New methods for evaluating meteorological models used in air quality applications

Meteorological models in conjunction with air quality models are being used to simulate the transport and fate of pollutants in the atmosphere. Hence, there is a need for an extensive evaluation of the entire modeling system. In this study, several new techniques to assess the performance of mesoscale meteorological models are introduced with an emphasis on evaluating the variables and processes that have the potential to influence the air quality predictions, since errors in the meteorological fields are passed on to the air quality model. Model performance was diagnosed by examining the inter-correlation of observable variables in the atmosphere on distinct time scales: intraday, diurnal, and synoptic. It was found that the Mesoscale Model version 5 (MM5) model did replicate the observed relationship between intraday wind speed and temperature, intraday surface pressure and temperature, diurnal surface pressure and temperature as well as most of the correlations between variables on the synoptic timescale. However, a negative correlation between temperature and precipitation was evident in the observations on the intraday scale, but such relationship was not evident in the model output. Furthermore, the diurnal response of increasing wind speed with temperature was strong in the observed time series, but it was much weaker in the model. The correlation between diurnal changes in temperature and cloud fraction was consistently negative in the model whereas it was slightly positive in the observations. Wind profilers were used to examine the simulated boundary layer wind structure. Of the twelve sites examined, the average distance error between the 24-h observed and modeled trajectory was approximately 150 km at height of 100m above the surface. Errors in transport of this magnitude (100–200 km) can produce errors in air quality predictions. It is not the intent of this study to establish quantitative links between the performance of the specific meteorological simulation analyzed here and subsequent air quality simulations. Rather, the results presented here draw attention to errors and inconsistencies in the meteorology that are passed on to the air quality model which, in turn, have the potential to cause errors in air quality model predictions. r 2006 Elsevier Ltd. All rights reserved.