CALPUFF Training by the Developers of the CALPUFF Model
Exponent’s Atmospheric Sciences staff has experience serving a wide range of industrial and government clients around the world. Our scientists and engineers have been influential in the evolution of air quality modeling, and have developed or contributed to five of the seven U.S. EPA models recommended in the Guideline on Air Quality Models. Most notable of these is the CALPUFF model, an advanced non-steady-state meteorological and air quality modeling system recommended by the EPA as the preferred model for assessing long-range transport of pollutants and their impacts on Federal Class I areas or for near-field applications involving complex meteorological conditions.
As the developers of the model, our staff offers training on all aspects of the CALPUFF model including the new features available in Version 7. We are currently exploring offering online training to make it easier for everyone particularly international participants to attend training sessions.
The group has more than 15 years of training experience and has conducted more than 80 courses on air quality modeling around the world. These sessions consist of both public courses, client-specific courses such as EPA, and state agencies such as the United Nations (IAEA) and USAID. Many courses are delivered to professional organizations, including: Air and Waste Management Association (A&WMA) (Canada and USA)
- Clean Air Society of Australia and New Zealand (CASANZ)
- National Association of Clean Air (NACA; South Africa)
Additionally, training courses have been given at universities in USA, Chile, Italy, South Africa, Hong Kong, Australia, New Zealand, Thailand, and Canada.
As the developers of and contributors to many of the EPA-recommended models in the past 35 years, our scientists are experts on the models’ capabilities and limitations. This knowledge enables Exponent to provide solutions beyond the typical or traditional modeling approaches.
Exponent’s CALPUFF Training Course
Exponent’s CALPUFF training course is an equal mix of discussion of practical model formulation material and hands-on computer work with case study applications. The first three days of the course provide an introduction to CALPUFF and an overview of the modeling system with case study problems to illustrate the important concepts. The first morning of training introduces the student to dispersion modeling, combined in the afternoon with practical experience to get to know the model’s new graphical user interface (CALApps). The second morning deals with the formulation and options of CALMET and the preprocessors. The afternoon session consists of hands-on work solving additional case study problems. The third day is focused on the theoretical components of CALPUFF and a discussion of the model postprocessors. In the afternoon additional case study problems will be solved.
Advanced topics such as special algorithms in CALMET or CALPUFF, nested grids, advanced ISORROPIA and aqueous phase chemistry, deposition, particle size distributions, source contribution analysis, flares and other specialized topics are addressed in detail at the longer 4-day and 5-day courses. Our advanced courses are often custom-built to suit specific client requests or to focus on new model developments and more complicated applications.
Click Here to See A Sample Course Outline (PDF)
How You Can Get Access to CALPUFF
To download the latest version of the CALPUFF modeling system, including documentation, model codes and executables, test case datasets and the Exponent CALPUFF Graphical User Interface, CALApps, go to: http://www.src.com/calpuff/calpuff1.htm.
CALPUFF Modeling Data
Links to many useful datasets containing terrain, land use, surface/upper air/buoy meteorological measurements and 3-D meteorological modeling data from WRF, MM5 and other models can be found on the CALPUFF Modeling Data page.
CALPUFF User Registration List
Please join the CALPUFF User List to receive notices of technical upgrades, model software upgrades, modeling tips, and training course announcements.
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