neighborhood scale modeling - us epa · neighborhood scale modeling develop air quality grid models...
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Neighborhood Scale Modeling
Tanya L. Otte1, Avraham Lacser2, Jason Ching1, and Sylvain Dupont1
1NOAA/ARL & U.S. EPA/NERL, RTP, NC2Israel Institute for Biological Research,
Ness Ziona, Israel
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Objectives for Neighborhood Scale ModelingDevelop air quality grid models for risk assessments at neighborhood scales
Develop urban canopy parameterizationsfor air quality grid modeling with CMAQ
Create sub-grid pollutant concentration variability fields using PDFs
Link to human population exposure models
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Design Issues
Scale-dependent model parameterizations in momentum, TKE, surface energy balance, vertical mixing for air quality simulations
Many area, point, and line sources within urban canopy
Near-source transport, and horizontal and vertical dispersion constrained by urban canopy characteristics
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Why use a UCP?
Want to improve urban simulations for ~1 kmParameterized roughness may not adequately account for heterogeneities in urban areasUCP allows for more specific treatment of urban contributions to dynamics and thermodynamics
Ultimately want to run CMAQ to simulate photochemical pollutant species on that scale
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About the UCP
Based on Brown and Williams “drag approach”Applied in 1.3-km MM5 simulationsDirectly impacts grid cells with non-zero urbanDrag and TKE effects due to urban structuresAnthropogenic heat as time-varying functionExtinction of radiation in city canyonsRoof top contribution under development
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Implementing the UCP in MM5
40 layers: 12 in lowest 100 mTypical setup has 30 layers, 3 in lowest 100 m
Updates U, V, TKE in Gayno-Seaman PBLEnergy modifications in RRTM, solve, slabUses fractional land use categoriesAdded new “urban zones” definitions
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Pseudo-Morphology for Philadelphia
1
23
4
5
6
7
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Preliminary Results
Four experiments here…Initialized 00 UTC 14 July 1995 from 4-km, 30-layer
“nocan30”: 30-layer, no UCP“nocan40”: 40-layer, no UCP“sens7”: 7 urban categories (e.g., morphology)“sens8”: 1 urban category (wgt’d avg morphology)
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Surface Observations
NXX
TTN
WRI
ACY
PNE
PHL
MIV
ILG
NEL
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PHL Temperature — 14 July 1995
20
25
30
35
40
0 4 8 12 16 20 24
T OBST nocan30T nocan40T sens7T sens8
T (°
C)
time (h)
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MIV Temperature — 14 July 1995
20
25
30
35
0 4 8 12 16 20 24
T OBST nocan30T nocan40T sens7T sens8
T (°
C)
time (h)
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RMSE — Temperature
0.51
1.52
2.53
3.54
0 4 8 12 16 20 24
nocan30nocan40sens7sens8
RM
SE (°
C)
time (h)
nocan30: 2.17nocan40: 2.41
sens8: 1.93sens7: 2.01
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Index of Agreement — Temperature
0
0.2
0.4
0.6
0.8
1
0 4 8 12 16 20 24
nocan30nocan40sens7sens8
Inde
x of
Agr
eem
ent
time (h)
nocan30: 0.50nocan40: 0.48
sens8: 0.58sens7: 0.57
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RMSE — Moisture
11.5
22.5
33.5
4
0 4 8 12 16 20 24
nocan30nocan40sens7sens8
RM
SE (g
/kg)
time (h)
nocan40: 2.16 nocan30: 2.58sens7: 2.11 sens8: 2.21
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WRI Wind Speed — 14 July 1995
1234567
0 4 8 12 16 20 24
S OBSS nocan30S nocan40S sens7S sens8
win
d sp
eed
(m/s
)
time (h)
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WRI Wind Direction — 14 July 1995
180190200210220230240250260
0 4 8 12 16 20 24
D OBSD nocan30D nocan40D sens7D sens8
win
d di
rect
ion
(deg
)
time (h)
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RMSE — Wind Direction
0102030405060
0 4 8 12 16 20 24
nocan30nocan40sens7sens8
RM
SE (d
eg)
time (h)
All Expts: 24.3 - 24.8
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RMSE — Wind Speed
0.81
1.21.41.61.8
22.22.4
0 4 8 12 16 20 24
nocan30nocan40sens7sens8
RM
SE (m
/s)
time (h)
nocan30: 1.39nocan40: 1.52sens7: 1.32
sens8: 1.56
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Bias — Wind Speed
-2-1.5
-1-0.5
00.5
11.5
2
0 4 8 12 16 20 24
nocan30nocan40sens7sens8
Bia
s
time (h)
nocan30: +0.75nocan40: -0.91sens7: -0.31
sens8: -0.96
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Summary of UCP
UCP at 1.3-km tends to produce desired effectsChanges to wind, TKE, temperature
UCP tends to be superior to 40-layer run w/o UCP and 30-layer run w/o UCP (standard set-up)UCP with morphology tends to be superior to UCP with homogeneous city…esp. wind speed
…must evaluate more days before concluding!
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Advanced UCP
Extend drag approach to all roughness elements inside canopy (buildings and vegetation)
Couple drag approach to urban soil model (SM2-U from French SUB-MESO)
Use actual urban morphology database
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Advanced UCP ConsiderationsMomentum sources:
Horizontal and vertical surfaces of buildingsVegetation
TKE sources:Horizontal and vertical surfaces of buildingsVegetationSensible heat fluxes
Heat and humidity sources:Buildings and vegetative surfacesAnthropogenic contributions
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Urban Canopy Parameters
Mean and std dev of building, vegetation heightRoof and vegetation area densityBuilding and vegetation plan area densityFrontal area density of buildingsSurface area of wallsPlan area fraction of vegetation, roof tops, water, and paved surfaces
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Urban Canopy Parameters (cont.)Ratio of building height to widthMean orientation of streetsSky view factorRoughness lengthDisplacement heightMaterial type of building surfacesPercent impervious area directly connected to draining system
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Obtaining Urban Morphology from Lidar Mapping
Very high data density: 100,000’s points/km2
High accuracy: 15-30 cm RMSE in open areasFlexible: independent of sun angle, cloud cover“Multi-Return” allows mapping in canopy gapsRapid collection of elevation data
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Typical Flight with LIDAR
�
30 km radiusGPS Ground Station
915 m AGL
210-240 kph
Swath width = 625 m
10-30% overlap
3 m spacing111,000 points/sq.km.
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Multiple Returns
1st return
2nd return
3rd return
4th return
�Source laser beam
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Raster Building Data Set
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Defining Morphology for MM5Where full data coverage…
GIS processing of raster and vector data
Distinguish roof tops from vegetation canopy
In data-void areas…Extrapolate based on co-relationships between building histogram and land use
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Next StepsComplete development and evaluation of UCP for Philadelphia for additional daysEvaluate 1.3-km CMAQ…modifications?Continue development of advanced UCP and apply to Houston with morphology databaseExplore sub-grid variability with PDFsExplore linkages to human population exposure models