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.