canopy layermodelling becomes increasingly important (Ching, 2013). Modelssuch as the Weather Research and Forecasting model (WRF)(Skamarock and Klemp, 2008) incorporate urbanparameterizations to take into account changes in albedo,roughness length and thermal properties imposed by buildings.However, their application is not straightforward and should bespecifically tested in the domain of interest before they can beintegrated in air quality modelling activities. In this study, foururban parameterizations were tested for the Madrid urban area(Spain). Summer and winter, 1 km2 resolution runs wereperformed for the following parameterizations:• Urban parameterization within the Noah Land Surface Model(Bulk scheme) (Liu et al., 2006)• Building Energy Parameterization (BEP) (Martilli et al., 2002)considering two different setups for street parameters• Building Energy Model (BEP+BEM) (Salamanca & Martilli,2010)
Model outputs were compared with observations from five meteorological stationsthroughout the Madrid city, representative of different urban morphologies in two 1-month periods (winter and summer). Multi-layer, more complex parameterizationsbrought about a reduction of wind speed overestimation of the reference option(bulk) (Table.1) and slight improvements on surface temperature predictions.
Table 1. Basic statistics results for wind (summer-winter average)
MB (º) Error (º) MB (m/s)RMSE (m/s)
IOA
BULK 13.4 49.4 0.4 0.6 0.502
BEP 10.7 43.3 0.2 0.6 0.665
BEP+BEM 5.9 46.1 0.2 0.5 0.695
SpeedDirection
Canopy model Table 2. Model configuration for the WRF simulations
Win
d st
atis
tics
.
BEP was identified as the optimal setting as a compromise ofmodel performance, input data requirements and CPU time.Finally, the selected parameterization was used to produceannual meteorological inputs to feed the CMAQ tounderstand the impact of incorporating WRF+BEP instead ofstandard WRF (bulk scheme)
Figure 1. Air Quality System. Three urban classes were defined to use urban scheme (from
land use database CORINE )
Figure 2. Domains WRF model and three urban classes
WRF simulations were driven by theNCEP (1º x 1º - 6h) spatial – temporalresolution.Madrid metropolitan area withhorizontal resolution 1 x 1 km and 30layers (Lowest level = 7 meters)
Four nested domains wereused for the WRF simulations
Figure 3. Differences T2, PBLH and Wind Speed
• Wind BEP brings about a clear improvement ofwind fields. Average bias for wind speed ispractically null, in constrast with the 1.6 m/soverestimation of the BULK scheme.
• T2. Both, the reference configuration and theurban parameterization overestimate ground-leveltemperature. Although the temporal variationpatterns are perfectly described in both cases, theurban parametrization predictions are worse in allcases with an overal bias of 1.7 K (Vs. 0.7 for thereference model)• PBL Height. BEP produced higher values ofPBLH (mainly in winter) in urban areas, both highand low intensity although usually the mixingheight predicted by BouLac is lower during night-time. The result for non-urban land uses is basicallythe opposite.
• The application of the BEP urban parameterizationbrings about a clear improvement of CMAQpredictions for all the main pollutants. It reduces theunderestimation of NO2 and PM2.5 and theoverestimation of O3. Despite improving biases anderrors, also a better index of agreement is achievedfor most of the monitoring stations.• The average NO2 bias of the model is reduced to 1μg/m3 (less than 12%) for the urban stations (thatprovide the most relevant reference for assessmentwith this spatial and temporal model resolution)
• The BEP (Building Effect Parametrization, Martilli et al., 2002) was selected as the option to perform WRFsimulations over the Madrid city.• This urban parameterization overestimates the ground-level temperature. Wind direction bias is also worsethan that of the reference configuration (Bulk scheme included in the Noah L-S model) although errors arediminished. BEP application significantly improves wind speed predictions in the city.• Wind speed can be pointed out as a key variable for AQ modelling in urban areas, since this improvement hasa strong positive effect on CMAQ predictions.•The influence of the representation of mixing height is difficult to identify due to the lack of reliableobservational data for this case study
• Model outputs will be further scrutinized to gain abetter understanting of the effects of theparameterizations used and the influence ofparticular schemes and input data, mainly the PBLscheme and the land uses considered in each case.• The incorporation of finer, more up-to-date landuses and urban features may further improve theresults and should be tested.
Figure 4. Differences NO2, O3 and PM2.5
ACKNOWLEDGEMENTS
Madrid City Council for providing the meteorological and air quality data
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