Forecasting Peak Daily Ozone Levels—I. A Regression with Time Series Errors Model Having a Principal Component Trigger to Fit 1991 Ozone Levels: Fid-Bau Portal

Forecasting Peak Daily Ozone Levels—I. A Regression with Time Series Errors Model Having a Principal Component Trigger to Fit 1991 Ozone Levels

Liu, Pao-Wen Grace / Johnson, Richard

Abstract

This research was motivated by the need to warn the population of Milwaukee, WI, on high-ozone days. A statistical model for the peak daily 1-hr ozone level is proposed. A Regression with Time Series Errors (RTSE) model, which includes a principal component (PC) trigger, is the basis for forecasting the peak daily 1-hr ozone level.

The RTSE model, with a PC trigger, is first employed to estimate daily peak ozone measured at the University of Wisconsin, Milwaukee-North (UWM-N), during the 1991 ozone season. The RTSE model uses peak daily temperature, morning vector average wind direction, and the PC trigger as predictor variables. The PC trigger was designed to summarize atmospheric circumstances when peak ozone was greater than 100 parts per billion (ppb). It is verified that the RTSE model, with a PC trigger, significantly improves the prediction of peak daily ozone, particularly peak ozone greater than 100 ppb. In comparison with the RTSE model without the PC trigger, the RTSE model with a PC trigger raised the R² from 0.680 to 0.809.¹ It is suggested that the RTSE model, with the PC trigger, is an adequate statistical model that has the potential for real-time ozone forecasting.