Meteorological modeling refers to the mathematical simulation of variables such as wind speed and direction, air temperature, humidity, and solar radiation.
Meteorological conditions combine with pollutant emissions to influence air quality. Because of this relationship, air quality models require meteorological data to correctly predict ambient pollutant concentrations. The requisite meteorological inputs can vary by air quality model, but typically involve information regarding: wind vectors, vertical mixing, temperature, and atmospheric moisture. The choice of meteorological model usually depends on the needs of the air quality model. Some meteorological models (e.g., grid models) use basic equations of momentum, thermodynamics, and atmospheric moisture to determine the space-time evolution of specific weather conditions from a given initial state. Other diagnostic models simply process available meteorological measurements and interpolate them, with consideration toward topographical effects.
A major advantage of meteorological models is that they provide a way of consistently characterizing meteorological conditions at times and locations where observations do not exist. When these models are applied in a retrospective mode (i.e., modeling a past event) they are able to combine ambient data with model predictions via four-dimensional data assimilation, thereby yielding temporal and spatially complete data sets that are grounded by actual observations. Additionally, meteorological forecast models, initialized with as much observational data as possible, have been also used in real-time to provide the inputs necessary to forecast next-day air pollution formation.
EPA has several meteorological data sets available online to assist air quality modeling exercises in the U.S.