MODELING AT NEIGHBORHOOD SCALE Sylvain Dupont and Jason Ching Introduction MODELING AT NEIGHBORHOOD SCALE Sylvain Dupont and Jason Ching E-mail: dupont@hpcc.epa.gov Collaborators: Tanya Otte and Avraham Lacser University Corporation for Atmospheric Research U.S. Environmental Protection Agency Research Triangle Park, NC

Objective: modeling air-quality for estimating human exposure to air pollution in urban area. Modeling at neighborhood scale: development of an Urban Canopy Parameterization (UCP) inside MM5 for CMAQ. Estimating the sub-grid-scale variability in the pollutant concentration fields.

Outline Definition of neighborhood scale Urban Canopy Parameterization (UCP) # General scheme # Dynamic, thermal, humidity and TKE components Preliminary results: Philadelphia case # MM5 results # CMAQ results Conclusions and Perspectives

Neighborhood scale Interaction between meso and local scales. Meso scale Neighborhood scale 1 km. Roughness Sub-Layer Local scale Rural Rural Urban

The details of the whole urban canopy can not be represented: Neighborhood scale The details of the whole urban canopy can not be represented: Parameterization of the urban surface effects. Meso scale Neighborhood scale 1 km. Roughness Sub-Layer Local scale Rural Rural Urban

Majority of pollutants are emitted inside the roughness sub-layer: Neighborhood scale Majority of pollutants are emitted inside the roughness sub-layer: Necessity to have a good representation of meteorological fields. Meso scale Neighborhood scale 1 km. Roughness Sub-Layer Local scale Rural Rural Urban

Neighborhood scale The ground conditions used by mesoscale models are not satisfactory at neighborhood scale:  Drag-force approach. Meso scale Neighborhood scale 1 km. Roughness Sub-Layer Local scale Rural Rural Urban

Drag-Force approach Meso scale Neighborhood scale Rural Rural Urban I Modèle de sol urbain SM2-U Drag-Force approach Meso scale Neighborhood scale 1 km. Roughness Sub-Layer Rural Rural Urban

Urban Canopy parameterization The UCP is introduced inside the Gayno-Seaman PBL model. Complete the drag-force approach introduced by Lacser & Otte in MM5 following the work of Martilli (2002). Extend the drag-force approach to all roughness elements inside the canopy: buildings and vegetation. Introduce the detail soil model SM2-U considering both rural and urban surfaces.

Urban canopy parameterization SM2-U

Urban canopy parameterization New version of the UCP

Urban canopy parameterization Urban morphology The knowledge of the vertical and horizontal distribution of the different surface types is necessary. Roof area density Vegetation area density Building plan area density Vegetation plan area density Building frontal area density

Momentum equation = forcing terms Urban canopy parameterization Dynamic component Momentum equation = forcing terms (modification of vertical turbulent transport term) + momentum sources due to horizontal and vertical building surface + momentum sources due to vegetation

Heat equation = forcing terms Urban canopy parameterization Sensible heat flux Latent heat flux Net radiation: solar, atmospheric, and earth radiations Storage heat flux Anthropogenic heat flux Hsens i LE Gs i Qanth i Rn i Thermal components Heat equation = forcing terms (modification of vertical turbulent transport term) + heat sources from surfaces + anthropogenic heat sources

Effects of the canopy thickness Urban canopy parameterization Effects of the canopy thickness Modification of paved surface temperature equation # Heat capacity of the wall # Heat exchange between through the buildings # Radiative trapping: introduction of an effective albedo parameterization deduced from Masson (2000). Extinction of the radiation through the canopy

Humidity equation= forcing terms Urban canopy parameterization Humidity components Precipitations Infiltration Draining network Return towards equilibrium Draining Evapotranspiration Water draining outside the system Humidity equation= forcing terms (modification of vertical turbulent transport term) + humidity sources from surfaces + anthropogenic humidity sources

TKE equation= forcing terms Urban canopy parameterization TKE components TKE equation= forcing terms (modification of vertical turbulent transport and dissipation terms) + TKE sources due to horizontal and vertical building surface + TKE sources due to vegetation + TKE sources due to sensible heat fluxes

Summary of MM5 versions Roughness approach Drag approach

Preliminary results: Philadelphia case 14 July 1995 (sunny day). MM5 has been run in a one-way nested configuration: 108, 36, 12, 4 and 1.33 km horizontal grid spacing. UCP uses only for the 1.33 km domain. Turbulent scheme model: Gayno-Seaman PBL with the turbulent length scale of Bougeault and Lacarrere (1989).

1.33 km domain 112x112x40 grid points 4 km domain 85x88x30 grid points Philadelphia case 1.33 km domain 112x112x40 grid points 4 km domain 85x88x30 grid points

7 urban categories have been defined following Ellefsen (1990-91). Philadelphia case For the 1.33 km domain: 7 urban categories have been defined following Ellefsen (1990-91). 23-category (USGS) vegetation categories.

Mixing height and wind vectors at 50 m AGL Philadelphia case Mixing height and wind vectors at 50 m AGL a) the standard version of MM5 using GS PBL b) GS PBL including TLSP (B-L,89) Without UCP (nocan)

Vertical profiles in central Philadelphia, Philadelphia case Vertical profiles in central Philadelphia, Ratios: a) local u*, and b) TKE to local u* max at 2 p.m. c) potential temperature at 6 a.m. Solid line (can), dash line (nocan); Roof percentage bottom right

Meteorological fields Philadelphia case Meteorological fields Can simulations Left: mixing height Right: air temperature and wind vectors at 50 m

(can-nocan) simulations Philadelphia case Difference fields (can-nocan) simulations Left: mixing height Right: air temperature and wind vectors at 50 m

CMAQ Results MM5 v 3.5 (w/UCP). CB-IV mechanism. Introduction Philadelphia case CMAQ Results MM5 v 3.5 (w/UCP). CB-IV mechanism. Turbulent scheme from the G-S PBL scheme. CMAQ computational domain and grid structure based on MM5 domains: # 21 layer gridding for 36, 12, and 4 km simulations # 31 layer gridding for 1.33 km runs with UCP Emission processing using SMOKE # Near surface emissions distributed into lowest 10 vertical layers for 1.33 km grid simulations

Philadelphia case Normalized Difference with and without BL89 (nocan) ( 2pm EDT) (a) CO; (b) HCHO; © NOx; (d) O3 a c d b

Parameter Sensitivity Case Study Philadelphia case Parameter Sensitivity Case Study July 14, 1995 Grid size 1.33 km (Pcan – P nocan) /P can

Normalized Difference for CO (6 a.m. local) Philadelphia case Normalized Difference for CO (6 a.m. local)

Normalized Difference (6 p.m. local) Philadelphia case Normalized Difference (6 p.m. local) NOx Ozone

Normalized Difference Philadelphia case Normalized Difference for Fine Particle Number (Left: 6 a.m. Right 6 p.m.)

Multi-scale Simulations Philadelphia case Multi-scale Simulations 36 -12 -4 -1.33 km grid sizes July 14, 1995 (6 p.m. local)

Philadelphia case CO

Philadelphia case NOx

Philadelphia case Ozone

Fine Particle Number (x10 9) Philadelphia case Fine Particle Number (x10 9)

Philadelphia case Sulfate (mg/m3)

Philadelphia case Ammonium (mg/m3)

Elemental Carbon (mg/m3) Philadelphia case Elemental Carbon (mg/m3)

Aldehydes (with UCP) HCHO CH3CHO Philadelphia case Aldehydes (with UCP) HCHO CH3CHO

Neighborhood-Scale Modeling Summary Points UCP introduced into MM5 # Modified turbulence length scale parameterization in GS-PBL model: Suppresses undesired undulations #Improved Dispersion parameters: Mixing heights, U*, stability, … Air quality fields # Sensitivity to introduction of UCP # Spatial pattern details resolved at N-S # Resolution requirements differ for different pollutants

Project Status, Future plans Testing and refining UCPs in MM5 and CMAQ Develop PDFs for sub- grid variability for different parent grid resolutions Work-in-Progress: Prototype study Preliminary results for Philadelphia Advanced N-S modeling for Houston, Texas Detailed urban morphology data base