A framework for the verification of air quality forecasting models using self-organizing feature maps

A fundamental problem in the development of an air quality forecast system is the implementation of an evaluation protocol. Traditionally, statistics are computed to compare the model output to the observations. These methods are limited in that they are generally unable to easily identify the nature of an error (such as location and timing errors). In this paper, we describe CALUMeT (Canadian pollution monitoring tool), an experimental framework that attempts to address these limitations. This framework makes use of self-organizing feature maps to compute the classification of feature vectors from the regions of interest. It encompasses both formalism and a software tool that is under active development. More specifically, the framework allows the specification and manipulation of invariants associated with topological elements of an air quality forecast.