Mukul Tewari 1, Fei Chen National Center for Atmospheric Research, Boulder, Colorado. William J. Coirier, and Sura Kim

Transcription

1 IMPACT OF COUPLING A MICROSCALE COMPUTATIONAL FLUID DYNAMICS MODEL WITH A MESOSCALE MODEL ON URBAN SCALE CONTAMINANT TRANSPORT AND DISPERSION Mukul Tewari 1, Fei Chen National Center for Atmospheric Research, Boulder, Colorado William J. Coirier, and Sura Kim CFD Research Corporation, Huntsville, Alabama SUBMITTED 5 SEPTMEBER Corresponding author address: Mukul Tewari, 3450, Mitchell Ln, Boulder, CO mukul@ucar.edu 1

2 ABSTRACT We present the results from a study designed to evaluate the impact upon urban area transport and dispersion (T&D) modeling accuracy by coupling a micro-scale CFD (Computational Fluid Dynamics) model with a mesoscale Numerical Weather Prediction (NWP) model. The CFD model taking part in the evaluation was the CFD-Urban model, while the NWP model was the Weather Research and Forecasting (WRF) model. Two different approaches of supplying boundary condition data to the CFD model were evaluated by comparing the resulting tracer gas transport fields to field data; (i) using observation obtained from a single sounding site during the URBAN 2000 field experiment, and (ii) using WRF/WRF_UCM (Urban Canopy Model) output to drive the CFD-Urban in quasi-steady mode. The WRF and the CFD model results were evaluated against data from the Intensive Observation Period (IOP) 10 during URBAN2000. It was found that the CFD T&D prediction was significantly improved when using wind fields produced by downscaling initial and boundary condition data from the WRF/WRF_UCM. One key reason for such success is that the turning of lower boundary layer wind and pressure gradient are well represented in the time-varying threedimensional WRF fields. Keywords: Urban Canopy Model, CFD model, transport and dispersion

3 Introduction The objective of this study is to explore the potential benefit of coupling a microscale transport and dispersion (T&D) model with a mesoscale numerical weather prediction model in improving T&D modeling in complex urban environments. In the past decade, much progress has been made in order to improve the prediction of airflow and its dispersion in urban regions. For instance, Chan and Leach (2007) developed a Computational Fluid Dynamics (CFD) model called Finite Element Model in 3- Dimensions (FEM3MP) to simulate airflow and dispersion of chemical/biological agents released in urban areas, and evaluated the model with observations from the Intensive Operating Period (IOP) 3 and 9 of the JU-2003 field study conducted in Oklahoma City, Oklahoma. Warner et al (2004) evaluated the T&D using HPAC (Hazard Prediction and Assessment Capability) model and used the URBAN 2000 data for simulating a SF6 release scenario in Salt Lake City, Utah. On the other hand, mesoscale models were used to study the T&D and urban processes. Bacon et al (2000) described an OMEGA model (an atmospheric dispersion model), which can adapt its grid both statically and dynamically to different phenomena such as fronts, clouds, hurricanes, and plumes. Zhong and Fast (2002) conducted an intercomparison of MM5, RAMS and Meso-Eta model using VTMX data for the Salt Lake Valley, and found that large forecast errors exist even after using fine spatial resolution and indicated that for accurate local forecasting, improved model parameterization of longwave radiation and turbulent mixing is needed. Chin et al (2005) evaluated the performance of an Urban Canopy Parameterization (UCP) in the COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) model and compared the model results 3

4 obtained with and without using UCP to the IOP10 of URBAN2000 and VTMX data occurring from Oct They found a close agreement between predicted and observed tracer concentration. Holt and Pullen (2007) coupled two different UCMs (W- UCM which is based on Kusaka s UCM (Urban Canopy Model) and BW-UCM which is based on Brown and Williams (1998) multilayer urban parameterization) with the COAMPS model for a study over the New York City region and compared their performances. Lin et al. (2008) and Miao et al. (2008) have used the WRF/UCM to study the urban heat island and its influence on the diurnal evolution of boundary layer structures over the Beijing and Taipei metropolitan regions. Using the fine-scale WRF Urban Canopy Model, Miao and Chen (2008) indicated that the WRF model with 500-m grid spacing is able to simulate the formation of horizontal convective cells over the Beijing areas. Despite the aforementioned progress in T&D modeling, a common problem encountered when using a computational fluid dynamics (CFD) model or any other microscale model for urban areas is how to properly specify initial and boundary conditions. Traditionally, most of the microscale models are initialized using observations from a single sounding point, which does not represent the variability of weather elements within urban areas. It is critical that T&D models should be initialized with more detailed atmospheric conditions than used traditionally. In this study, as one of the first effort of its kind, we run the Weather Research and Forecasting (WRF) model at sub-kilometer resolution, so that the WRF temporal and spatial meteorological fields could be used to supply boundary conditions to the CFD model, and then evaluate the 4

5 accuracy of the resulting simulation of urban area contaminant T&D resulting from this approach. In fact, the coupling of CFD model and mesoscale models offers the advantage of improving the performance of both models. Mesoscale models can provide more accurate, spatially-varying initial conditions to the urban-scale model, while the CFD urban model can provide detailed representation of urban process as a feedback to the mesoscale model. Nevertheless, in this study, the WRF model was used only to supply boundary conditions to the CFD model. We applied WRF with 500-m grid spacing to the complex environment over the Salt Lake City (SLC) region in order to assess the impact of using WRF/WRF_UCM as input to the CFD urban model on T&D simulations. The paper is organized as the following: Section 2 describes the models used in the present study followed by a brief description of the URBAN2000 field experiment in section 3. Section 4 discusses the numerical experiments performed in the study. Section 5 describes the results followed by the conclusions Numerical Modeling System In this study, we used WRF model, Advanced Research WRF (ARW core), simulations to provide the initial and boundary conditions to the CFD model. A brief description of each of these models is as follows: WRF model WRF is a joint development effort among NCAR (National Center for Atmospheric Research), AFWA (Air Force Weather Agency), NCEP (National Center for Environment Prediction), other government agencies, and the university community. 5

6 Further information can be found at WRF is a completely redesigned code, has superior numerics, targeted for the 1-10 km grid-scale, designed for operational weather forecasting, regional climate prediction, air-quality simulation, and idealized dynamical studies. In the present work, we use the research-quality version of the WRF model with nesting capability (WRF V2.0, Skamarock et al. 2005) released in May The following WRF configurations were used for this study: Non-hydrostatic dynamics, twoway interactive nesting, radiative upper-boundary condition, time-dependent lateralboundary conditions, relaxed toward large-scale model forecasts, the Kain-Fritsch cumulus parameterization for grid-increment 10km, Mellor-Yamada-Janjic (MYJ) TKE level 2.5 planetary boundary-layer parameterization, the Dudhia cloud-radiation scheme for shortwave and the Rapid Radiative Transfer Model for longwave, and the Noah landsurface model (Chen and Dudhia 2001, Ek et al. 2003). The references for these schemes are also available from the wrf users page mentioned above Urban Modeling in WRF Two types of urban parameterization schemes were used in the WRF/Noah coupled system. : 1) a simple urban treatment in the Noah LSM (Liu et al., 2006), and 2) a coupled Noah/UCM. In UCM, the effects of urban building geometry on the thermal and dynamic properties in the atmospheric surface layer are taken into consideration at subgrid scales. Some of the features of the UCM are: user-defined canyon orientation, shadowing from buildings and radiation reflection, multi-layer heat-transfer equations for roof, wall and road surfaces, and an exponential wind profile in the canopy layer (Inoue, 6

7 ). The UCM (Kusaka et al 2004) is implemented in WRF/Noah (Chen et al. 2006) and released in Dec CFD model CFD-Urban is a suite of Computational Fluid Dynamics modeling software that is being used to simulate the wind, turbulence and dispersion fields in urban areas (Coirier, et al., 2002, 2005, 2006a). CFD-Urban was developed under a program sponsored by the Defense Threat Reduction Agency. It solves the Reynolds-Averaged Navier-Stokes (RANS) equations using a collocated, Finite-Volume method implemented upon structured, unstructured and adaptively-refined grids using a pressure-based approach based upon the SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm. Turbulence closure is found by solving a variant of the standard k-ε model. Buildings are parameterized either explicitly, by resolving the buildings themselves, and/or implicitly, by modeling the effects of the buildings upon the flow by the introduction of source terms in the momentum and turbulence model equations. CFD- Urban solves the governing mass and momentum conservation laws at scales smaller than the buildings themselves, important urban aerodynamic features are naturally accounted for, including effects such as channeling, enchanced vertical mixing, downwash and street level flow energization. 2.2 Coupling of WRF and CFD models CFD and WRF models were coupled using a quasi-steady approach described later in this section. 7

8 Spatial interpolation of WRF Data WRF uses a staggered grid approach (Arakawa C-Grid), where thermodynamic data are stored at the cell-centers of the mesh, and the velocity fields are stored at the face-centers of the mesh. In order to simplify the interpolation procedures, we average the velocity components to the cell centers. Furthermore, we store the mesh dual (cellcenters) as nodes in the on-disk data representation, which simplifies both visualization and processing of the data, since all data stored at the same location. The field data is spatially interpolated from the hexahedral WRF cells to the individual face centroids in the CFD mesh using a continuous, linear interpolant. The particular data read in from WRF includes the velocity components, pressure base state and perturbation potential temperature, turbulence kinetic energy (TKE) and the momentum diffusion coefficient. The TKE dissipation rate for the k-ε model is found from the definition of the diffusion coefficient: 2 ε = ρc µ k / µ (1) t In this formula, the density (ρ) is computed from a perfect gas (dry air) equation of state, the turbulence kinetic energy (k) is from the MYJ model, and µ t is the momentum diffusion coefficient. The dissipation rates determined from this relation (1) are higher than what is predicted from equilibrium theory, and TKE and dissipation-rates fields from the CFD solution are typically unrealistic. We defer to future studies to improve this behavior Imposing WRF pressure gradient We supply pressure forces onto the boundaries of the CFD model by finding the difference in the imposed WRF pressure from the local pressure that would be present in 8

9 an ideal atmosphere. Furthermore, we operate the CFD model in a constant density mode and do not apply a gravitational source term to the z-momentum equation. This approach was chosen after a considerable effort was made running the CFD solver with the gravitational source term included, since it was found that directly coupling it to the thermodynamic field from the mesoscale model was problemmatic. If the direct, hydrostatic (i.e. with gravitational source term included) coupling is made, both models must have consistent Thermodynamic models (including humidity transport and equations of state), as well as having similar air-to-ground heat transfer models. Small differences in these thermodynamic quantities can produce unrealistic flow behavior in the CFD model. After a series of computations, we found that the best approach was to apply the difference of the local WRF pressure to that of an ideal atmosphere, which the CFD model uses as the pressure difference from the (constant) CFD reference pressure. That is, impose on the face-centers of the CFD mesh: g P = PWRF, G Ph = PWRF. G Pb 1 ( z zb) κ RTb where P b represents the base state pressure and к=γ/( γ-1) where γ=c p /C v 2.3 Quasi-steady coupling κ (2) The quasi-steady mode first computes the steady state, equilibrium, flow fields at 15 minute intervals, using the WRF data as boundary conditions. The unsteady, contaminant transport evolution equation is then solved using the quasi-steady velocity and turbulence field that is found by linearly interpolating the appropriate steady state velocity and turbulence fields in time. We call this collection of steady-state wind fields a wind field 9

10 library, and the blending of these wind fields in time to solve the contaminant transport equation, the unified frozen hydrodynamic solver, as noted in Coirier et al. (2006a) URBAN 2000 field experiment The URBAN 2000 field experiment was sponsored by U.S. Department of Energy s Chemical and Biological National Security Program. This experiment was conducted in Salt Lake City (SLC), Utah, during October 2000 and was highlighted by seven nightlong intensive operations periods (IOPs). The objective of this experiment was to investigate transport and diffusion around a single downtown building, within and through the downtown area and into the greater SLC urban area. Doran et al (2002) described the field campaign including instrumentation, measurment strategies and weather conditions. The data from this field experiment have been used by many researchers to understand the meteorological and fluid dynamical processes governing dispersion in urban areas e.g. Hanna et al (2003a), Zhong and Fast (2003). We use data from IOP 10 to quantify the accuracy of T&D modeling using downscale/input data from WRF. IOP 10 took place from 1200 MST 25 October to 1200 MST 26 October Sulfur hexafluoride (SF 6 ) was released from a point source at a rate of 1 gs -1, three times over the IOP, with the release on for 1 hour and then off for one hour, for a total of six hours. The WRF/WRF_UCM model results are verified with the meteorological observations

11 Numerical Experiments The WRF_UCM and WRF models were configured with five two-way interactive nested grids having grid spacing of 40.5,13.5,4.5,1.5, and 0.5 km, as shown in figure 1(a). The domain-5 (0.5km) has 154 grid points in the east-west direction and 250 grid points in the north-south direction. There were 31 vertical levels with 16 levels within the lowest 2 km in the atmosphere to better resolve the boundary layer. The WRF model was initialized at 00 UTC 26 October 2000 and integrated for 24 hours. The two WRF runs, which were used to initialize the CFD model, are conducted with: 1) WRF with simple urban treatment (labelled WRF ), and 2) Coupled WRF/UCM (labelled WRF_UCM ). The initial and lateral boundary conditions for WRF/WRF_UCM runs were supplied by analyses and forecasts from the NCEP Eta data assimilation system (EDAS). For this study, we use a combination of USGS 24-category 1-km dataset and a gridded high resolution (30-meter) urban land-use data with detailed classifications for the SLC urban zones (low-intensity residential, high-intensity residential, and the industrial/commercial zone) which was aggregated into the WRF nested domain-5 with 0.5 km grid spacing. The zoomed-in image of domain-5 with a rectangle showing the CFD-model domain is shown in figure 1(b). The CFD model mesh (figure 2) is constructed using a quadtree-prismatic/octree, Cartesian mesh generator that is embedded in a solution adaptive flow solver (Coirier et al., 2002, 2006a). The CFD mesh covers a domain of 8.4 by 7.4 by 1 kilometers and contains approximately 325,000 cells with a lateral resolution of approximately 20 meters in the Central Business District (CBD) growing to approximately 200 meters near the domain boundaries. The mesh is clustered (stretched) in the z-direction, normal to the 11

12 ground plane, with a resolution of 1 meter near the ground growing smoothly to approximately 40 meters near the upper boundary. Computational cells that lie completely within buildings are removed, while buildings that occupy partial cells are modeled using the drag model. Digital elevation data is used to map the constant height ground plane mesh to be conformal to the terrain using a displacement model Results and Discussion The purpose of comparing WRF and WRF_UCM results with the meteorological observations is to ensure the quality of the input data that drive the CFD model. While evaluating the WRF model results, no effort has been made towards improving individual model performances of WRF/WRF_UCM. 5.1 Evaluation of WRF/WRF_UCM model results Figure 3 shows the diurnal variation of wind speed and temperature for WRF/WRF_UCM at Green City Center and the UMASS site for the 0.5km domain. The observational site at Green City Center was one of the six meteorological stations which were deployed in the downtown SLC regions from 1-27 October The station, Green (500) was mounted on a light pole in the parking lot between the City Center building and the Heber Wells building. On comparing the 2m-temperature at Green City Center, we found that from 00 UTC-15 UTC, WRF_UCM compares much better with observations than WRF. However, WRF_UCM and WRF are closer to each other after 1500 UTC. At UMASS, WRF_UCM and WRF results are close to each other. During the daytime, the WRF/WRF_UCM 2m temperatures are cooler, whereas at night both are warmer than observations. The higher night time temperatures result in positive night- 12

13 time wind biases at both these places. During the day, the cooler temperature from WRF/WRF_UCM simulations at UMASS led to the under- prediction of wind. The WRF_UCM simulation at Green City Center shows more variability than WRF in day/night time. The wind vectors for WRF and WRF_UCM were compared to observations obtained from the PNNL, Raging waters, and Green City Center sites (not shown here), and we noticed that WRF_UCM simulation is able to capture the evolution of wind better than the WRF model especially at the following downtown sites: MO2 (the LDS Administrative building), MO8 (the Warehouse), and MO9 (the Wonder Bread Bakery). 5.2 Evaluation of CFD-urban model results The T&D modelling accuracy is assessed by comparing predicted to measured concentration values at sensor locations using standard statistical measures. The study here focuses upon using two measures: FAC2 and MG. ( o p) MG = exp ln C ln C 291 p FAC2 = fraction of data that satisfy C C o In the equations above, the averaging operator is taken over samplers located in four groupings (arcs): Near the source, R2, R3 and R4, corresponding to the CBD, 2km, 4km and 6km arcs in the field test (figure 1, Warner et al 2004). To assess the model accuracy, we have first performed baseline CFD-urban calculations using sounding data taken from the Raging waters site during the field test, as this mode of operation is commonly used when performing the CFD calculations in the absence of mesoscale model data. Next, we assess the performance of quasi-steady WRF- 13

14 CFD coupling approach. The measured ( observed ) quantities are taken from the URBAN 2000 IOP10 data, and are represented as C o, while the computed ( predicted ) values are C P. The averages are taken over each arc. For our statistical comparisons, we use 4 arcs, as shown in Hanna et al (2003). As noted by many earlier investigators, an acceptable model will have: 0.7<MG<1.3 FAC2>0.5 Since a low or null reading at the sampler is useful data, we must incorporate a floor or minimum value into our processing of the measured and predicted statistics. To do this, we use the following strategy: for those samplers that indicate a concentration of less than 10 ppt, and if the predicted concentration value at this same sampler is less than 10 ppt, we reset the predicted value to 5 ppt, in order to introduce a floor or minimum value within a factor of 2 of the sensitivity of the sampler. Since the predicted values can produce concentration levels several orders of magnitude lower than the sensitivity of the sampler, we feel that this approach does not corrupt the statistics with (presumably) accurate, yet highly under-predicted concentration values where the measured values are questionable. In addition to the statistical measures, we compare measured to predicted maximum concentrations in scatter plots. We compare statistical and scatter plots for the CFD-Urban model initialized with a (i) single sounding ( Raging Waters input ) data as initial conditions at all boundary faces and (ii) the CFD-Urban model run in quasi-steady mode with WRF initial and boundary conditions ( Wind Library). Here, in (ii) WRF_UCM results are not shown because they are not significantly different from the WRF results. In the first experiment, 14

15 the Raging Waters meteorological data set was used to supply initial and boundary conditions of velocity and turbulence to the CFD-Urban model. There are significant deficiencies when using this sounding data, namely: there is only one measurement location, which is inadequate for the domain size considered (8.4 by 7.4 by 1 kilometers) here. There is no appropriate pressure data to apply at the CFD-Urban boundaries, which can miss the important large-scale pressure gradients, and there is inconsistent turbulence data, which is either missing or in non-equilibrium with the wind speed profiles. To overcome the lack of consistent turbulence model data we use a Monin-Obukhov Similarity (MOS) profile with : u * = 0.35 m/s, z o = 0.55 m, and a MOS length scale of 80m, which is found from the supplied wind profile data. For the wind direction, we directly use the measured data. The FAC2 and MG calculations for the above 2 experiments are presented in Table1. It is evident from the table that using WRF three-dimentional fields (even at the 12- hour forecast time) instead of single-point observed initial conditions leads to significant improvement. Except for locations R2 and R3, the WRF-CFD-Urban produced FAC2 are larger than the threshold value of 0.5, while the values FAC2 from CFD-Urban using single sounding are less than 0.5 at all locations. Similar results are found for MG comparisons. When averaging these statistics over all locations, the superior performance of WRF-CFD-Urban is clear. This is due to the fact that the quasi-steady state runs (made at 15-min time intervals of the IOP10) give consistently good flow fields that have trends and behavior matching the WRF fields. The turning of lower boundary layer wind to NNW from N is well represented in WRF and the imposed WRF pressure gradient is felt by the CFD-Urban calculations. These improved steady-state flow fields result in 15

16 significantly improved plume transport behavior and statistics. The improved statistics can be readily seen in figure 4, where we compare predicted to measured maximum concentration values for all sensor locations for two different modes of operation. The underestimation of concentration from CFD-Urban using single sounding at Raging Waters is, to a large degree, mitigated in the prediction of the coupled WRF-CFD-Urban, which is consistent with statistics shown in Table 1. The dispersion of plume transport from the two sets of experiment 60 minutes after the 3 rd release of SF 6 is shown in figure 5. The dots in the figure are colored according to concentration using the same scale as the ground contours, and show the maximum measured gas concentrations throughout the entire experiment times. The measured data showed (not shown here) that the plume is transported to the NNW, although the Raging Waters experiment shows that the contaminant is dispersed to the North (figure 5a). This is caused by the lack of an imposed lateral pressure gradient. In addition to the important vertical and lateral velocity variations imposed by WRF upon the CFD-Urban calculations via the boundary conditions, the lateral pressure gradient imposed by WRF was a significant forcing term that drove the plume transport in the direction similar to the observations (figure 5b) Conclusions A very high-resolution numerical modeling study was conducted over the complex terrain, complex urban areas in Salt Lake City. Using high-resolution mesoscale WRF model simulated meteorological fields as input for initial and boundary conditions required by CFD-Urban leads to significant improvement in replicating observed dispersion during the URBAN2000 IOP10. WRF was run using two urban treatments, a 16

17 simple modification of the Noah LSM, and the more sophisticated WRF-UCM, but there was little difference in the result for this particular application. Our study found that the urban T&D modeling accuracy is quantifiably improved when the CFD-Urban model is coupled to the WRF model in a quasi-steady fashion. This mode of operation downscales velocity, turbulence and thermodynamic fields from the NWP model at set time intervals, where at these time intervals, the CFD-Urban model is run in a steady-state mode, using the downscaled data as initial and boundary conditions. The wind fields produced by this periodic downscaling are used in an Eulerian T&D model embedded in CFD-Urban. Therefore, the key reason for this significant improvement in WRF-CFD-Urban coupling is that the turning of lower boundary layer wind and pressure gradient are well represented in the time-varying three-dimensional WRF fields, but absent in the traditional approach of using single-point sounding to drive CFD-Urban Acknowledgements The authors gratefully acknowledge the support for this work from a Small Business Innovation Research Phase I project, funded through the Defense Threat Reduction Agency/TDOC, Technical Monitor CDR Stephanie Hamilton/USN. Part of this work is also supported by the NCAR FY07 Director Opportunity Fund. The valuable comments of Dr. Margaret A LeMone and Dr. C.M. Kishtawal helped in improving the manuscript

18 391 References Allwine, K, J., J.H. Shinn, G.E. Streit, K. L. Clawson, and M. Brown, Overwiew of URBAN 2000: A multiscale field study of dispersion through ab urban environment. Bull. Amer. Meteor. Soc., 83, Bacon, D. P. and coauthor, 2000; A Dynamically Adapting Weather and Dispersion Model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA). Mon. Wea. Rev., 128, Brown, M. J. and M.Williams, 1998; An urban canopy parameterization for mesoscale meteorological models. Proc. Second Symp. on Urban Environment, Albuquerque, NM, Amer. Meteor. Soc., Chan S., T., and M. J. Leach 2007; A Validation of FEM3MP with Joint Urban 2003 data. J. Appl. Meteor. and Clim., 46, Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129,

19 Chen, F., M. Tewari, Y. Liu, T. T. Warner, H. Kusaka, J. W. Bao, W. J. Coirier, A. Lau, 2006; Development and application of an integrated urban modeling system for the Weather Research and Forecast (WRF) model. ICUC-6, Sweden June Chin, H.N.S., M.J. Leach, G.A. Sugiyama, J.M. Leone Jr., H. Walker, and J.S. Nasstrom, Evaluation of an urban canopy parameterization in a mesoscale model using VTMX and URBAN 2000 data. Mon.Wea. Rev., 133, Coirier, W.J., Furmancyzk, M., Przekwas, A.J., 2002: Development of an Image- and CFD-Based Urban Scale Wind Field and Dispersion Simulator, American Meteorological Society, 4 th Symposium on the Urban Environment, p. J17, J Coirier, W.J., Fricker, D.M., Furmanczyk, M., and Kim, S.: A Computational Fluid Dynamics Approach for Urban Area Transport and Dispersion Modeling, in print, Environmental Fluid Mechanics, Vol. 15(5) Coirier, W.J., Kim, S.K, 2006.a, CFD Modeling for Urban Area Contaminant Transport and Dispersion: Model Description and Data Requirements, American Meteorological Society, 6 th Symposium on the Urban Environment, 30Jan-2Feb, Doran J.C., J.D. Fast, and J. Horel, 2002; The VTMX 2002 campaign. Bull. Amer. Meteor. Soc., 83,

20 Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grummann, V. Koren, G. Gayno, and J. D. Tarplay, 2003: Implementation of the Noah land-use model advances in the NCAP operational mesoscale Eta model. J. Geophys. Res., 108 (D22), No NOV Hanna S.R., R. Britter, and P. Franzese, 2003a: A baseline urban dispersion model evaluated with Salt Lake City and Los Angeles tracer data. Atmos Environ., 37, Holt T., and J. Pullen, 2007: Urban canopy modeling of the New York City Metropolitan Area: A comparison and validation of single- and multilayer parameterization. Mon. Wea. Rev., 135, Inoue, E., 1963; On the turbulent structure of airflow within the crop canopies. J. Meteor. Soc. Japan, 41, Kusaka H., and F. Kimura, 2004; Thermal Effects of Urban Canyon Structure on the Nocturnal Heat Island: Numerical Experiment Using a Mesoscale Model Coupled with an Urban Canopy Model. J. Appl. Meteor., 43, Lin, C-Y, F. Chen, J.C. Huang, W-C. Chen, Y.-A. Liou, W.-N. Chen and Shaw-C. Liu, 2008: Urban Heat Island effect and its impact on boundary layer development and land- sea circulation over northern Taiwan, Atmospheric Environment. 42,

21 Liu, Y., F. Chen, T. Warner, and J. Basara, 2006: Verification of a Mesoscale Data- Assimilation and Forecasting System for the Oklahoma City Area During the Joint Urban 2003 Field Project. J. Appli. Meteorol. and Climat., 45, Miao, S., and F. Chen, 2008: Formation of horizontal convective rolls in urban areas. Atm. Res., 89, Miao, S.G., F. Chen, M.A. LeMone, M. Tewari, Q. Li, Y. Wang, 2008; An observational and modeling study of characteristics of urban heat island and boundary layer structures in Beijing. J. Appl. Meteorol. Clim., in press Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp. [Available from NCAR, P. O. Box 3000, Boulder, CO ] Warner, S., N. Platt and J. F. Heagy, 2004; Comparisons of Transport and Dispersion model predictions of the URBAN 2000 field experiment. J. Appl. Meteor., 43, Zhong, S. and J. Fast, 2003; Mon. Wea. Rev.,131,

22 List of Figures and Tables Table 1: Statistics showing the comparison of 2 different scenarios of CFD model initialization. Figure 1: (a) WRF Domains and (b) Landuse Map for partial-domain5 (includes CFD domain shown as a rectangle). Figure 2: Surface mesh of CFD model. Figure 3: 2m Temperature (ºC) and Wind Speed at 10m (m/s) at Green City Center and UMASS. Figure 4: Predicted vs. Measure Concentration Values for (a) Raging Waters, (b) Quasi- Steady Model Figure 5: SF 6 gas dispersion (in PPT) 60 minutes after the third release for CFD-urban model using a) single sounding from the Raging Waters, (b) quasi-steady Model

23 Table 1: Statistics showing the comparison of 2 different scenarios of CFD- Urban model initialization Statistic Experiment Location Near R2 R3 R4 All Source FAC2 RW Single Sounding Initialization FAC2: WRF-CFD Coupling (Quasi-steady mode) MG: RW Single Sounding Initialization MG: WRF-CFD Coupling (Quasi-steady mode)

24 Fig 1: (a) WRF modeling domains and (b) land use map for partial-domain5. The black rectangle box in (b) represents the CFD-Urban modeling domain Fig 2: Surface mesh of CFD model 24

25 Fig 3: 2m Temperature (ºC) and Wind Speed at 10m (m/s) at Green City Center and UMASS. Solid line: Observed, Dash line: WRF_UCM, Dot-Dot-Dash: WRF

26 Fig 4: Scatter plot of predicted and measured concentration values. The predicted values are from CFD-Urban model of using a) single sounding from the Raging Waters, (b) coupled to WRF in quasi-steady Mode Fig 5: SF 6 gas dispersion (in PPT) 60 minutes after the third release for CFD-urban model using a) single sounding from the Raging Waters, (b) quasi-steady Mode