O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone in rural and suburban environment

Abstract

With the impact of tropospheric ozone pollution on humankind, there is a compelling need for robust air quality forecasts. Here, we introduce a novel deep learning (DL) forecasting system called O3ResNet that produces a four-day forecast for ground-level ozone. O3ResNet is based on a convolutional neural network with residual blocks. The model has been trained on 22 years of ozone and nitrogen oxides in-situ measurements and ERA5 reanalysis data from 2000 to 2021 at 328 stations in Central Europe located in rural and suburban environment. Our model outperforms the state-of-the-art Copernicus Atmosphere Monitoring Service regional forecast model ensemble for ground-level ozone with respect to the mean square error and mean absolute error of the daily maximum 8-hour running average ozone, thus marking a major milestone for DL-based ozone prediction. O3ResNet has a very small bias without requiring additional post-processing, and it generalizes well so that new stations can be added with no need to re-train the neural network. As the model works on hourly data, it can be easily adapted to output other air quality metrics. We conclude that O3ResNet is sufficiently advanced and robust to become a test application for operational air quality forecasting with DL.