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Real‐time forecasting of photosmog episodes: the Naples case study
Author(s) -
Riccio A.,
Barone G.
Publication year - 2001
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.638
Subject(s) - statistics , wind speed , autoregressive integrated moving average , variable (mathematics) , autoregressive model , time series , standard deviation , econometrics , mean squared error , regression analysis , data set , meteorology , mathematics , environmental science , geography , mathematical analysis
In this paper we analysed the ozone time series data collected by the local monitoring network in the Naples urban area (southern Italy) during the spring/summer period of 1996. Our aim was to identify a reliable and effective model that could be used for the real‐time forecasting of photosmog episodes. We studied the applicability of seasonal autoregressive integrated moving average models with some exogenous variables (ARIMAX) to our case study. The choice of exogenous variables—temperature, [NO 2 ]/[NO] ratio and wind speed—was based on physical reasoning. The forecasting performance of all models was evaluated with data not used in model development, by means of an array of statistical indices: the comparison between observed and forecast means and standard deviations; intercept and slope of a least squares regression of forecast variable on observed variable; mean absolute and root mean square errors; and 95% confidence limits of forecast variable. The assessment of all models was also based on their tendency to forecast critical episodes. It was found that the model using information from the temperature data set to predict peak ozone levels gives satisfactory results, about 70% of critical episodes being correctly predicted by the 24 h ahead forecast function. Copyright © 2001 John Wiley & Sons, Ltd.