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Schlink, Uwe, Dorling, Stephen R., Pelikan, Emil, Nunnari, Giuseppe, Cawley, Gavin 
ORCID: https://orcid.org/0000-0002-4118-9095, Junninen, Heikki, Greig, Alison J., Foxall, Rob, Eben, Krystof, Chatterton, Tim, Vondracek, Jiri, Richter, Matthias, Dostal, Michal, Bertucco, Libero, Kolehmainen, Mikko and Doyle, Martin
(2003)
A rigorous inter-comparison of ground-level ozone predictions.
Atmospheric Environment, 37 (23).
pp. 3237-3253.
Abstract
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.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Environmental Sciences Faculty of Science > School of Computing Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Computational Biology |
| Depositing User: | Rosie Cullington |
| Date Deposited: | 19 May 2011 09:15 |
| Last Modified: | 22 Apr 2023 02:03 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/30948 |
| DOI: | 10.1016/S1352-2310(03)00330-3 |
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