Applications of the Integrated WRF/Urban Modelling System to Regional Air Quality Fei Chen, Mukul Tewari, Kevin Manning: National Center For Atmospheric Research (NCAR), Boulder, CO Hiroyuki Kusaka: University of Tsukuba, Japan Shiguan Miao: Institute of Urban Meteorology, Beijing, China Alberto Martilli: Centro de Investigaciones Energeticas, Madrid, Spain Jason Ching: USEPA, Research Triangle Park, NC, Susanne Grossman Clarke: Arizona State University, Tempe, AZ XueMei Wang: Sun Yat-Sen University, Guangzhou, China 2009 CMAS Conference, 20 October 2009, Chapel Hill, NC.

Aspects to the urban environmental problems Climate change and human health Sea-level rise Indoor and outdoor air quality Human thermal stress Water resources and management Designing livable cities Atmospheric transport of accidental or intentional releases of toxic material Severe weather, flood

The factors that influence urban environmental risk Population Increase/ City Growth Regional and Global Climate Change and Extreme Weather 3

Urban Physical Effects on Local Climate and Weather The factors that influence urban environmental risk Population Change City Growth Urban Physical Effects on Local Climate and Weather Regional and Global Climate Change and Extreme Weather 4

The physical modeling system – A spectrum of coupled scales Current technology for operational weather and climate prediction Global Scales Continental Scales Regional Scales Local Scales Long Island Urban Scales

Urban Modeling for Weather Research and Forecast (WRF) Model WRF is widely used in both operational and research community. We can now bridge the gap between traditional mesoscale (~ 10 km) and fine-scale urban transport and dispersion modeling (~ 10 m) WRF, new generation NWP model, 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 air quality and transport and diffusion (T&D).

Integrated WRF Urban Modeling Framework Meet both numerical weather prediction (NWP) and air quality (including T&D) modeling requirements

The Noah Land Model Noah LSM primarily for NWP, air pollution, and regional hydrology applications. Noah has been implemented in NCEP, AFWA, and oversea-agency operational models. Two urban canopy models (UCM) Single layer urban-canopy model (SLUCM, based on Kusaka 2001). Released in WRF V2.2 (Dec. 2006). Multi-layer UCM (Building Effect Parameterization, BEP) by Martilli et al. (2002). Released in WRF V3.1 (April 2009). Natural surface Coupled through ‘urban fraction’ Urban canopy models: Man-made surface The core of this modeling system is the coupling of the natural surface through the community Noah land surface model with an urban canon model. The coupled Noah with single-layer UCM developed by Dr. Kusaka has been in the community release of WRF since 2006. Chen et al., 2009, Intl. J. Climatology

Indoor-outdoor exchange model The Building Energy Model (BEM) is inlcued in the Multi-layer BEP For each floor, BEM solves prognostic equations for indoor air temperature and air moisture by considering: generation of heat due to the occupants and equipments. radiation entering from the windows interchange of heat and moisture with the exterior through ventilation heat diffusion through the walls. IU+1 IU IU-1 Vertical levels in the UCP BEM !! We do not attempt to simulate a specific building, rather an average behaviour over the grid cell!! Salamanca and Martilli (2009, Theoreti. Appli. Climatol.)

Requires detailed mappings of buildings National Urban Database and Access Portal Tool (NUDAPT), led by Jason Ching (USEPA) Example of NUDAPT gridded urban canon parameters for Houston, Texas. Plan area density (PAD), frontal area density of the buildings (FAD). Ching et al., 2009, Bull. American Meteorol. Soc. 10

Applications of Coupled WRF-Urban Models Salt Lake City: Diurnal wind direction (URBAN-2000) Oklahoma City: 2-m temperature (JU-2003) Beijing, Taipei, and Tokyo: surface weather, precipitation Houston: Diurnal cycle of wind profile (TexAQS-2000) Hong Kong: 10-day surface wind Liu, Chen, Warner, and Basara: 2006, J Appli. Meteorol. Lo, Lau, Chen, and Fung, 2007: J. Appli. Meteorol. Lo, Lau, Fung, and Chen, 2007: J Geophys. Res. 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., 2009: Atm. Res. .

WRF/urban 4-km regional climate simulation are able to capture urban heat islands Monthly mean surface air temperatureat 2 m in the Tokyo area at 0500 JST in August averaged for 2004-2007 Observations WRF-Slab land model WRF-Noah-SLUCM Kusaka et al., 2009, ICUC-7

Rapid Urban Growth in China YRD: Yangtze River Delta region PRD: Pearl River Delta region

Such urban growth resulted in ozone increase WRF 12-km monthly (March 2001) averaged difference (urban - preurban) of the surface ozone (in ppbv) and relative 10-m wind vectors Daytime Nighttime Wang et al., 2009, Adv. Atmosp. Physics

2000 Houston in 2000 How does future climate change and urban growth modify air pollution in Houston? Urban expansion Impacts meteorology: Temperature Boundary layer depth Emissions Biogenic emissions Anthropogenic emissions 2030 Projected Houston growth in 2030 industrial or commercial high intensity residential low intensity residential

Results: Land-use change (urbanization) has similar effect on future 8-hour Ozone concentrations to climate change, based on 4-km WRF-Chem simulations. Increase of surface ozone by climate change along Increase of surface ozone by urban growth and climate change Increase of surface ozone by urban growth Jiang et al., 2008, JGR.

WRF downscale and upscale coupling strategies 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 Downscale data transfer Mesoscale modeling system: WRF-Noah/UCM forecast model Urban T&D modeling system: EULAG LES/CFD model Coupler: MCEL Library Upscale data transfer: Turbulence and wind fields explicitly resolved by EULAG are feedback to WRF-urban EULAG fields are volumetrically averaged to (coarser) WRF mesh WRF urban framework introduce source terms in the momentum and turbulence equations The coupling impact urban and downstream weather forecast

Coupled WRF/urban-LES/CFD model results CFD-urban use single sounding CFD-urban use WRF 12-hforecast Density of SF6 tracer gas (in parts per thousand) 60 minutes after the third release, CFD-urban simulations are contoured. The dots represent the observed density at sites throughout the downtown area of Salt Lake City, Utah. Tewari et al. (2009). Dispersion footprint for IOP6 0900 CDT release for Oklahoma City downtown area, Oklahoma) calculated with WRF/EULAG.

Summary An international, collaborative effort has developed an integrated, cross-scale WRF/urban modeling capability, and evaluated it against surface and PBL observations obtained from major cities. WRF/urban (WRF-Chem/uban) is a useful tool for addressing various indoor and outdoor air quality problems in cities. Muck work remains to be done: identify model and parameter uncertainties, to incorporate urban canopy parameters (detailed building data, remote-sensing, and extrapolation approach. The AMS 9th Symposium on the Urban Environment will be held in August 2010, Co-chaired by Fei Chen and Julie Lundquist.