Peter J. Hurley

TAPM: a practical approach to prognostic meteorological and air pollution modelling

Abstract Air pollution predictions for environmental impact assessments are usually generated by Gaussian plume/puff models driven by observationally-based meteorological inputs. An alternative approach is to use prognostic meteorological and air pollution models, which are better founded than the Gaussian approach, but tend to require much expertise and time to set up and large computing resources to run. This paper provides an overview of the science and verification of The Air Pollution Model (TAPM), which was designed to apply the best science in an easy-to-use and fast-to-run model. TAPM is a PC-based, nestable, prognostic meteorological and air pollution model (with photochemistry) driven by a Graphical User Interface, and is a viable tool for year-long simulations. Datasets of the important inputs needed for meteorological simulations (such as terrain and land-use data and synoptic analyses) accompany the model, allowing quick GUI configuration of the model for any region. We present results from the application of TAPM for urban and coastal areas in Australia, for two United States tracer experiments (Kincaid and Indianapolis) used for international model inter-comparison, and for point source dispersion in wind-tunnel building wakes. The meteorological results show that TAPM performs well in coastal, inland, and complex terrain, in sub-tropical to mid-latitude conditions, for both case studies and year-long simulations. The pollution results show that TAPM performs well for a range of important phenomena such as nocturnal inversion break-up fumigation, convective dispersion, shoreline fumigation, building wakes, and general dispersion in complex rural and urban conditions. In particular, the TAPM performance is very good for the prediction of extreme pollution statistics, important for environmental impact assessments, for both non-reactive (tracer) and reactive (nitrogen dioxide, ozone and particulate) pollutants for a variety of sources (e.g. industrial stacks and surface or urban emissions).

Comparison of closure schemes used to specify the velocity PDF in Lagrangian stochastic dispersion models for convective conditions

Lagrangian stochastic dispersion models make use of the probability density function (PDF) of the Eulerian vertical turbulent velocities. For convective conditions, the PDF is often assumed to have a bi-Gaussian form. Using new laboratory measurements of velocity PDFs in the convective boundary layer (CBL), we propose a new closure for constructing this bi-Gaussian PDF and compare results with three other closure schemes in current use. Of the three existing closures, two utilize the second and third moments of the vertical velocity as inputs, while the third one also incorporates the fourth moment. The new closure is defined with the desirable property that it collapses to a simple Gaussian in the limit of zero skewness. The value of an adjustable parameter in this closure scheme is selected using laboratory data for the third and fourth velocity moments. We determine the parameters in the PDF expression obtained using the four closures, and compare them with those derived by fitting velocity PDF data from the convection tank experiments. Significant differences are found between the values of the PDF parameters from the various closures and the water tank data. The performance of the closure schemes is compared by using a Lagrangian stochastic model to compute ground-level crosswind-integrated concentrations from particles released at four source heights. It is shown that the differences between the concentration estimates obtained using various closures increase as the source height increases. Using, as the benchmark, the dispersion results calculated from the Lagrangian stochastic model incorporating the laboratory velocity data without any closure, we recommend our new closure scheme. The results highlight the importance of turbulence observations in the CBL for accurate dispersion modelling.

Evaluation of TAPM, a prognostic meteorological and air pollution model, using urban and rural point-source data

Abstract The Air Pollution Model (TAPM) (version 2.0), a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorology and air pollution concentrations, is evaluated using the 1985 Indianapolis and 1980−1981 Kincaid field data sets on point-source plume dispersion. The data sets represent urban and rural conditions, respectively, in relatively flat terrain. Multi-level nesting is applied, the Lagrangian particle approach is used to describe near-source dispersion, and the model is operated both with and without local wind data assimilation. Comparison with (published) results obtained from some commonly used plume/puff models, which do not calculate meteorology, indicates that the performance of TAPM is comparable to the best of these models. TAPM gives better concentration predictions when the observed winds are assimilated, but the results without data assimilation are almost as good. The latter implies that the model predicts the local meteorology well. The Indianapolis results point to some bias in TAPM to underpredict in the nighttime stable/neutral conditions and to slightly overpredict in the daytime convective/neutral conditions. The latter is also highlighted by the comparison with the convective Kincaid data. The nighttime underprediction is perhaps due to the fact that the local urban effects are not properly accounted for by the generic single-layer canopy scheme used. The comparison also highlights the roles that light winds, wind direction shear and unsteadiness of the flow play in influencing the locations of concentration maxima.

A skewed homogeneous lagrangian particle model for convective conditions

Abstract Numerical solution of the Langevin equation for convective conditions usually requires quite small time steps in order to resolve properly the smaller-scale turbulence near the boundaries. This time step restriction can be eased significantly if it is assumed that the turbulence is uniform over the whole depth of the mixed layer. In this paper, we investigate the effect of this homogeneous turbulence assumption on ground-level concentration (glc) by comparing results with (a) the Willis and Deardorff laboratory experiments on dispersion in fully convective conditions and (b) the predictions of a model employing a more realistic inhomogeneous parameterization for the turbulence. As far as the ability to predict the magnitude and location of the near-source maximum glc, and the ability to maintain a well-mixed profile further downwind are concerned, we conclude that the assumption of homogeneous turbulence is quite adequate. We also compare Gaussian and skewed distributions for homogeneous turbulence and show that realistic results in convective conditions can only be obtained with the skewed distribution.

AN EVALUATION OF SEVERAL TURBULENCE SCHEMES FOR THE PREDICTION OF MEAN AND TURBULENT FIELDS IN COMPLEX TERRAIN

A prognostic three-dimensional mesoscale model has been developed andused in one- and two-dimensional modes to evaluate ten local turbulenceclosure schemes. The schemes ranged from first-order to the two-equationprognostic schemes. Predictions by the models were compared for aone-dimensional convective boundary layer using mixed layer scaling andmeasurements to interpret the results. Two-dimensional simulations were alsoperformed for a sea-breeze flow and for flow over a hill. The results showedthat for all of the models considered, minor differences were produced in themean meteorological fields and in the vertical scalar fluxes, but majordifferences were apparent in the velocity variances and dissipation rate.Predicted tracer concentrations were very sensitive to the turbulence modelformulation for dispersion from a point source in the convective boundarylayer, particularly for the prediction of maximum concentrations. Predictedtracer concentrations from a surface volume source for the two-dimensionalsimulations were similar for all models, although the degree of mixing in themorning growth period produced some differences. Generally, good results forthe mean meteorological fields can be obtained with first-order schemes, evenif they underpredict the magnitude of turbulence in the convective boundarylayer, and reasonable tracer concentrations can also be obtained with thesemodels provided near-source effects are not important. The two-equationprognostic models performed best for the prediction of turbulence in theconvective boundary layer.

Application of a prognostic model TAPM to sea-breeze flows, surface concentrations, and fumigating plumes

Abstract We apply The Air Pollution Model (TAPM) (version 2.0), a three-dimensional prognostic model developed as a tool to predict both meteorological and air pollution fields for environmental impact assessments and related air pollution studies, to data from the 1995 Kwinana Coastal Fumigation Study. The field study was conducted over a 12-day summer period in the coastal region of Kwinana in Western Australia in order to investigate the local sea-breeze meteorology, the footprint of SO 2 concentration due to industrial sources, and the behaviour of fumigating plumes from power plant stacks. The National Centers for Environmental Prediction meteorological analyses are used as input synoptic fields in the model, multi-level nesting is applied, and the model is run both with and without assimilation of local surface wind measurements. The results show that TAPM simulates the onset of sea breezes, and their magnitude and decay with time, reasonably accurately. As regards to the plume footprint, TAPM performs as well as a specialised regulatory model driven by observed meteorology in Kwinana. As expected, TAPM gives better predictions when the wind data are assimilated, but the results without data assimilation are also good. Comparison with the hourly-averaged dispersion moments (i.e., the mean plume height, standard deviations and skewness values), obtained using a limited number of lidar plume scans both before and after fumigation, demonstrates that TAPM is capable of realistically simulating the observed fumigation characteristics (e.g., negative vertical skewness) and the influence of vertical wind direction shear (i.e., positive lateral skewness).

Simulating Australian Urban Climate in a Mesoscale Atmospheric Numerical Model

We develop an urban canopy scheme coupled to a mesoscale atmospheric numerical model and evaluate the simulated climate of an Australian city. The urban canopy scheme is based on the Town Energy Budget approach, but is modified to efficiently represent the predominately suburban component of Australian cities in regional climate simulations. Energy conservation is improved by adding a simple model of air-conditioning to prevent the urban parametrization acting as an energy sink during the Australian summer. In-canyon vegetation for suburban areas is represented by a big-leaf model, but with a largely reduced set of prognostic variables compared to previous approaches. Although we have used a recirculation/venting based parametrization of in-canyon turbulent heat fluxes that employs two canyon wall energy budgets, we avoid using a fixed canyon orientation by averaging the canyon fluxes after integrating over 180° of possible canyon orientations. The urban canopy scheme is evaluated by simulating the climate for Melbourne, Australia after coupling it to The Air Pollution Model. The combined system was found to predict a realistic climatology of air temperatures and winds when compared with observations from Environmental Protection Authority monitoring stations. The model also produced a plausible partitioning of the urban energy budget when compared to urban flux-tower studies. Overall, the urban canyon parametrization appears to have reasonable potential for studying present and predicting changes in future Australian urban climates in regional climate simulations.

Lagrangian particle modelling of buoyant point sources: Plume rise and entrapment under convective conditions

This paper describes several aspects of a Lagrangian particle model capable of simulating dispersion from buoyant point sources. The equations of Briggs (American Meteorological Society, 1975) are used to calculate the plume final rise heights, and a skewed homogeneous turbulence parameterization is used within the convective boundary layer. The homogeneous assumption enables an order of magnitude greater time step to be used than is normally the case, and was shown to have minimal effect on hourly averaged ground level concentrations by Hurley and Physick (Atmospheric Environment 25A, 1313–1325, 1991; 27A, 619–624, 1993). By including statistics from both ambient and source-induced (plume) turbulence in the probability density function (pdf) of the Langevin equation, we are able to apply this equation to particles in the plume as it rises from the stack to the final rise height. The model is used here to simulate various plume rise and entrapment laboratory experiments of Willis and Deardorff (Atmospheric Environment 17, 2435–2447, 1983; 21, 1725–1735, 1987) under convective conditions with a capping stable atmosphere. The simulations show that the model can reproduce the results of the laboratory experiments when a 15% enhancement to the entrainment parameter in the mean plume rise equations is used. Justification for this modification can be related to neglect of the effect of ambient turbulence upon entrainment in the plume rise equations, which in free convective turbulence may be significant.

An approach for estimating exposure to ambient concentrations.

The degree of certainty in epidemiological studies is probably limited more by estimates of exposure than by any other component. We present a methodology for computing daily pollutant concentration fields that reduces exposure uncertainty and bias by taking account of spatial variation in air quality. This approach, using elliptical influence functions, involves the optimum blending of observations from a monitoring network with gridded pollution fields predicted by the complex air quality model TAPM. Such fields allow more information to be incorporated in the exposure fields used in epidemiological studies, rather than having to assume that ambient exposure is the same across a whole city and/or that individuals remain at the one location for the duration of a study.

Grid sensitivity analysis for the calibration of a prognostic meteorological model in complex terrain by a screening experiment

Abstract In this study we present an application of a sensitivity analysis to identify a set of important factors that are allowed to be calibrated in the grid setup of a prognostic meteorological model for laboratory-based simulations. The use of a calibrated grid is of paramount importance for repeated procedures by leaving unnecessary information unprocessed and inevitably reducing run times. Identification and evaluation of sensitivity, importance and uncertainty elements is attempted by the design of a ‘good practice experiment’ for site-specific calibration. The factors of varied grid size and resolution, based on a one-factor-at-a-time approach, are used for the determination of local sensitivity in the area of interest. A total of five simulations was performed for a grid configuration study that aimed to calibrate The Air Pollution Model.