Assessment of Atmospheric Ozone from Reanalysis and Ground-based Measurements in the Baikal Region

Abstract

The machine learning model used to predict ozone concentrations at the Listvyanka monitoring station in the Baikal region is described. The model was trained and verified using automatic ground-based gas analyzer ozone measurements. Random forest and boosting machine learning models were used. According to the ERA5 reanalysis, the mean absolute error of ozone values exceeds 16 ppb, and the mean percentage error is 80%. The respective errors in the ozone values calculated using machine learning models are 6.7 ppb and 29%. The results of forecasting are the most sensitive to the season, air temperature, and vegetation. The ozone values for 2017–2022 were simulated and analyzed using the trained model and reanalysis data.