A framework for the verification of air quality forecasting models
using self-organizing feature maps
Abstract -
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.
I. INTRODUCTION
Air quality forecast is the process of making
predictions of concentrations of a number of pollutants
both in space and time in the atmosphere. The production
and the transport of tropospheric ozone, particulate
matter of less than 10 or 2.5 micrometers in diameter and
other chemical pollutants are predicted by sophisticated
computer models developed by the Meteorological
Service of Canada (MSC), such as CHRONOS [7] and
AURAMS. These forecasts, combined with public
awareness programs in the community, allow Canadians
to make more informed choice to protect their health and
reduce emissions at the personal and community level.
The validation of air quality models is a
challenging task because the spatial and temporal
distributions of pollution are highly discontinuous.
Traditionally, statistical methods have been used to
compare observations on precise location with the values
computed at the corresponding positions by the forecast
model. These validations are actually made by the use of
statistical analysis that is generally insensitive to location
and timing error. Widely used score, like root mean
square error and the correlation coefficient are sensitive
to discontinuities, noise and outliers. Hence, these
methods can give an increased error rate if the values are
not similar although the observed meteorological
phenomenon may have been rightfully forecast but with
only a minor spatial translation of the meteorological
event.
Previous studies have shown the importance of
discriminating between the different sources of forecast
error. Brown et al. [2] and Bullock et al. [3] developed an
alternative "object-based" approach in which forecast
and observed precipitation events are modelled as basic
geometrical shape, such as a band-aid, convex hull and
ellipse approximation. Comparison of the attributes of
forecast and observed shapes features (such as the
centroid location, axis orientation, eccentricity, axis
magnitude) were then used to detect different types of
error and to characterize them. While this approach gives
interesting results for the verification of the precipitation
forecasts, more complex matching rule set may be
necessary to fully account for the unique characteristics
of atmospheric pollution.
An alternative approach for the verification of
air quality forecasts is introduced here. CALUMeT is an
experimental framework that computes regions of
interest (ROI) from both forecast field and observation
map and uses a self-organizing feature map (SOFM) to
make the classification of their feature vectors. These
features are extracted from the different sections isolated
by segmentation from the input maps. Geometrical and
physical invariants are used to develop a realistic and
discriminative model.


Download full report
http://ieeexplore.ieeeiel5/10421/33092/01556260.pdf