`Potential Benefits and Implementation of MM5/RUC2 Data with the CALPUFF Air Modeling System,` presented at the 2004 AWMA annual conference

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Courtesy of Trinity Consultants

In April 2003, U.S. EPA officially revised the federal guidelines for air quality modeling (40 CFR Part 51, Appendix W) to incorporate CALPUFF as the preferred dispersion model to study long-range pollutant transport for regulatory purposes. At the absolute minimum, CALMET (CALPUFF's meteorological preprocessor) requires hourly measurements of surface meteorological data and twice-daily upper air data soundings (along with terrain and land-use characteristics) as input to generate three-dimensional gridded fields of meteorology to be used in CALPUFF. These measurements come from a variety of sources, some of which are traditional/well-known, while others are new and emerging.

During the past decade, a new source of meteorological data suitable for dispersion modeling applications has resulted from the emergence of next-generation weather forecasting models. Meteorological output from prognostic mesoscale forecast systems, such as the Penn State/NCAR Fifth-Generation Mesoscale Model (MM5) and Rapid Update Cycle Model Version 2 (RUC2), provides superior input data for long-range pollutant transport studies. This is due to the increased spatial and temporal resolution of atmospheric data fields. Prognostic mesoscale forecast systems operate by assimilating all available data and by using meteorological models to result in a consistent analysis of the current atmospheric conditions. This initial analysis is used as the starting point for numerical forecasting of conditions at some hourly intervals in the future. It is the analysis of the current conditions that is the desired information to use as input to the air quality models.

Meteorological model analysis data allow CALMET to derive the meteorological input fields for CALPUFF much more accurately than would be possible from traditional National Weather Service data sets. As a result, the analysis data enable CALPUFF to provide greatly improved modeling results. CALPUFF modeling that uses meteorological model analysis data meets the requirements of the revised federal modeling guidelines; therefore, associated modeling results would be acceptable to regulatory agencies.

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