Simple Statistical Methods for Comparative Evaluation of Air Quality Models

  • Steven R. Hanna
  • David W. Heinold
Part of the NATO · Challenges of Modern Society book series (NATS, volume 10)

Abstract

It is often necessary to decide which air quality model is “best” by comparison with observations at a specific site. Usually this decision is connected to a regulatory procedure, but it may also be part of a research and development program. The purpose of this project is to develop and test a simplified statistical procedure for determining whether one model is significantly better than another. In 1980, the American Meteorological Society and the Environmental Protection Agency sponsored a workshop in which a comprehensive set of performance measures for air quality models was outlined (Fox 1981, EPA 1981). These measures included several methods of determining model bias, error, and correlation. However, application of the full set of performance measures results in many tens of pages of tables of statistical measures (Murray et al. 1982). The full set of AMS/EPA performance measures is redundant, and it is difficult for anyone to assimilate all the figures in order to decide which model is best.

Keywords

Mean Square Error American Petroleum Institute Mean Square Error Rela Mineral Management Service Model Evaluation Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer Science+Business Media New York 1986

Authors and Affiliations

  • Steven R. Hanna
    • 1
  • David W. Heinold
    • 1
  1. 1.Environmental Research & Technology Inc.ConcordUSA

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