Validation of the Urban Dispersion Model (UDM)

Brook, D.R.; Felton, Nicola; Clem, C.M.; Strickland, D.; Griffiths, I.H.; Kingdon, R. D.; Hall, D.; Hargrave, J.M.

doi:10.1504/ijep.2003.004236

Cited by 23 publications

(16 citation statements)

References 3 publications

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“…The MUST dataset has been used extensively in the formulation and validation of digital models (e.g., Brook et al, 2003;Milliez and Carissimo, 2007;Santiago and Martilli, 2007;Goricsan et al, 2007). While Goricsan et al (2007) compared the windfields generated by WinMISKAM to those measured on site during the MUST campaign, and Milliez and Carissimo (2007) analysed the concentration predictions of the CFD code Mercure Saturne (Archambeau et al, 2004) against MUST observations, no-one has yet compared concentration predictions by WinMISKAM against the MUST data.…”

Section: A C C E P T E D Article In Pressmentioning

confidence: 99%

Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling

Donnelly

Lyons

Flassak

2009

Atmospheric Environment

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Donnelly, R.P. , Lyons, T.J. and Flassak, T. (2009) Evaluation of results of a numerical simulation of dispersion in an idealisedThis is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. M A N U S C R I P T A C C E P T E D ARTICLE IN PRESSEvaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling. AbstractWinMISKAM is evaluated from an emergency response perspective. Comparisons are made between ground level concentrations observed during selected Mock Urban Setting Test (MUST) field trials and predictions generated by the model. The model was driven by 5 minute averaged on-site meteorological data, and used minimum grid spacing of 0.5 m in both the horizontal and vertical. The code was found to perform well, with 46% of all predictions (paired in time and space) and 83% of arc maxima predictions within a factor of two of observed concentrations. The model was found to perform better for neutral cases than stable cases with 27% of stable case predictions and 57% of neutral case predictions within a factor of two when compared in time and space.

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Section: A C C E P T E D Article In Pressmentioning

confidence: 99%

Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling

Donnelly

Lyons

Flassak

2009

Atmospheric Environment

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“…the UK Urban Dispersion Model (UDM) (Brook et al, 2003). Such potential for local-scale urban simulation in the ARGOS system is considered by Thykier-Nielsen and Roed (2003).…”

Section: Copenhagen Emergency Decision-support Systemmentioning

confidence: 99%

Integrated systems for forecasting urban meteorology, air pollution and population exposure

Hänninen²,

et al. 2007

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Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

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“…When a puff intercepts a building, it is instantaneously placed in the lee of the building in the cavity and puffs are then emitted over time from the cavity. Brook et al [10] have evaluated the model against idealized building arrays in the lab and field as well as against outdoor urban field experiment data. Another well known, but non-peer reviewed modeling system, called MIDAS-AT, computes 3D wind fields around building complexes using potential flow theory with dispersion modeled using a traditional three-term boundary-layer random-walk model (http://www.absconsulting.com/midas/).…”

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confidence: 99%

Evaluation of the QUIC-URB fast response urban wind model for a cubical building array and wide building street canyon

et al. 2008

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This paper describes the QUIC-URB fast response urban wind modeling tool and evaluates it against wind tunnel data for a 7 × 11 cubical building array and wide building street canyon. QUIC-URB is based on the Röckle diagnostic wind modeling strategy that rapidly produces spatially resolved wind fields in urban areas and can be used to drive urban dispersion models. Röckle-type models do not solve transport equations for momentum or energy; rather, they rely heavily on empirical parameterizations and mass conservation. In the model-experiment comparisons, we test two empirical building flow parameterizations within the QUIC-URB model: our implementation of the standard Röckle (SR) algorithms and a set of modified Röckle (MR) algorithms. The MR model attempts to build on the strengths of the SR model and introduces additional physically based, but simple parameterizations that significantly improve the results in most regions of the flow for both test cases. The MR model produces vortices in front of buildings, on rooftops and within street canyons that have velocities that compare much more favorably to the experimental results. We expect that these improvements in the wind field will result in improved dispersion calculations in built environments.

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Validation of the Urban Dispersion Model (UDM)

Cited by 23 publications

References 3 publications

Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling

Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling

Integrated systems for forecasting urban meteorology, air pollution and population exposure

Evaluation of the QUIC-URB fast response urban wind model for a cubical building array and wide building street canyon

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