Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations

✍ Scribed by S.M. Robeson; D.G. Steyn

• Publisher: Elsevier Science
• Year: 1990
• Tongue: English
• Weight: 835 KB
• Volume: 24
• Category: Article
• ISSN: 0957-1272
• DOI: 10.1016/0957-1272(90)90036-t

✦ Synopsis

Three statistical models that estimate daily maximum ozone (03) concentrations in the lower Fraser Valley of British Columbia (BC) are specified using measured concentrations from two monitoring stations during the time period 1978-1985. The three models are (I) a univariate deterministic/stochastic model, (2) a univariate autoregressive integrated moving average (ARIMA) model, and (3) a bivariate temperature and persistence based regression model.

The three models as well as a persistence forecast are tested by comparison with 03 concentrations observed during 1986; it is concluded that the bivariate model is superior to both univariate models and persistence. The ARIMA model has nearly the same predictive capability as persistence while the mixed deterministic/stochastic model performs the worst. This suggests that the traditional time series technique of decomposing a series into a trend, a cycle and a stochastic component may not be appropriate for 0 3 air quality forecasting.