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A rigorous inter-comparison of ground-level ozone predictions

Schlink, Uwe; Dorling, Stephen; Pelikan, Emil; Nunnari, Giuseppe; Cawley, Gavin; Junninen, Heikki; Greig, Alison; Foxall, Rob; Eben, Krystof; Chatterton, Tim; Vondracek, Jiri; Richter, Matthias; Dostal, Michal; Bertucco, Libero; Kolehmainen, Mikko; Doyle, Martin


Uwe Schlink

Stephen Dorling

Emil Pelikan

Giuseppe Nunnari

Gavin Cawley

Heikki Junninen

Alison Greig

Rob Foxall

Krystof Eben

Jiri Vondracek

Matthias Richter

Michal Dostal

Libero Bertucco

Mikko Kolehmainen

Martin Doyle


Novel statistical approaches to prediction have recently been shown to perform well in several scientific fields but have not, until now, been comprehensively evaluated for predicting air pollution. In this paper we report on a model inter-comparison exercise in which 15 different statistical techniques for ozone forecasting were applied to ten data sets representing different meteorological and emission conditions throughout Europe. We also attempt to compare the performance of the statistical techniques with a deterministic chemical trajectory model. Likewise, our exercise includes comparisons of sites, performance indices, forecasting horizons, etc. The comparative evaluation of forecasting performance (benchmarking) produced 1340 yearly time series of daily predictions and the results are described in terms of predefined performance indices. Through analysing associations between the performance indices, we found that the success index is of outstanding significance. For models that are excellent in predicting threshold exceedances and have a high success index, we also observe high performance in the overall goodness of fit. The 8-h average ozone concentration forecast accuracy was found to be superior to the 1-h mean ozone concentration forecast, which makes the former very significant for operational forecasting. The best forecasts were achieved for sites located in rural and suburban areas in Central Europe unaffected by extreme emissions (e.g. from industries). Our results demonstrate that a particular technique is often excellent in some respects but poor in others. For most situations, we recommend neural network and generalised additive models as the best compromise, as these can handle nonlinear associations and can be easily adapted to site specific conditions. In contrast, nonlinear modelling of the dynamical development of univariate ozone time-series was not profitable. © 2003 Elsevier Science Ltd. All rights reserved.


Schlink, U., Dorling, S., Pelikan, E., Nunnari, G., Cawley, G., Junninen, H., …Doyle, M. (2003). A rigorous inter-comparison of ground-level ozone predictions. Atmospheric Environment, 37(23), 3237-3253.

Journal Article Type Article
Publication Date Jan 1, 2003
Journal Atmospheric Environment
Print ISSN 1352-2310
Publisher Elsevier
Peer Reviewed Not Peer Reviewed
Volume 37
Issue 23
Pages 3237-3253
Keywords ground-level ozone
Public URL
Publisher URL
Additional Information Additional Information : Equal co-author. Chatterton, researcher on IST RTD project (2000 � 2002) involving 9 institutions in 5 countries developing modelling tools for improved smog management. This paper evaluated ozone predictions by various methods and thereby assists air quality managers in the selection of appropriate predictive tools.