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A crucial aspect of regional air quality modelling is the accurate characterisation of local meteorology, as this plays a significant role in the transport and chemical transformation of pollution across a region. This study explores the potential of The Weather Research and Forecasting (WRF) model as an alternative meteorological model to support near-time air quality predictions for the NSW Greater Metropolitan Region (NSW GMR). We develop an optimal WRF configuration for the NSW GMR by downscaling the latest Bureau of Meteorology (BOM) Australian Community Climate and Earth-System Simulator (ACCESS) Numerical Weather Prediction (NWP) data using a three nested domain setup with resolutions of 12km, 4km and 1 km by varying physical parameterisations including two cumulus schemes, two planetary boundary layer schemes, two microphysical schemes, two compatible shortwave and longwave radiation schemes and a standard land surface model. Thus, a set of 16 experiments were executed and evaluated on different days involving high ozone days, bushfire smoke, dust storms and heatwaves. Each simulation was initialised at 12UTC with a forecast horizon of four days, including three hourly updates of boundary conditions. All simulations were evaluated with metrics including Mean Absolute Errors (MAE) and Index of Agreement (IOA) calculated over selected stations operated by NSW Department of Planning, Industry and Environment (NSW DPIE) and BOM for surface and vertical variables (temperature (T), relative humidity (RH), wind speed (WS)) including precipitation (PRECIP) and planetary boundary layer heights (PBLH). Overall, results showed that the best configuration produced acceptable agreements at the surface for T (IOA > 0.8), RH (IOA > 0.7) and WSP (IOA > 0.6). Errors in PBLH were elevated in the mid-boundary layer but was within 100m where vertical profiles of temperature and winds were well simulated. Similarly, experiments with double moment microphysical schemes were better in simulating precipitation. Other performance metrics for the experiments conducted under different case days will also be discussed. [ABSTRACT FROM AUTHOR]

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate. [ABSTRACT FROM AUTHOR]