title: The potential of statistical state space models in urban ozone forecasting.
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 microg/m(3) and increases logarithmically with time until it reaches 26 microg/m(3) 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 microg/m(3) O(3). 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.
authors.affiliation: Laboratory for Extraterrestrial Physics, Code 692, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
authors.forename: D
authors.initials: D
authors.lastname: Vassiliadis
authors.forename: K
authors.initials: K
authors.lastname: Kourtidis
authors.forename: O
authors.initials: O
authors.lastname: Poulida