Meteorological modeling for air-quality assessments

Meteorological "elds are required inputs for air-quality models, but they can contain signi"cant errors which contribute to uncertainties in simulations of airborne chemical species, aerosols and particulate matter. Atmospheric states can be diagnosed from observations or simulated by dynamical models (with or without four-dimensional data assimilation, FDDA). In general, diagnostic models are straightforward to operate, but obtaining su$cient observations to analyze regional-scale features is costly, may omit key variables and often lack su$cient spatial or temporal density to describe the "elds adequately. Dynamical models, although still imperfect, have improved in recent years and are now widely accepted for many air-quality modeling applications. Examination of the current state of dynamical models used as meteorological pre-processors indicates that useful simulations for real cases are feasible for scales at least as "ne as 1 km. Introduction of faster computers and practical FDDA techniques already allow simulations of regional episodes lasting up to 5}10 d with "ne resolutions (5 km or less). As technology has improved, however, a need has developed for better parameterizations to represent vital physical processes, such as boundary layer #uxes, deep convection and clouds, at these "ner grid scales. Future developments in meteorological modeling for air-quality applications will include advanced model physics and data assimilation, better coupling between meterological and chemical models, and could lead eventually to widespread use of fully integrated meteorological-chemical models for simulating and predicting air quality. ( 2000 Elsevier Science Ltd. All rights reserved.