Peter J. Hurley’s research while affiliated with CSIRO Marine and Atmospheric Research and other places

Information about emission sources, such as their release rates, can be inferred from measurements of their atmospheric concentrations using an inverse modelling approach. The Bayesian probabilistic approach, coupled to a transport model for source-receptor relationship, provides a robust framework for source estimation and is adopted. We consider the complex problem of source estimation at regional scale where surface conditions are inhomogeneous and the meteorology governing transport is spatially variable. Such applications necessitate the use of a mesoscale meteorological and transport model to calculate the source-receptor relationship, for which an efficient backward model formulation of CSIRO’s TAPM is developed. The Bayesian posterior probability density function provides the probabilities of all the source parameter hypotheses. Our methodology can determine emissions from multiple sources and types (e.g. point and area). We test it by considering a synthetic case involving seven coal-mining sources of methane surrounding a single monitoring site. The initial results show that the inverse approach is largely able to reproduce the source emission rates, but improvement in the source estimation is expected if the monitoring site is optimally located and/or if there are multiple monitoring sites. Some modelling issues regarding the backward TAPM formulation are also discussed.

A recently developed building-averaged urban surface scheme as coupled to an operational mesoscale model, TAPM, is evaluated for both flow and tracer dispersion using data from the 2002 Basel UrBan Boundary Layer Experiment (BUBBLE) conducted in the city of Basel, Switzerland. This scheme is based on the so-called town energy balance (TEB) approach and simulates turbulent fluxes using a generic canyon geometry to resolve energy balances for walls, roads and roofs. Air conditioning to close the building energy budget, in-canyon vegetation, and the effects of recirculation and venting of air within the canyon on turbulent fluxes are included. Comparison is also made with the original urban surface scheme of TAPM based on a simple slab approach with separate urban and vegetation–soil tiles and a specified anthropogenic heat flux. The results show that the new scheme leads to an overall improvement in the prediction of surface fluxes, and is able to reproduce the observed near-neutral to weakly unstable conditions at night, which is a feature of urban meteorology. In contrast, the slab scheme predicts stable conditions at night. The observed concentration fields from the tracer experiments are better simulated using the new scheme, but because there were no nighttime tracer releases, the capability of the new scheme under full diurnal conditions could not be demonstrated. For the applications considered here, the computational efficiency of the new scheme in TAPM is on par with the slab scheme.

We apply a three-dimensional prognostic model, namely TAPM, consisting of coupled meteorological and air pollution modules to simulate the turbulence and short-range dispersion (about 0.5 km) of a near-surface tracer release measured during the 1974 Idaho Falls experiment in light-wind stable conditions. We find that the model substantially underestimates the concentration data. An examination of the near-surface turbulence levels reveals that the modelled vertical turbulence is similar in magnitude to the data but the horizontal component is seriously underestimated. We investigate the latter through a comparison of the boundary condition for the turbulent kinetic energy (TKE) in the model with turbulence data from the U.K. Met Office’s Cardington site, and propose a semi-empirical modification to the TKE boundary condition that accounts for the observed horizontal meandering of the air not represented in the model. This modification increases the modelled horizontal turbulence for the Idaho Falls case, in accordance with the observations, but leads to a further underestimation of the observed concentrations. We attribute this underestimation to the inadequacy of vertical resolution in the model in resolving the concentration gradients near the ground. It is not feasible to simply increase the vertical model resolution near the surface, and instead we derive an analytical scheme based on a mass-balance approach that corrects the modelled concentrations at the lowest model level for such gradients. A remarkable improvement in the predicted concentrations is obtained following the application of the derived correction factor. This factor can be easily incorporated in a typical model for point sources and is valid for all stabilities. Lastly, it is clear that more research is required on horizontal flow meandering and its incorporation in prognostic models.

Fully integrated mesoscale meterology–photochemical air pollution modelling has been underway in Australia since 1999. The focus has principally been on PC platforms using a graphical user interface, with performance and accuracy as key objectives to compare with statistical air quality objectives. Runs involving predictions for every hour for a year are commonplace. More recently, Australian integrated modelling has expanded in scale, to include synoptic scales, using clever mapping approaches and more complex photochemistries. The findings will be included in routine weather forecasting outputs as CSIRO works closely with Australian Bureau of Meteorology to build ACCESS.

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.

In this paper we develop a customisable downscaling approach for local-scale air quality and meteorological forecasting applications, using The Air Pollution Model (TAPM) with the Conformal Cubic Atmospheric Model (CCAM). The CCAM–TAPM system allows users to define their own local pollution emissions, terrain and land-use data and then downscale the meteorological and air pollution forecast on PC hardware. To evaluate the meteorological component of the CCAM–TAPM system, we have produced 7-day forecasts dynamically downscaled to 3km resolution, centred on Melbourne (Australia). We produce 365, 7-day forecasts with a forecast starting on each day during 2003, initialised with National Centre for Environmental Prediction (NCEP) 1° Global Forecasting System (GFS) analyses (i.e., no boundary conditions are needed). We estimate that CCAM–TAPM can simulate 10-m wind speeds and 2-m temperatures for up to two forecast days with errors within 10% of previous TAPM studies in which air pollution was successfully predicted.

The meteorology at the Cabauw tower site in the Netherlands has been modelled for 2005 using a local scale prognostic meteorological and air pollution model called TAPM. A number of performance measures have been used to assess model accuracy, including comparison statistics such as root-mean-square error (RMSE) and index of agreement (IOA). Results show that the model performs very well for prediction of wind and temperature at the six tower levels that range from 10 to 200 m above the ground, as well as performing well for radiation and surface fluxes. The model simulation shows almost no bias in mean and standard deviations of wind and temperature at each tower height level, with small RMSE (e.g. RMSE of 1.2m s−1 for 10-m wind speed, and 1.6°C for 10-m temperature) and high correlation and IOA (e.g. IOA of 0.92 for 10-m wind speed and 0.98 for 10-m temperature). Results for radiation and surface fluxes also show good performance, although some biases were seen for these variables, and possibilities for future model development were identified. An examination of model sensitivity also explored several aspects of the model configuration and input.

Low or weak wind-speed conditions, roughly defined as the periods when the mean wind speed at 10m above the ground is 2ms−1 or less, are of considerable practical interest. However, they are not readily amenable to treatment within prognostic meteorological models and, consequently, difficult to predict, especially when the ambient stability is strong. In this paper, we apply an E − ε prognostic meteorological model to simulate near-surface meteorology and, focusing on low wind speeds, compare the predictions with measurements from two independent datasets. A sensitivity analysis is performed to investigate the possible reasons for the relatively inferior model performance for low winds when the atmosphere is stably stratified. A comprehensive data analysis is carried out to study low wind stable conditions, concentrating on the validity of various forms of flux–gradient relationships for momentum and heat within the framework of the Monin-Obukhov similarity theory, which models employ for calculating surface fluxes. The observed behaviour of various stability parameters, such as the Richardson number, is investigated. The results point to inadequacies of the current flux–gradient relationships, especially regarding momentum, under strongly stable conditions as being a dominant reason for the poor low wind predictions. The modelling issues identified are not just restricted to the present model, but are general in nature. The use of an alternative stability function for momentum under strongly stable conditions is explored. It results in improved model performance for low winds; however, further research is needed to better understand strongly stable flows in the lower atmosphere and to develop methods that can translate that understanding to operational meteorological modelling.

Perhaps the weakest aspect of epidemiological studies is the exposure component. How accurate is our assessment of the dose received by a person or population on short- or long-term scales? Most epidemiological analyses involving air quality use data from a fixed location monitor(s), with the implicit assumption that pollutant concentration is spatially uniform across the study domain. This is not the situation in reality, nor do individuals remain at the one location, even over short periods.We have developed a methodology which takes account of spatial variation in air quality and reduces uncertainty in ambient exposure estimates. This approach, using elliptical influence functions, involves the blending of observations from a monitoring network with gridded meteorological and pollution fields predicted by the complex air quality model TAPM. Examples from exposure fields developed on a 1.5 km-spaced grid for each day of a 6-year period (1998–2003) for Brisbane will be shown.