Fine Scale Meteorology & Air Quality Models - School of Architecture ...

ASI2_Lecture 3_7/12/2011 

Croucher Advanced Study Institute 2011 

Urban Climatology for Tropical & Sub-tropical Regions 

Fine Scale Meteorology & Air Quality Models 

Urban Forecasting, planning and assessment tools 

Jason Ching 

Senior Research Fellow, UNC 

Institute for the Environment, 

Chapel Hill, North Carolina, USA 

URBAN AIR POLLUTION 

Lecturer’s Photo 

ASI 2: URBAN CLIMATE AND AIR POLLUTION School of Architecture, The Chinese University of Hong Kong, Hong Kong, 7-8 Dec 2011 

Introduction and Background: 

Presentation Outline 

• Part 1: Introduction and Context 

• Part 2: Mesoscale Modeling 

– Part 2a: Mesoscale RANS-Urban canopy-based modeling 

– Part 2b: Urban canopy descriptions and data 

– Part 2c: Fine scale mesoscale modeling issues 

• Part 3: Air Quality Modeling: Framework for characterizing 

(AQ) sub-grid features 


1


Part 1: Introduction: Multi-scale modeling 

Modeling Tools 

• Meteorological modeling 

• Air quality modeling 

• Sub-grid context 

• Urban modeling 

• Canopy, in-canyon modeling 

• LES 

• CFD 

• Local Scale-Hybrid modeling 

Modeling System Development 

•Meteorological Model Developments: 

– Hurricane Model-> MM5-> WRF-> MPAS 

Multi-scale WRF System 

•General Framework 

•PBL Formulations 

•TKE vs Non-TKE schemes 

•Land-surface models 

•Urbanization in WRF 

•UHI Sensitivity studies 

•Air Quality Model Developments 

– Dispersion models-> photochemical grid models 

-> Acid Deposition model -> Community 

Multiscale One-Atmosphere AQ/Deposition 

model 

•Climate Model Developments 

– GCMs-> Community Climate models-> 

Community Earth systems model 


Common Community 

Model Design Features 

Examples: 

WRF (Meteorological) 

CMAQ (Air Quality) 

Preprocessors -> Core -> Postprocessors 

Maintenance, Version Controls 

Open System 

Open Source, 

Dynamic Development Framework 

User Friendly 

Community Support Infrastructure 

Multiscale: Gridding/Nesting capabilities 

User Setups & Controls 

Science evolves; Module options grows 

Complementary Documentation 

Meteorology and AQ feedbacks possible 


2


Multi-Scale Urban Air Quality simulations 

Pollutant concentration resolution based on CMAQ grid size 

Example: Nested runs for NO x (top set) and Ozone (bottom set): 

Philadelphia modeling domain, July 14, 1995 


WRF: Integrated Cross‐scale Modeling Framework 

WPS System 

Enhanced Inputs 

WRF‐V3 System 

Enhanced modeling components 

Advanced Applications 

In‐situ and remote‐sensing 

urban land‐use and building 

characteristics, anthropogenic 

heat and moisture. 

Urbanized high‐resolution 

land data assimilation system 

(u‐HRLDAS) 

Fine‐scale atmospheric 

analysis (FDDA) 

Noah land surface model 

Urban canopy models 

Indoor‐outdoor exchange models 

Urban T&D models (CFD, LES) 

Chemistry models (WRF‐Chem) 

Human‐response modeling 

Weather prediction 

Air quality 

Regional climate 

Public health and risk 

assessments 

Urban water resources 

Adaptation and UHI 

mitigation 

Decision support 

Chen et al., JOC,2010 


3


Part 2a: Multiscale dependent WRF 

Example: PBL Ht (m) Aug 4, 2006 @ 1500 CDT 

27km 

9 km 

3 km 

1 km 

BEP 

Wave-like, convective patterns are different in scale 


WRF Physics Options 

• Turbulence/Diffusion (Km Options, Diff 

Options) 

• Radiation 

– Long Wave 

– Shortwave 

• Surface 

– Surface layer 

– Land/water surface 

• PBL 

• Cumulus Parameterization 

• Microphysics 


4


PBL schemes provide ensemble statistical 

representation of turbulence in mesoscale models 

Ensemble statistics model vs. LES 

S(ww) -5/3 

resolvable scales 

SGS 

~ 1 km ~100 m ~10 m < cm 

NWP grid >> Λ 

Λ 

LES grid


Land Surface Modeling 

• Accounts for subgrid-scale sensible and latent 

fluxes 

• LSM becomes increasingly important: 

-More complex PBL schemes are sensitive 

to surface fluxes and cloud/cumulus schemes 

are sensitive to the PBL structures 

NWP models decrease their grid-spacing (1-km 

and sub 1-km). Need to capture mesoscale 

circulations forced by surface variability in 

albedo, soil moisture/temperature, landuse, 

and snow 

• Challenges: 

Tremendous land surface variability 

Complex land surface/hydrology processes 

Initialization of soil moisture & temperature 

WRF LSM provides surface energy flux 

- ground heat storage Q G 

- surface sensible heat flux Q H 

- surface latent heat flux Q E 

-upward longwave radiation Q lu 

Alternatively: 

-skin temperature and sfc emissivity 

-upward (reflected) shortwave radiation, 

Q S 

- surface albedo, including snow effect 


Land Surface Options in WRF (5) 

• =1 sf_surface_physics: 5-layer 

thermal diffusion model, no prediction of 

soil moisture, snow, and vegetation. 

• =2 sf_surface_physics: Noah LSM 

(Unified ARW/NMM Version 3) 

• Vegetation effects 

• Predicts soil temperature and soil 

moisture in four layers and diagnoses 

skin temperature, 

• Predicts snow cover and canopy 

moisture, handles 

• Fractional snow cover and frozen soil 

• New time-varying snow albedo (in V3.1) 

• Noah is coupled with two Urban Canopy 

Model (UCM) options (sf_urban_physics, 

ARW only) 

=1 sf_urban_physics: single layer 

UCM (SLUCM) 

=2 sf_urban_physics: Multi-layer 

Building Environment 

Parameterization (BEP), Used with MYJ 

or BouLac to represent buildings 

higher than lowest model levels 

=3 sf_urban_physics: Building 

Energy Model (BEM). Works with 

BEP. 

• =3 sf_surface_physics: RUC LSM 

• • Vegetation effects included 

• • Predicts soil temperature and soil 

moisture in six layers 

• • Multi-layer snow model 

• =7 sf_surface_physics: Pleim-Xiu 

LSM (EPA) 

• • New in Version 3 

• • Vegetation effects included 

• • Predicts soil temperature and soil 

moisture in two layers 

• • Simple snow-cover model 

• =88 sf_surface_physics: GFDL slab 

model. Simple land treatment 


6


Part 2b: Urban 

Modeling 

• Requirements for urban 

canopy PBL schemes 

• Gridded databases for 

canopy model 

formulations 

• Options in WRF are: 

SLUCM, BEP, 

BEP_BEM 

• Implementing NUDAPT 

in WRF 


Melbourne 

Slide courtesy of Grimmond 

Chicago 

Modifed Oke 1997 

Scales 

Chicago Gothenburg 

Scales 

BremenASI 2: URBAN CLIMATE AND AIR POLLUTION School of Architecture, The Chinese University of Hong Kong, Hong Kong, 7-8 Dec 2011 

7


Impact of UCPs on Mesoscale modeling: 

Mixing height and wind vectors at 50 m AGL 

a) the standard version of MM5 using G-S PBL 

b) GS PBL including UCP w/BouLac 

a) 

Mixing Height at 6 p.m. 

b) 

Mixing Height at 6 p.m. 

X 

100 

80 

60 

40 

20 

0 

0 20 40 60 80 100 

Y 

(in meter) 

2000 

1800 

1600 

1400 

1200 

1000 

800 

600 

X 

100 

80 

60 

40 

20 

5m.s -1 0 

0 20 40 60 80 100 

Y 

(in meter) 

2000 

1800 

1600 

1400 

1200 

1000 

800 

600 

5m.s -1 


Impact of UCPs on 

Mesoscale modeling 

Difference fields 

(UCP- noUCP) MM5 simulations 

LHS: mixing height 

RHS: air temperature, wind 

vectors @50 m AGL 

X 

X 

X 

a) 6a.m. 

100 

b) 2p.m. 

100 

c) 6p.m. 

100 

0 

0 20 40 60 80 100 

Y 

0 

0 20 40 60 80 100 

Y 

0 

0 20 40 60 80 100 

Y 


80 

60 

40 

20 

80 

60 

40 

20 

80 

60 

40 

20 

(in meter) 

250 

200 

150 

100 

50 

0 

-50 

-100 

-150 

-200 

-250 

(in meter) 

250 

200 

150 

100 

50 

0 

-50 

-100 

-150 

-200 

-250 

(in meter) 

250 

200 

150 

100 

50 

0 

-50 

-100 

-150 

-200 

-250 

X 

X 

X 

100 

80 

60 

40 

20 

100 

80 

60 

40 

20 

100 

0 

0 20 40 60 80 100 

Y 

0 

0 20 40 60 80 100 

Y 

80 

60 

40 

20 

6a.m. 

2p.m. 

6p.m. 

0 

0 20 40 60 80 100 

Y 

(in K) 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

-0.5 

-1 

0.2 m.s -1 

(in K) 

1 

0.8 

0.6 

0.4 

0.2 

0 

-0.2 

-0.4 

-0.6 

-0.8 

-1 

0.2 m.s -1 

(in K) 

1 

0.8 

0.6 

0.4 

0.2 

0 

-0.2 

-0.4 

-0.6 

-0.8 

-1 

0.2 m.s -1 

8


Sources of building data 

High resolution (meter scale) building data are technologically 

and operationally feasible to obtain; datasets are becoming 

increasingly more available and affordable. Such data provide 

the bases for advanced contemporary grid modeling and for 

each specific urban area. 

Photogrammetric methods 

Airborne LIDAR platforms 


High resolution urban mesoscale modeling 

Digitization of urban details now feasible/desirable 

Salt Lake City 

Changing Morphology 

Hong Kong, 

Courtesy: Chan, Lau and Fung 

Nanjing, a rapidly evolving city 

Courtesy of Tijian Wang 


9


Urban Modeling in WRF-Noah 

Three parameterization schemes 

Multi-layer Urban Canopy Model: 

Building Effect Parameterization 

(BEP) 

• Direct interactions with WRF PBL 

scheme at multiple vertical layers 

• Calculate effects of buildings on 

momentum and heat fluxes 

• Modify TKE scheme and turbulent 

length scales 

• Available since WRF3. 1 

sf_surface_physics = 2 (Noah) 

sf_urban_physics = 2 

• works with WRF’s BouLac and MYJ 


Implementation of BEP & BEP_ BEM in WRF 

• Building Environment Parameterization (BEP, Martilli et al.) 

– Sub-grid wall, roof, and road effects on radiation and fluxes 

– Can be used with MYJ PBL or BouLac PBL to represent buildings higher 

than lowest model levels (Multi-layer urban model) 

• Building Energy Model (BEM, Martilli and Salamanca) 

– Includes anthropogenic building effects (heating, air-conditioning) in 

addition to BEP 

– The natural ventilation, the heat generated by equipment and 

occupants, the convective heat through the walls, and the radiation 

through the windows are considered in the model. 

– The UCP(BEP) and BEP-BEM (with and without the AC systems) 

schemes have been evaluated against urban energy balances fluxes 

measured in the BUBBLE experiment. 


10


HYPOTHESIS: 

Anthropogenic 

heating is important 

model variable in 

urban heat island 

simulations 

RMSE for T2 decreased 

26% (18 stations in 

Houston) using BEP+BEM 

vs BEP alone 

Martilli et al, 2011 

Building Energy Model (BEM) in WRF 

representing indoor-outdoor heat exchanges 

• Time-varying room air temperature and air humidity are estimated 

• Natural ventilation, heat generated by equipment and occupants, 

heat transfer through the walls, and radiation through windows 

• Heat generated from cooling(air conditioning)/heating the indoor air 

• Available since WRF3. 2 

sf_surface_physics = 2 (Noah), sf_urban_physics = 3 

ASI 2: URBAN works CLIMATE with BEP AND and AIR BouLac POLLUTION and MYJ 

School of 

PBL 

Architecture, 

onlyThe Chinese University of Hong Kong, Hong Kong, 7-8 Dec 2011 

NUDAPT ‐ 44 UCPs 

• Mean, Std Dev of building heights 

• Plan-area weighted mean building 

height 

• Height Histogram (5-m increments) 

• Plan area fraction (Bldg area total sfc 

area) 

• Plan area density (Air volume by 

Bldg) 

• Roof area density (Like LAI) 

• Complete aspect ratio A sfc /A T 

• Frontal area index (dd)) A proj /A T 

• Frontal area density (dd) FAI/height 

• Height-to-width ratio (some) 

• Sky view factor (some) 

• & Roughness length 

• & Displacement height 

BEP, BEP_BEM UCPs 

• Street width , w 

• Building width, b 

• Distribution of building heights 

• Street direction (either N-S and E-W) 

• Urban fraction (from other tables 

based on Land use) 

• Thermal parameters (heat capacity, 

thermal diffusivity 

• Roughness lengths 

• Albedo 

• Emissivity 


11


NUDAPT gridded UCP database being installed at NCAR 

for supporting WRF urban model applications 

11 UCPS for 44 US cities 

gridded at 1km & 250m 

derived from NIMA’s airborne 

lidar data sets 

(Courtesy NBSD: Burian &Brown 

Installed into 

WPS in CONUS 

(enlarged to 

include Honolulu) 

Supports SLUCM, 

BEP, BEP+BEM, & 

future urban options 

Albuquerque Des Moines Minneapolis Raleigh-Durham 

Baltimore Detroit New Orleans Richmond, VA 

Beaumont Fort Lauderdale New York Riverside, CA 

Boston Herndon-Dulles Oakland, CA Salt Lake City 

Buffalo Honolulu Oklahoma City San Antonio 

Chicago Houston Orlando San Diego 

Cincinnati Jacksonville, FL Philadelphia San Francisco 

Cleveland Kansas City, MO Phoenix Savannah 

Dallas Las Vegas Pittsburgh Seattle 

Daytona Beach Los Angeles Portland, OR St Louis 

Denver Miami Providence Washington ,DC 

Example of UCPs in NUDAPT: Harris County‐Houston 

1 km gridded fields from processed digitized lidar data 

300 

250 

Heig ht (m ) 

200 

150 

North 

West 

Northwest 

100 

50 

0 

0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 

af(z) 

Gridded Frontal Area (packing 

density) Index as a function of 

height and approach angle of 

wind (based on Lidar data). 


12


Geo‐Referencing NUDAPT‐44 database for WRF 

Large (1 o ) and Small (0.25 o ) Grid Tiles in WPS 


rd_wf_binary.f90 

Processing steps 

NUDAPT into WRF 

Intermediate Mesh 

Met_em 

File 

geogrid.exe 

real.exe 

WRF_input 

Module_physics_i 

nit 

Module_sf_urban 

Module_sf_noahdr 

y 

Call urban_var_init 

Use module_sf_urban 

Call 

BEP_BEM 

Call 

Module_sf_bep_bem 

Module_sf_bep 

ASI 2: URBAN CLIMATE AND AIR POLLUTION School of Architecture, The Chinese BEPUniversity of Hong Kong, Hong Kong, 7-8 Dec 2011 

13


BEP ‐ 2228 BEP_BEM ‐ 2238 

1500 CDT 

1900 CDT 

2300 CDT 

BEP: UHI of 1-2 

degrees is modeled 

during daytime, but 

nocturnal UHI was 

muted. 

BEP_ BEM: Stronger 

UHI during day, and 

also some UHI during 

night, even if after 

midnight the UHI 

begins to weaken 


0300 CDT, 26 August 2006 PBL=8 (Bougeault and Lacarrere) 

BEP only: 2228 BEP_BEM: 2238 

Introduction of NUDAPT enhances the magnitude 

and gradients of the UHI. The heterogeneities of 

the city are also better represented. 


14


Cooling California Cities: 

Cases refer to different ranges of increasing albedo 

and canopy coverage (surrogate for moisture) 

Taha 2008, BLM 127: 218‐239 

Delta T (K) 

-0.5 

-1 

-1.5 

-2 

-2.5 

-3 

-3.5 

-4 

San Fernando Valley 

0 

23 

7 

15 

23 

7 

15 

23 

7 

15 

23 

7 

15 

23 

case-01 

case-02 

case-10 

case-20 

case-22 

LST 

Los Angeles 

1 

0 

Delta T (K) 

-1 

-2 

-3 

23 

-4 

7 

15 

23 

7 

15 

23 

7 

15 

23 

7 

case-01 

15 

23 

case-02 

case-10 

case-20 

case-22 

LST 

San Diego 

0 

Delta T (K) 

-0.5 

-1 

-1.5 

-2 

-2.5 

23 

-3 

7 

15 

23 

7 

15 

23 

7 

15 

23 

7 

15 

23 

case-01 

case-02 

case-10 

case-20 

case-22 

LST 


Air Quality response to UHI modeled modifications: 

Comparison against base case for Sacramento CA 

Taha 2008: BLM V127: 218‐239 

Base case [O3] 

Base- Change 

(albedo change) 

Change in [O3] from base case at time of peak. 

Increased urban albedo scenario (corresponds to a reduction 

of 2‐3C in air temperature) 


15


Potential exposure assessment application: 

Theory- Pollution retention in urban canyons 

Courtesy of J. Richmond-Bryant 

U 

U 

WIND 

D 

l 

WIND 

D 

W 

• H = Uτ/D = f(UD/ν, k 0.5 /U, l/D, D/W) 

= f(Re, turbulence intensity, shape) 

o H = nondimensional residence time of pollutant in canyon 

o τ = residence time 

o k = turbulence kinetic energy of the wind 

o ν = kinematic viscosity 

o Re = Reynolds number 

• Based on dimensional analysis and derived from the equation 

of scalar flux transport (Humphries & Vincent 1976) 


Residence time H vs D/W 

Courtesy of J. Richmond-Bryant 

Mid Manhattan05 

• Significant fit: 

o H = 22(D/W) -0.69 

o R 2 = 0.62 

o p < 0.001 

Two Cities MM and 

OkCity 

• Poor fit: 

o H = 51(D/W) -0.812 

o R 2 = 0.035 

o p = 0.022 

JU2003: OklCity 

• Scatter visible 

• Novel methodology, under development 

• Significant fit: 

• Qualitatively similar results for two 

o H = 296(D/W) cities 

-0.812 

o R 2 = 0.48 

• Two cities differ quantitatively 

o p < 0.0001 

• Parameterizations can utilize fine scale 

modeling to provide inputs for exposure 

assessment 

• Improved methodologies with multiple 

canopy formulation sets such as from 

ASI 2: URBAN CLIMATE AND AIR POLLUTION NUDAPT to be tested. 

School of Architecture, The Chinese University of Hong Kong, Hong Kong, 7-8 Dec 2011 

16


Crossroads: 

Advances and Opportunities 

• Urban predictions on now on firmer bases with UCP formulations, 

model formulations can become even more sophisticated 

• New opportunities (base analyses & “what if” scenarios include: 

–Advanced AQ modeling, exposure assessments 

–Improved urban dispersion, threat assessments 

–UHI assessments, urban climate analyses 

–Hydrological and flooding assessments 

–Urban design and architecture for ventilation and comfort 

analyses 


Part 2c: Fine Scale Modeling Issues 

Goal: Realistic outputs from 

fine grid WRF mesoscale 

modeling. 

• Evolution of mesoscale 

•Capabilities: Available/ growing computer 

capabilty/capacity and evolving modeling 

system 

•Issues: Grid sizes now of same order or 

smaller as pbl heights, turbulence 

(Wyngaard’s “terra Incognita” regime) 

•Problems: Fine grid modeling needed to 

resolve observed features 

•Challenges: Determining and removing or 

reducing the levels of stochastic model noise 

•Approaches: WRF with parameterized SG 

physics options 

WRF with LES 

Conduct PULSE Study 


17


WRF modeled vertical velocity at mid‐PBL (~500 m) PBL Eddies 

38.7 

( courtesy Lemone et al, WRF Workshop, 2011 , updated) 

38.1 

37.5 

36.9 

‐98.6 ‐98.0 ‐97.4 ‐96.6 ‐96.0 ‐95.4 ‐98.6 ‐98.0 ‐97.4 ‐96.6 ‐96.0 ‐95.4 

38.7 

38.1 

37.5 

36.9 

36.3 

‐98.6 

‐98.0 ‐97.4 ‐96.6 ‐96.0 ‐95.4 

‐98.6 

‐98.0 ‐97.4 ‐96.6 ‐96.0 ‐95.4 

Observations in weakly‐forced convective boundary layers: 

Quasi‐stationary 2‐ & 3‐D PBL meteorological fields 

http://lance.nasa.gov/imagery/rapid‐response/ 

Eastern USA, October 2010 

Daily summertime occurrence in Houston,TX 

area (2006): (MODIS:Terra/Aqua Satellite ~ 

local noon) 

Aug 3 Aug 4 

Aug 5 

Aug 9 Aug 11 Aug 15 

Aug 18 Aug 20 Aug 24 


18


ACM-2 

BouLac 

QNSE 

TKE-NO 

TKE-YES 

TKE-YES 

YSU 

TKE-NO 

Results Sensitive 

to PBL schemes 

TERRA: Pixel Size 500m 

Aug 4 2006 @ 17:20UTC 

Simulations @ 20:00 UTC 

MYJ 

MYNN2 

MYNN3 

TKE-YES 

TKE- 

TKE- 


Grid size dependent results 

4km(upper) vs 1 km Base (lower) 

Heat Flux Latent Heat PBL height Ustar 

Fine scale structures are unresolved at 4 km, even though features are of that order. Resolution becoming 

apparent for grids at 1km (or less). From Rampanelli et al., (2004), with 1km grids, shallow circulations can 

appear due to convective instability (superadiabatic layer) at the minimum resolved scale (4dx). At the 

coarser horizontal grids typical of mesoscale models, such circulations would grow too slowly to be noticed. 

Hence at 1km the simulations are in a resolution regime where turbulence cannot be explicitly resolved, yet 

the superadiabatic ASI 2: URBAN CLIMATE layer can AND induce AIR POLLUTION circulations that School grow of Architecture, to noticeable The Chinese University size over of Hong the Kong, integration Hong Kong, 7-8 Dec period. 2011 

19


TERRA: Aug 4 2006 @ 1720 UTC 

WRF V3.2 (NOAH BEP_BEM BouLac) 

SW Down Base 2238 PBL Base 2238 

Martilli’s filter scheme 

applied to base case. 

SW Down Filtered Base 2238 PBL Filtered Base 2238 

Objective: To reduce 

amount of model noise 


Preliminary Study Findings 

• Low level convective features are frequent and regular features 

in study area for weakly forced systems based on cloud fields 

from satellite) 

• Convective features are resolvable with fine scale, less so as 

grid size increases, characteristics depend on physics options 

• Modeled simulations shows convective features to be quasi 

stationary 

• Spatial characteristic are sensitive to choice of physics options. 

Presents a significant challenge to modeling air quality at fine 

scales 

• Ad hoc filtering approaches might be useful towards reducing 

model “noise” 

• Concern: What is resolved, what is 

noise 


20


erturbations nique inks to etup xploration 

Taking the model’s 

• Fine scale modeling enters the “Terra Incognita” regime 

• Given that mesoscale modeling systems have many 

operating options, how are the resulting simulation 

dependent on the user selection of model options. 

• Need to understand the properties of modeling systems 

dependence on the properties of their individual 

components and their linkages. 

• Ad hoc modeling group is currently undertaking this study, 

preliminary results follows. 

• Perform & explore model sensitivity studies including: 

Model physics, numerics, time steps, grid size, filtering 


Numerical Considerations: Horizontal Diffusion 

Standard configuration of WRF has in the horizontal the Smagorinsky scheme 

K 

h 

s 

C x 

2 

s 

C 0.4 

2 

u 

v 

 

0.25 

2 2 

 

 

x 

y 

 


2 

2 

u 

v 

 

 

 

y 

x 

 

 

In Martilli’s simulations, with DX=1km, this gives K h values of the order of 100-200 m2/s 

Standard MM5 has in the horizontal (see Xu et al. MWR 2001) 

K 

K 

h 

H 0 

K 

H 0 

3. 10 

2 

2 

 

2 2 u 

v 

u 

v 

 

Cs 

x 

0.25 

2 2 

 

 

x y 

 

 

y x 

 

 

3 

x 

t 

2 

2 

600. 

m 2 //s 

s 

For Dx=1000.m and Dt=5s (Martilli’s case) 

“the background term K H0 is dominant at all levels and locations 

throughout the simulation time” Xu et al. MWR 2001 

21


WRF simulations over Madrid (1 km resolutión) 1200 UTC 

K H Smagorinsky Smagorinsky CONST: With K HO =600 m 2 /s (MM5 style) 

Temperature 

(2m) 

CONST 

Vertical velocities 

(250m) 

Vertical 

velocity 


Alternative methodology: 

In addition to filtering structures smaller than the grid cell, we also 

filter turbulent eddies smaller than the size of the most energetic 

eddies. Because we are running in RANS mode, all turbulence should 

be filtered out by the turbulence closure scheme. Martilli’s choice is 

to improve the treatment of the horizontal turbulent transport terms. 

u 

uu 

uv 

uw 

P 

uu 

uv 

uw 

 

t 

x 

y 

z 

x 

x 

y 

z 

Mean advection and pressure: resolved 

Can we still neglect these terms for high 

resolution (1km or less) runs 

Horizontal turbulent 

transport: neglected 

Vertical turbulent transport: 

parameterized 

Some ad hoc 

horizontal diffusion 

is often added 

Try to parameterize them: if they are not relevant they will show no impact on the 

ASI 2: URBAN CLIMATE AND AIR POLLUTION School results. of Architecture, The Chinese University of Hong Kong, Hong Kong, 7-8 Dec 2011 

22


Suggested parameterization 

u u 

u u K 

 

i j 2 

Best option would be to treat 

 

i j 

ijtke 

x 

j 

x 

 

 

i 

3 

As first step, Martilli assumed 

ui 

u 

iuj 

K 

Relatively easy to implement in WRF…. 

x 

j 

For “ K” Martilli invoked the Bougeault and Lacarrere scheme, so 

the vertical diffusion coefficient becomes 

K 

z 

 

cl 

boulac 

tke 

By analogy, in the horizontal 

K c maxl 

, x tke 

x 

boulac 

 

z 

zdz 

tkez 

 

down 

 

z 

zdz 

tkez 

 

z o 

1/ 

2 

l 

upldown 

 

minl 

,l 


z 

 

zlup 

z 

zl 

l 

l 

 

boulac 

g 

o 

g 

up 

down 

l up and l down are the distances that a parcel 

originating from level z, and having the 

TKE of level z, can travel upward and 

downward before coming to rest due to 

buoyancy effects. 

Application of Filter Option 

Base 

New 

23


Model study of Vortices in high-resolution WRF 

Courtesy of E. Gutierrez, J. Gonzalez, CCNY, & R. Bornstein, SJSU 

Simulations of a 2010 NYC Heat Wave: Real or Noise 

z=12 m, 18 EST; coarse domain, ∆x=9 km, ∆t = 1 s: 

Temperature (12m) 

W velocity (12m) 

Reasonable results, but not much detail 


Sensitivity to ∆x 

Temperature (12m) 

∆t fixed at 1 s 

Vertical velocity (12m) 

∆X=1 km 

∆X=333 m 

∆X=1 km 

∆X=333 m 

W (12m) X-section thru 

Manhattan 

∆X=1 km 

∆X=333 m 


24


Sensitivity to ∆t; ∆x = 1 km 

Vertical Velocity x-section through Manhattan 

∆t=6 s 

∆t=1 s 

∆t=0.66 s 

∆t=0.11 s 


Terra Incognita 

LES and mesoscale limits 

From Wyngaard 

•If the (integral) scale of the turbulence is l, then in the LES limit, i.e., ∆ ≪ l, the numerical grid resolves all of the 

energy-containing turbulence. 

•The subgrid model is responsible only for energy transfer from the resolved scales. The standard Smagorinsky 

(or Lilly) subgrid model produces this transfer at the correct mean rate. 

•In the mesoscale limit, ∆ ≫ l, no turbulence is resolved. Turbulence effects are represented through a subgrid 

model, typically of the ensemble-mean type. In principle the subgrid model can be adjusted to perform well in this 

limit. 

•Between these two limits lies the Terra Incognita where the subgrid model carries significant fluxes and 

transfers energy. In principle neither ensemble mean nor the traditional LES closures are appropriate there. 


25


Summary: Fine grid modeling issues 

• A Conundrum: Real features of 1-10 km scales require grid resolution 

about an order of magnitude finer in size; however, much stochastic 

noise is present in current mesoscale simulations at fine scales (Terra 

Incognita regime). 

• Quasi Stationary, persistent 2-D and 3-D circulation in weakly forced flows 

• Simulations are physics option dependent 

• K H using SMAG (allows rolls), K H SMAG + Constant (supresses rolls) 

• Horizontal vortices and/or structures: 

– Become more pronounced with decreasing ∆x = 1km, 333m & 111m 

– Dissipate for decreasing ∆t= 6s,1s, 0.66s, 0.33s (features are non convergent) 

– Reaches max strength in afternoon, dissipate at night, are computationally stable 

• Numerical filters may helpful in reducing stochastic model noise, but 

there is no fundamental guidance that currently apply. 

• Other aspects/sensitivities, approaches still need to be explore: 

Even vs odd order filters. 

WRF LES 


Part 3: Fine grid air quality modeling (examples CMAQ) 

1 km UCP 4km Native 

Ozone NOx 

• 1 km results are 

significantly more 

textured than 4 

km 

• The titrating effect 

of NOx on ozone 

(especially along 

highways and 

major point 

sources) are 

captured at 1km 

but not at 4 km 

• Photochemistry at 

4 km results in 

part from overdilution 

of withingrid 

sources 


26


Dependence on UCP 

Ozone (1 km grid CMAQ simulations) @ 2100 GMT 

UCP no_UCP ∆ (UCP-no_UCP) 

• Significant differences in the spatial patterns shown between 

UCP and noUCP runs (titration effect occurs in both sets) 

• Flow, thermodynamics & turbulent fields differ between the 

UCP and noUCP simulations & contribute to differences 


Simulations are unique to grid size: 

Example: Ozone: Fine vs Coarse (2100 GMT) 

Aggregate: 4km mean 

from 1km (w/ucp) 

Native 4 km simulation 

∆ (Aggregate-Native) 

• Differences between Mean 4km aggregated from 1 km vs Native 4km 

• Accumulative and net of atmospheric processes acting on 1 km scale 

differs from that at 4km scale 


27


Characterizing, Modeling Sub-Grid Features 

Ching and Majeed, 2011 

Increasing level of detail of the pollutant concentration distribution possible with 

decreasing model grid size. 

In principle, even finer concentration detail occur below 1 km grid simulations. 

Histogram of concentrations is derived from nested grid concentration simulations. 


Two-parameter Weibull probability density function 

Hereinafter “Distribution Function” 

k > 0 is the shape parameter 

λ > 0 is the scale parameter 


28


HCHO Histograms and fitted Weibull distributions 

12km grids, Wilmington Delaware July 2001. Based on 1km CMAQ 


1.2 

1 

Cell11:2 

Cell12:2 

Wilmington - Weibull Scale Factors 

Cell11:3 

Cell12:3 

0.8 

0.6 

0.4 

0.2 

0 

1 101 201 

Time 

301 

(Hours) 

401 501 601 

25 

20 

Wilmington - Weibull Shape Factors 

Cell11:2 

Cell12:2 

Cell11:3 

Cell12:3 

Shape Factor 

15 

10 

5 

0 

1 101 201 301 401 501 601 

Time (Hours) 


29


Cell row: Cell column R 2 

Regression model of Shape & Scale 

Wilmington grid cell 11:2 

Shape Linear-form Log-form 

11:2 (SW) 0.26 0.28 

11:3 (SE) 0.27 0.35 

12:2 (NW) 0.18 0.17 

12:3 (NE) 0.08 0.09 

Cell row: Cell column R 2 

Scale Linear-form Log-form 

11:2 (SW) 0.65 0.73 

11:3 (SE) 0.62 0.81 

12:2 (NW) 0.65 0.77 

12:3 (NE) 0.68 0.80 

C 12km = Concentration (12-km cell value) 

E 12km = Emissions (12-km cell value) 

σ E1km = Std. Dev. of Emission (based on 1-km cell 

value) 

Skew E1km = Skewness of Emission (based on 1-km cell 

value) 

Kurt E1km = Kurtosis of Emission (based on 1-km cell value) 

U* 12km = Ustar (12-km cell value) 

W* 12km = Wstar (12-km cell value) 

PBL 12km = PBL height (12-km cell value) 

WS10 12km = 10m Wind Speed (12-km cell value) 

WD10 12km = 10m Wind Direction (12-km cell value) 

T10 12km = 10m Temperature (12-km cell value) 

Q10 12km = 10m Mixing Ratio (12-km cell value) 

Linear form: 

Shape = 0.28+0.33 C 12km -0.19 E 12km +0.26 σ E1km -0.74Skew E1km +0.75Kurt E1km -0.33 U* 12km +0.10PBL 12km 

+0.36WS10 12km -0.09WD10 12km -0.14T10 12km +0.08Q10 12km 

Scale = 0.13+0.69 C 12km +0.11 E 12km -0.04Kurt E1km -0.27U* 12km +0.21W* 12km -0.10 PBL 12km + 0.04 T10 12km 

Logarithmic form: 

Shape = -0.22+0.37 C 12km –7.4x10 -05 E 12km -0.33 σ E1km -0.30k E12km +1.50Skew E1km +0.77Kurt E1km 

+0.14WS10 12km -0.01WD10 12km -0.17Q10 12km 

Scale = 1.25+0.74 C 12km +4.8x10 -05 E 12km +0.08 σ E1km -1.34k E12km 2.60Skew E1km +0.74Kurt E1km -0.19WS10 12km 

ASI 2: URBAN -0.01WD10 CLIMATE 12km AND -0.07Q10 AIR POLLUTION 12km, 

School of Architecture, The Chinese University of Hong Kong, Hong Kong, 7-8 Dec 2011 

Fitted Shape and Scale vs regression model 

1 

0.8 

0.6 

Series1 

Series2 

Series3 

0.4 

0.2 

0 

‐0.2 

1 101 201 301 401 501 601 

‐0.4 

1 

0.8 

0.6 

0.4 

0.2 

0 

‐0.2 

Series1 

Series2 

Series3 

1 101 201 301 401 501 601 

Shape based on Regression 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 0.2 0.4 0.6 0.8 1 

‐0.4 

Best it Shape 


30


SGV parameterization model 

• Framework and template based upon a given ensemble set of a 

priori simulations. 

• Framework and template designed to provide on-line 

complimentary gridded output to coarse (operational) CMAQ 

simulations 

• Given growing availability of annual regional-scale CMAQ 

simulations, a similar approach could be satisfactorily applied 

and extended to very coarse grid scales, e.g., global and 

climate model simulations. 

• Potential for population exposure assessment application 

• Model evaluation and “Weight of Evidence” regulatory 

applications 


• Modeling meso‐scale to urban‐scale features requires care when selecting sets of 

modeling parameterizations; one size (set) may not fit all! 

• There are currently a variety of modeling approaches for the urban canopy ranging 

from slab (Reynolds averaging) to single and multiple canopy layers. Each 

approach has advantages and limitations, strengths and weaknesses, and its 

suitability may be more dependent on the required application (“Fit for Purpose” 

quoting Martilli). 

• Model results are sensitive to the choice of grid size and parameterization 

schemes; model evaluation approaches will need to be robust and requiring 

specialized datasets. 

• Requirements for urban modeling can differ significantly from that of meso‐scale 

for urban canopies that are deep and complex. 

• The modeling results depicting characteristic fine scale spatial structure is not as 

yet verified. Some important and critical issues remain to be resolved regarding 

simulation in the Terra Incognita regime). 

• Advanced urban modeling makes possible numerical experiments for predicting 

changes in the urban climate, UHI, and air quality of urban environments. 

• Databases of buildings are becoming increasingly available facilitating realization of 

operational feasibility for modeling meteorology and air quality for individual 

cities. 

• The modeling framework supporting urban scale modeling is emerging, but the 

science bases and insights are growing rapidly. 


31


Thank You 

Contact information: 

Jason Ching 

University of North Carolina 

Institute for the Environment CB6116 

(137 E. Franklin St, Chapel Hill, NC, USA 27599) 

Email: jksching@gmail.com 


32

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