Can dispersion model predictions be improved by increasing the temporal and spatial resolution of the meteorological input data?

In the case of a major pollution incident, terrorist attack, or a radioactive event such as the Chernobyl disaster in 1986, dispersion models are used to predict the transport of pollution away from its source, so allowing potentially affected areas to be warned or even evacuated. Thus it is important for both economic and human health reasons that there is continued research in developing and evaluating dispersion models. The UK Met Office has developed NAME III (Numerical Atmospheric-dispersion Modelling Environment) as its thirdgeneration dispersion model. In addition to emergency response applications, NAME III can also be used for air-quality modelling and to source attribution problems (Jones, 2004). Some recent examples for which NAME III has been used include predicting the spread of the smoke plume caused by the Buncefield Oil Depot fire in December 2005 (Webster et al., 2006) and for investigating the mechanisms for the farmto-farm spread of foot and mouth disease (Gloster et al., 2004). So how does a dispersion model work? Particles of a pollutant released into the atmosphere form a ‘plume’ or ‘cloud’ which spreads out and gradually moves away from its source. To calculate the spread of a plume, dispersion models require data to be input to the model (Turner, 1994), which typically include: