Comparison of 24 h Surface Ozone Forecast for Poland: CAMS Models vs. Simple Statistical Models with Limited Number of Input Parameters

Abstract

Surface ozone is usually measured in national networks, including the monitoring of gaseous components important for determining air quality and the short-term forecast of surface ozone. Here we consider the option of forecasting surface ozone based on measurements of only surface ozone and several weather parameters. This low-cost configuration can increase the number of locations that provide short-term surface ozone forecast important to local communities. 24 h prediction of the 1-h averaged concentration of surface ozone were presented for rural (Belsk, 20.79° E, 51.84° N) and suburban site (Racibórz, 18.19° E, 50.08° N) in Poland for the period 2018–2021 via simple statistical models dealing with a limited number of predictors. Multiple linear regression (MLR) and artificial neural network (ANN) models were examined separately for each season of the year using temperature, relative humidity, an hour of the day, and 1-day lagged surface ozone values. The performance of ANN (with R2 = 0.81 in Racibórz versus R2 = 0.75 at Belsk) was slightly better than the MLR model (with R2 = 0.78 in Racibórz versus R2 = 0.71 at Belsk). These statistical models were compared with advanced chemical–transport models provided by the Copernicus Atmosphere Monitoring Service. Despite the simplicity of the statistical models, they showed better performance in all seasons, with the exception of winter.