Modeling Urban Environmental Risks Under Future Climate Change Part I: WRF...

ASI1 ‐ Dr Fei Chen ‐ Part 1

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Modeling Urban Environmental Risks Under Future Climate Change Part I: WRF-Urban model capabilities

• Fei Chen • Senior Scientist• Research Applications Laboratory• National Center for Atmospheric

Research• Boulder, Colorado, USA Lecturer’s Photo

Outline• Part-I: WRF-Urban model capabilities

• Introduction to urban environmental risks associated with climate change

• WRF-Urban modeling capabilities • Part-II: WRF-Urban applications


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Increasingly urbanized global population• 2007: >50% population; 2050: ~67% (UN, 2012)• 1975: only 5 mega cities (>10 million); 2015: 23 mega cities.

• Climate change and sea‐level rise

• Indoor and outdoor air quality• Human thermal stress• Water and energy sustainability • Atmospheric transport of accidental or intentional releases of toxic material

• Extreme weather events, flood

Examples of urban environmental risks


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IPCC AR5 (2014): Future risks and impacts caused by a changing climate

Adaptation to reduce risk

Urban effect on weather and climate: unintentional modification of the atmosphere by humans

• Urban heat islandsThermal Stress

• Greenhouse gas (CO2) emission• Air pollution on radiation

Climate Change• Boundary layer structures. Local

and regional atmosphere circulations

• Precipitation Floods

Topics discussed by Jamie Voogt and Janet Barlow


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Factors influencing urban environmental risk

Population Change

City Growth

Urban Physical Effects

on Local Climate and Weather

Regional and Global Climate Change

and Extreme Weather

But the risk also depends on

Population Change

City Growth

Urban Physical Effects

on Local Climate and Weather

Global

Climate Change

Societal Factors


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Modeling requirements for urban environmental-risk prediction and mitigation

• Global and regional atmospheric modeling (established)

• Building-scale, outdoor and indoor atmospheric modeling (new)

• “Coupled” human-response modeling (new)

Message ‐ Risk prediction and mitigation must include physical modeling AND human‐response modeling

Modeling the human response to urban environmental risks

• For example, vulnerability to extreme urban heat differs spatially by ecology, socio-economic status and cultural context.

• Needs – Understanding local adaptive capacity (e.g., social networks, social capital, community resiliency, utilization of available resources) is critical toward reducing risk.


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The physical modeling system:A spectrum of coupled scales

Global Scales

Continental Scales

Regional Scales

Local ScalesLong Island

Urban Scales

Current technology for operational weather and climate prediction

Challenge in representing multi-scale urban microclimate

Mesoscale modelsLong Island

Urban Scale models (CFD, LES)

Building energy models Indoor-outdoor exchange


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Weather Research and Forecasting (WRF) ModelWidely used “community model” for both research and operational forecasting• Academic scientists • Forecast teams at operational

weather centers• Applications communities (e.g.

Regional Climate, Air Quality, Agriculture, Hydrology, Utilities)

North Atlantic and North American Regional Climate Changes

Registered Users 1/1/12

American universities,Govt. labs, Private sector 5951

Foreign users 12432--------18383

Countries represented: 137

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Urban Modeling for Weather Research and Forecast (WRF) Model

• We can bridge the gap between traditional mesoscale (~ 10 km) and fine-scale urban transport and dispersion modeling (~ 10 m)

• WRF running with 1-4 km grid spacing• Availability of new data at urban scales, urban canopy

models• Land data assimilation techniques • Techniques to couple mesoscale and CFD (LES) models.

• Hence, the WRF model is able to deal with regional climate, fine-scale weather forecast, and urban scales.


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International collaborative effort：Developing WRF-Urban modeling capability

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More than 100 groups in 25 countries have used the WRF‐Urban modeling system.

Integrated WRF-Urban Cross-scale Modeling Framework

Improved Input

Fine‐scale urban land‐use and building characteristics

Urbanized high‐resolution land data assimilation system

(u‐HRLDAS)

Fine‐scale atmospheric analysis

(FDDA)

WRF Modeling System

Urban modeling componentsAdvanced Applications

Noah land surface model

Urban canopy models

Indoor‐outdoor exchange

Urban T&D models (CFD, LES)

Chemistry (WRF‐Chem)

Hydrology (WRF‐Hydro)

Human‐response modeling

Urban extreme weather and climate

Public health and risk assessment

City flood and water management

Adaptation and mitigation

Emergency response

Chen et al., 2010, Intl. J. Climatology


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What urban effects do we model in WRF?

HeatTurbulenceMomentum

Drag Wake diffusion

Radiation

Surface energy balance: QN+ QF= QH + QE + DQS+ DQA

boundary layer and radiation parameterizations

Fluxes of momentum, latent heat, and sensible heat; radiometric temperature

The Noah Land Model

Coupling the Noah land surface model (LSM) to urban canopy models (UCM)

Natural surface

Urban canopy models: Man‐made surfaceCoupled through ‘urban fraction’

Chen et al., 2010, Intl. J. Climatology


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Urban Modeling in WRF-Noah:Three parameterization schemes

1. Bulk parameterization• Increased roughness length: from 0.5 m to 0.8 m

• turbulence generated by rough elements • Reduced surface albedo: from 0.18 to 0.15

– radiation trapping• Increased volumetric heat capacity and thermal

conductivity: 3.0106 J m-3 K-1 and 3.24 W m-1 K-1• heat storage in buildings

• Reduced evaporation: vegetation fraction was reduced to 0.05; reduced available urban soil water capacity.

• Available since WRF V2.0sf_surface_physics = 2 (Noah), sf_urban_physics = 0

Reference: Liu et al., 2006: Journal of Applied Meteorology and Climatology

2. Single-layer Urban Canopy Model (SLUCM) • 2-D urban geometry • Street canyons • Shadowing from buildings and

reflection of radiation• Multi-layer roof, wall and road

models• Available since WRF V2.2

sf_surface_physics = 2 (Noah) sf_urban_physics = 1

• References:• Kusaka et al., Bound.-Layer Meteor., 2001• Miao et al., J. Appl. Meteor. Climatol., 2009



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3. Multi-layer Urban Canopy Model: Building Effect Parameterization (BEP) scheme• Direct interactions with WRF PBL schemes at multiple vertical

layers • Calculate effects of buildings on momentum and heat fluxes • Modify WRF TKE scheme and turbulent length scales

• Available since WRF3. 1sf_surface_physics = 2 (Noah) sf_urban_physics = 2

• works with BouLac and MYJ PBL only

Reference: Martilli et al., 2002, Boundary Layer Meteorology.


Salamanca and Martilli (2009, Theoreti. Appli. Climatol.)

Building Energy Model (BEM):Represent indoor-outdoor exchange

• Improve the estimate of the anthropogenic fluxes. • Estimate energy consumption related to meteorology (air conditioning and

heating).

• Available since WRF3. 2sf_surface_physics = 2 (Noah), sf_urban_physics = 3

• works with BEP and BouLac and MYJ PBL only

Air conditioning (cooling)

Air conditioning (heating)

Heat conductionthrough walls

ventilation

Solar radiationthroughwindows

Indoor heatsources(occupants, equipments)

BEP

BEM


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Challenge: from Real World to UCM

Urban canopy model (UCM) parameter space

Specification of UCM parameters in WRF

Two methods

• Using a look‐up table as function of urban land use types

• Directly ingest 2‐D spatial distributions of UCM parameters


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Parameters High density ResidentialCommercial /

IndustryLow density Residential

SLUM20

building height [ m ] 10 7.5 5Standard Deviation of roof height [ m ] 4 3 1

Anthropogenic heat [ W m{-2} ] 90 50 20

road width [ m ] 10 9.4 8.3Fraction of the urban landscape which does not

have natural vegetation. [ Fraction ] 0.95 0.9 0.5

Thermal conductivity of roof [ J m{-1} s{-1} K{-1} ] 0.67 0.67 0.67

Surface albedo of ground (road) [ fraction ] 0.2 0.2 0.2Lower boundary temperature for building wall

temperature [ K ] 293 293 293

…… …… …… ……

BEP3

BUILDING HEIGHTS:

Roughness length for momentum over roof [ m ] 0.01 0.01 0.01

STREET PARAMETERS …… …… ……

BEM13

Coefficient of performance of the A/C systems 3.5 3.5 3.5Thermal efficiency of heat exchanger 0.75 0.75 0.75

Target Temperature of the A/C systems[ K ] 297 298 298Peak number of occupants per unit floor area

[ person/m^2 ] 0.02 0.01 0.01

Peak heat generated by equipment [ W/m^2 ] 36 20 16…… …… …… ……

Specification of UCM parameters in WRF:Using urban parameter table (URBPARM.tbl)

Depicting global urbanization using remote-sensing dataRed: urban areas in the Pearl River Delta, China

1993 USGS data2001 MODIS data

Data from HK urban planning office

MODIS land‐use and land‐cover has been released in WRF 3.1 since April 2009.


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Urban Model Input: detailed regional urban land-use

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NLCD 30-m Landsat land-cover for Houston

National Urban Database and Access Portal Tool (NUDAPT), led by Jason Ching

Ching et al., 2009, Bull. American Meteorol. Soc.

Example of NUDAPT gridded urban canon parameters for Houston, Texas: Plan area density (PAD), frontal area density of the buildings (FAD).

Specification of UCM parameters in WRF Methode2: Directly ingest 2-D spatial distributions of UCM parameters

Released in WRF v3.5, April 2013.How to use the data? http://www.ral.ucar.edu/research/land/technology/urban.php


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WUDAPT: Facilitating advanced urban canopy modeling for

weather, climate, air quality and environmental analyses

Led by Jason ChingWUDPAT workshop this weekend

Sources of Anthropogenic Heating (Country scale)

Source: www.eia.doe.gov. U.S. Data.

Residential21%

Commercial17%

Transport27%

Metabolism1%

Manufacturing/Industry34%

Courtesy of David Sailor, Portland State University


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Anthropogenic heating (AH) for Beijingderived using approach of Sailor and Lu (2004)

0800 LST Winter0800 LST Summer

AH critical for capturing spatial variance of land surface temperature

MODIS Observations

WRF‐Urban simulations

Miao et al., 2008, J. Appl. Meteor. Climatol.


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WRF-Urban: Coupling mesoscale WRF with building-scale models (EULAG LES)

Mesoscale modeling system:WRF‐Noah/UCM forecast model

Urban T&D modeling system:EULAG LES/CFD model

WRF provides initial and lateral boundary conditions for EULAG in two modes• Isolated sounding data mode – short term, quasi steady conditions, small scale urban domain• Unsteady (temporal‐based coupling) mode – linear interpolation of the WRF data in time and space→ Building geometry flow features resolved explicitly with immersed boundary (IB) approach

Coupler:

MCEL Library

Upscale data transfer:

Downscale data transfer

Wyszogrodzki, Miao, and Chen, 2012, Atmospheric Research

Synchronize models for a practical means with Model Coupling Environmental Library (MCEL)

Downscale filter : ‐ extract data and bound it to specific area‐ change coordinates and grid structure

MCEL ‐ COBRA Client/Server

• Computationally efficient system • Allows components to operate independently • Provide continuous data transfer • Improves transfer timing• Send, receive and store native WRF/EULAG grids In

Netcdf format for further processing

FILTERS: resolve problems of the data incompatibility due to different models numeric (WRF Arakawa C‐grid, EULAG A‐grid) and vertical coordinates


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5 two‐way nested domains, grid spacing and grid sizes:

• D1: 40.5km ( 90* 90*38)• D2: 13.5km (100*100*38)• D3: 4.5km (100*100*38)• D4: 1.5km (100*100*38)• D5: 0.5km (100*100*38)

Multiscale WRF-Urban resolved urban flows

EULAG LES domain

EULAG simulated transport and diffusion of SF6 gas tracer concentrationsDispersion footprint for IOP6 9:00 am

Joint Urban 2003 experiment

IOP6 IOP8Release

typeStart

(CDT)Release Amount

Start (CDT)

Release amount

Plume (30 min) 0900 3.0 g/s 2300 3.1 g/sPlume (30 min) 1100 3.2 g/s 0100 3.0 g/sPlume (30 min) 1300 3.0 g/s 0300 3.0 g/s

Puff 1500 0.498 kg 0500 0.500 kgPuff 1520 0.499 kg 0520 0.500 kgPuff 1540 0.510 kg 0540 0.300 kgpuff 1600 0.500 kg 0600 0.305 kg

Wyszogrodzki, Miao, and Chen, 2012, Atmospheric Research


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How to solve interactions between steady flow and unsteady flow in coupling mesoscale models with microscale models?

WRF/CFD-Urban Quasi-steady coupling

WRF domains 0.5-km domain

CFD-Urban domain8.4x7.4x1 km

The quasi-steady mode first computes the steady state, equilibrium flow fields using 15-min WRF output as boundary conditions. The unsteady, contaminant transport evolution equation is then solved using the quasi-steady velocity and turbulence field by linearly interpolating the steady state velocity and turbulence fields.


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steady-flow coupling Quasi steady-flow coupling

Quasi steady-flow coupling improves urban T&D predictions

Problems in Urban Hydrologic Modeling

SLUCM underestimate city latent heat fluxes, a common problem in urban parameterization schemes (Grimmond et al., 2011)

Annual average of heat fluxes for July 2009‐2010:Dashed: observations from Beijing 320‐m tower (from 140‐m height),

Solid: SLUCM simulated

Red: sensible heat fluxGreen: latent heat flux

model

Obs


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Urban hydrological processes in Single‐layer Urban Canopy Model

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Urban irrigation Oasis effect Anthropogenic

latent heat Evaporation over

engineered surfaces

Improving WRF‐Urban hydrological processes

Miao and Chen, 2014: China Earth Sciences

SLUCM significantly improve city evaporation simulation (right panel) by considering the above urban hydrological processes.

model

Obs


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Improve WRF-Urban Hydrologic ModelingPhoenix summer (JJA) Vancouver spring (MAM)

Green: latent heat fluxesRed: sensible heatBlue: storage heat

Yang et al. (2014, Boundary Layer Meteorology)

Dots: observationSolid: old modelDashed: new model

Cool Roofs

Green Roofs

Assess mitigation and adaptation strategies in urbanized areas.


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• Oasis Effect: Considering enhanced potential evapotranspiration of sparse vegetation in urban area due to the lack of obstacles to both radiation and wind.

• To activate this option: Oasis (in URBPARM.TBL) > 1.0 • Irrigation Option (IRI_SCHEME): represent irrigation practice in urban

areas. When activated, irrigation is scheduled for 9 to 10 pm every day from May to September. During this period, moisture of the top two soil layers of the urban vegetation and green roofs is set to field capacity (i.e., transpiration is not limited by water availability).

• 1: for activation; 0: for deactivation. • Anthropogenic latent heat (ALH): add urban anthropogenic latent heat

to a diurnal profile to the latent heat flux term. • Related variables in URBPARM.TBL): are ALH, ALHOPTION,

ALHSEASON, ALHDIUPRF. • Set ALHOPTION to 1 and 0 for turning on and off the option.

New Options for Single Layer Urban Canopy Model in WRFV3.7 (2015)

• Evaporation over impervious surface (IMP_SCHEME): evaporation over engineered surfaces during and shortly after rainfall events.

• Related variables in URBPARM.TBL are IMP_SCHEME, PORIMP, DENGIMP.

• IMP_SCHEME = 1: use the original parameterization in WRF; =2 : the new scheme.

• Multi‐layer green roof (GROPTION): Enable modeling of multi‐layer green roof system on buildings in urban area.

• Related variables in URBPARM.TBL): GROPTION, FGR, DZGR. • GROPTION = 1 , using green roof modeling

= 0 , No green roof, using default vegetation and soil type of green roof is grassland and loam, respectively.

New Options for Single Layer Urban Canopy Model in WRFV3.7 (2015)


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New mosaic/tiling approach in WRF‐Noah released in WRF V3.6 (2014)

Li et al. 2014: Development and evaluation of a mosaic approach in the WRF‐Noah framework, JGR.

WRF‐Noah WRF‐Noah‐mosaic (N=4)

Urban 100%

dx= 1km

dy= 1km

Drylandcropland25%

Deciduous broadleaf forest22%

Low density urban40%

Others13%

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Latent heat fluxes

A clear‐day case: surface fluxes at Cub hill (Towson, MD) simulated by WRF‐Urban with Mosaic approach

Ground heat fluxes

Obs

Old

New

Li et al. 2014: Development and evaluation of a mosaic approach in the WRF‐Noah framework, JGR.


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Evaluations of the WRF-Urban Model

Houston: Diurnal cycle of wind profile (TexAQS‐2000)

Hong Kong: 10‐day surface wind

Beijing, Taipei, and Tokyo: surface weather, precipitation

Salt Lake City: Diurnal wind direction (URBAN‐2000)

Miao and Chen, 2008: Atm. Res. Lin et al., 2008: Atm. Environ.Jiang et al. 2008: J Geophys. Res.Miao et al., 2009: J. Appli. Meteorol. Climatol.Zhang et al., 2009: J Geophys. Res.Tewari et al., 2010: Atm. Res.

Oklahoma City: 2‐m temperature (JU‐2003)

Wang et al. , 2009, Adv. Atmos. Sci., Loridan et al., 2010, Q. J. Roy. Meteorol. Soc., Miao et al., 2011, J Appl. Meteorol. Climatol.,Chen et al., 2011. J Geophys. Res.Salanmanca et al., 2011, J. Appl. Meteor. Climatol.,Kusaka et al., 2012, . J Met Soc Japan.Wyszogrodzki et al., 2012, Atm. Res.,

Challenge (reflections)

• WRF-Urban provides new modeling capabilities to represent city-atmosphere interactions

• No model is perfect (WRF-Urban is no exception)

• No city is exactly the same• Form, morphology, anthropogenic heating

• Need to improve specification of urban parameters and to evaluate model performance


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WRF-Urban web site at NCAR/RALhttps://www.ral.ucar.edu/solutions/products/urban-canopy-model

Thank you!Fei ChenResearch Applications laboratory National Center for Atmospheric ResearchP.O. Box 3000, Boulder, CO 80307, USATel: 303-497-8454Email: [email protected]

• Website: • http://www.rap.ucar.edu/research/land/• Research ID on Web of

Science: http://www.researcherid.com/rid/B-1747-2009

Modeling Urban Environmental Risks Under Future Climate Change Part I: WRF...

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