Examination of model predictions at different horizontal grid resolutions

While fluctuations in meteorological and air quality variables occur on a continuum of spatial scales, the horizontal grid spacing of coupled meteorological and photochemical models sets a lower limit on the spatial scales that they can resolve. However, both computational costs and data requirements increase significantly with increasing grid resolution. Therefore, it is important to examine, for any given application, whether the expected benefit of increased grid resolution justifies the extra costs. In this study, we examine temperature and ozone observations and model predictions for three high ozone episodes that occurred over the northeastern United States during the summer of 1995. In the first set of simulations, the meteorological model RAMS4a was run with three two-way nested grids of 108/36/12 km grid spacing covering the United States and the photochemical model UAM-V was run with two grids of 36/12 km grid spacing covering the eastern United States. In the second set of simulations, RAMS4a was run with four two-way nested grids of 108/36/12/4 km grid spacing and UAM-V was run with three grids of 36/12/4 km grid spacing with the finest resolution covering the northeastern United States. Our analysis focuses on the comparison of model predictions for the finest grid domain of the simulations, namely, the region overlapping the 12 km and 4 km domains. A comparison of 12 km versus 4 km fields shows that the increased grid resolution leads to finer texture in the model predictions; however, comparisons of model predictions with observations do not reveal the expected improvement in the predictions. While high-resolution modeling has scientific merit and potential uses, the currently available monitoring networks, in conjunction with the scarceness of highly resolved spatial input data and the limitations of model formulation, do not allow confirmation of the expected superiority of the high-resolution model predictions.