A METHODOLOGY FOR THE CREATION OF METEOROLOGICAL DATASETS FOR LOCAL AIR QUALITY MODELLING AT AIRPORTS

In order to properly estimate local air pollution concentrations at airports, several different dispersion models are routinely applied using a variety of different modeling approaches (Gaussian, Lagrangian or Eulerian). Common to all dispersion models is the requirement for accurate meteorological parameters. The paper outlines the benefits and risks of three separate approaches to obtain meteorological input for atmospheric dispersion models. The preferred approach is based on directly-measured observations and the primary source of readily available observed data at airports is METAR. A typical METAR report contains observations of temperature, dew point, wind, precipitation, cloud cover, cloud heights, visibility, and barometric pressure. However, most dispersion models require information on atmospheric stability. Although stability is not directly reported in METAR data, a widely-available algorithm allows for the estimation of atmospheric stability class using measured values of wind speed and the observed cloud cover. The next preferred option should be used when METAR or other observed data are not readily available or more sophisticated 3D gridded meteorological fields are required by the specific dispersion model. This second approach uses meso-scale numerical weather prediction (NWP) models. These models can produce high quality ‘best guess’ meteorological fields on a wide variety of time and distance scales. NWP models, however, require high-level meteorological expertise in order to run and are computationally intense. This may make the NWP approach impractical for use in routine applications or for large-scale studies which involve many different airports. Finally, this paper outlines a third approach to obtaining meteorological data. This approach uses long-term, globally-archived, gridded meteorological analysis fields, such as REANALYSIS data, which are readily available and cover long-term time scales. Although less accurate than METAR and NWP models, this approach may be of benefit to those users who require ‘good guess’ meteorological data for air pollution studies in those cases where direct observations, such as METAR, are not available and NWP modelling is not a viable solution