Non-linear regression and trajectory analysis applied to ozone forecasting in Louisville, Kentucky
A ground-level ozone forecasting model using nonlinear regression and an air mass trajectory parameter has been developed and field tested. The model was developed for the Louisville, Kentucky metropolitan area. The model performed significantly better in predicting daily maximum 1-h ozone concentrations during a five year model calibration period (1993--1997) than did a previously reported regression model. On 28 high ozone days ([O3] {lt} 120 ppb) during the period, the mean absolute error (MAE) improved from 21.7 ppb to 12.1 ppb. On 77 days meteorologically conducive to high ozone, the MAE improved from 12.2 ppb to 9.1 ppb, and for all 580 calibration days the MAE improved from 9.5 ppb to 8.35 ppb. The model was field tested during the 1998 ozone season. 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 correctly predicted by the ozone forecasters when the model was used for next day forecasts.
- Research Organization:
- Univ. of Louisville, KY (US)
- OSTI ID:
- 20002095
- Report Number(s):
- CONF-990608-; TRN: IM200002%%95
- Resource Relation:
- Conference: Air and Waste 92nd Annual Meeting and Exhibition, St. Louis, MO (US), 06/20/1999--06/24/1999; Other Information: 1 CD-ROM. Operating Systems: Windows 3.1, '95, '98 and NT; Macintosh; and UNIX; PBD: 1999; Related Information: In: Air and Waste 92nd annual meeting and exhibition proceedings, [9500] pages.
- Country of Publication:
- United States
- Language:
- English
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