Air Modeling - Meteorological Grid Models
For air quality modeling purposes, meteorological grid models are used in conjunction with chemical interaction models to provide gridded output of chemical species or pollutant data. Meteorological grid models use mathematical formulations that simulate atmospheric processes such as the change of winds and temperature in time. These meteorological parameters are calculated at distinct spatially equidistant points over an area of interest which is called a grid. When these models are applied in a retrospective mode (i.e. modeling a past event) they are able to blend ambient data with model predictions via four-dimensional data assimilation, thereby yielding temporal and spatially complete data sets that are grounded by actual observations.
There are several commonly-used meteorological grid models that can develop inputs for air quality models. These grid models differ in their simulation of atmospheric processes but each produce gridded meteorological parameters. There are also several post-processors which are needed to convert the raw meteorological modeling output to suitable air quality model input. A few of the most commonly used meteorological models and post-processors are briefly described below.
The EPA's Air Quality Modeling Group has completed modeling applications for several years over multiple domains. These data are available from EPA as described below, along with documentation describing the methodology and evaluation of these simulations.
|Gridded Meteorological Models|
The Fifth-Generation Penn State University / National Center for Atmospheric Research mesoscale model (commonly referred to as MM5) is a frequently-used meteorological model for historical episodes. It is a limited-area, nonhydrostatic, terrain-following sigma-coordinate model designed to simulate or predict mesoscale and regional-scale atmospheric circulations. As a community model it is continuously being improved by contributions from multiple users.
Full 1996 Continental U.S. MM5 at 36 km and July 1996 for Western U.S. at 12 km
Full 2001 Continental U.S. MM5 at 36 km
Summer 2001 (July 12 - August 14) Eastern U.S. MM5 at 12 km
Full 2001 Eastern two-thirds of the U.S. MM5 at 12 km
Meteorological Modeling of 1996 for the United States with MM5 (PDF)(61 pp, 1 MB, 09-29-2000) 36 km MM5
Evaluation of July 1996 Western U.S. 12 km MM5 (presently only available as hardcopy)
Comparison of MM5 Model Estimates (PDF)(36 pp, 4 MB, 05-07-2002) for February and July 2001 Using Alternative Input Databases
Annual Application of MM5 to the Continental U.S. for 2001: Modeling Protocol (PDF)(24 pp, 91 K, 05-28-2002)
Annual Application of MM5 to the Continental U.S. for 2001: Final Report and Evaluation (PDF)(179 pp, 15 MB, 03-31-2003)
Episodic Application of MM5 for a summer 2001 Episode in the Eastern United States (PDF)(119 pp, 4 MB, 09-16-2003)
EPA has developed the Meteorology-Chemistry Interface Processor (MCIP) tool to convert MM5 output into CMAQ input. MCIP provides a complete set of meteorological data needed for air quality simulations. Because most meteorological models are not built for air quality modeling purpose, MCIP deals with issues related to data format translation, conversion of units of parameters, diagnostic estimations of parameters not provided, extraction of data for appropriate window domains, and reconstruction of meteorological data on different grid and layer structures.
Over the next few years it is expected that both real-time and historical meteorological modeling will begin to use the Weather Research and Forecast (WRF) modeling system. This state-of-the-art system will serve as an update to MM5. It is designed to be a flexible, state-of-the-art atmospheric simulation system that is portable and efficient on available parallel computing platforms. WRF is suitable for use in a broad range of applications across scales ranging from meters to thousands of kilometers and will also be community-based.
|Real-time Prognostic Models
Traditionally air quality modeling applications have used meteorological data from observations or specific meteorological modeling simulations. A possible alternative to these two approaches would be to access pre-existing (archived) meteorological data from weather forecast models and convert the data into consistent sets of meteorological inputs for various air quality models. Two potential prognostic meteorological models that could be used for this purpose are the North American Mesoscale Model (NAM) model and the RUC model.
**Available data note: