An enhanced ozone forecasting model using air mass trajectory analysis

An enhanced ozone forecasting model using nonlinear regression and an air m ass trajectory parameter has been developed and field tested. The model per formed significantly better in predicting daily maximum 1-h ozone concentra tions during a five-year model calibration period (1993-1997) than did a pr eviously reported regression model. This was particularly true on the 28 "h igh ozone" days ([O-3] > 120 ppb) during the period, for which the mean abs olute error (MAE) improved from 21.7 to 12.1 ppb. On the 77 days meteorolog ically conducive to high ozone, the MAE improved from 12.2 to 9.1 ppb, and for all 580 calibration days the MAE improved from 9.5 to 8.35 ppb. The mod el was field-tested during the 1998 ozone season, and performed about as ex pected. Using actual meteorological data as input for the ozone predictions , the MAE for the season was 11.0 ppb. For the daily ozone forecasts, which used meteorological forecast data as input, the MAE was 13.4 ppb. The high ozone days were all anticipated by the ozone forecasters when the model wa s used for next day forecasts. (C) 1999 Elsevier Science Ltd. All rights re served.