The potential of statistical state space models in urban ozone forecasting |
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Authors: | Dimitrios Vassiliadis Kostas Kourtidis Olga Poulida |
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Institution: | (1) Laboratory for Extraterrestrial Physics, Code 692, NASA Goddard Space Flight Center, 20771 USA-Greenbelt, MD, USA;(2) Laboratory of Atmospheric Physics, Physics Dpt., Aristotle University of Thessaloniki, Campus Box 149, GR-54006 Thessaloniki, Greece;(3) Frederick Research Center, P.O. Box 4729, CY-1303 Nicosia, Cyprus |
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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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Keywords: | Ozone forecasts accurate short-term predictions state space models ozone forecasts urban ozone forecasts |
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