Abstract
State space models for tropospheric urban ozone prediction are introduced and compared with linear regression models. The linear and non-linear state space models make accurate short-term predictions of the ozone dynamics. The average prediction error one hour in advance is 7 μg/m3 and increases logarithmically with time until it reaches 26 μg/m3 after 30 days. For a given sequence of solar radiation inputs, predictions converge exponentially with a time scale of 8 hours, so that the model is insensitive to perturbations of more than 150 μg/m3 O3. The slow increase of the prediction error in addition to the uniqueness of the prediction are encouraging for applications of state space models in forecasting ozone levels when coupled with a model that predicts total radiation. Since a radiation prediction model will be more accurate during cloud-free conditions, in addition to the fact that the state space models perform better during the summer months, state space models are suitable for applications in sunny environments.
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References
Broomhead, D.;G.P. King (1986): Extracting qualitative dynamics from experimental data, Physica D, 20, 217–236
Casdagu, M. (1991): Chaos and deterministic versus stochastic nonlinear modelling, J. R. Statist. Soc. B, 54, 203–328
Casdagli, M. (1992): A dynamical systems approach to modelling input-output systems, In:Casdagu M. andS. Eubank (Eds.): Nonlinear Modeling and Forecasting. Addison-Wesley, MA, pp. 265–281
Casdagli, M.;S. Eubank (1992): Nonlinear Modeling and Forecasting. Addison-Wesley, Reading, MA, USA
Comrie, A.C. (1997): Comparing neural networks and regression models for ozone forecasting, J. Air & Waste Manage. Assoc. 47, 653–663
Fraser, A.M. (1989): Reconstructing attractors from scalar time series: A comparison of a singular system and redundancy criteria, Physica D, 34, 391–404
Gibson, J.F.;J.D. Farmer;M. Casdagu;S. Eubank (1992): An analytic approach to state space reconstruction, Physica D, 57, 1
Levinson, N. (1977): The Wiener RMS (root mean square) error criterion in filter design and prediction, Appendix B In:N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series with Engineering Applications. MIT Press, Cambridge, MA
NLO (1990): Lufthygienisches Überwachungssystem Niedersachsen, Jahresbericht 1990. Niedersächsisches Landesamt für Ökologie, Hildesheim, ISSN 0940-1776 edition
NLO (1992): Datenkatalog zur Luftguete in Niedersachsen 1978–1992. Niedersächsisches Landesamt für Ökologie, Hildesheim, ISSN 0945-4187 edition
Preisendorder, R.W.;C.D. Mobley (1988): Principal component analysis in meteorology and oceanography. Elsevier, Amsterdam-New York
Press, W.H.;B.P. Flannery;S.A. Teukolsky;W.V. Vetterling (1992): Numerical recipes: the art of scientific computing. Cambridge University Press, Cambridge, second edition
Spichttnger, N.;M. Winterhalter;P. Fabian (1996): Ozone and Grosswetterlagen — Analysis for the Munich Metropolitan Area, Environ. Sci. Pollut. Res. 3, 145–152
Weigend, A.;N. Gershenfeld (1994): Time Series Prediction. Addison-Wesley, Reading, MA
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Vassiliadis, D., Kourtidis, K. & Poulida, O. The potential of statistical state space models in urban ozone forecasting. Environ. Sci. & Pollut. Res. 5, 7–11 (1998). https://doi.org/10.1007/BF02986367
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DOI: https://doi.org/10.1007/BF02986367