A Unified Model of Forecasting Ozone by Deep Learning

We propose a simple yet effective framework for real-time surface ozone forecasting using deep learning. The framework highlights three key modules: independent channel encoders, frequency information extraction, and fine-tuning, all of which consistently enhance model performance. This unified model is built well to autonomously capture different spatial and temporal patterns of ozone concentrations, with an averaged RMSE of 8 ppb for day 1 forecasting. The performance of day 4 forecasting is slightly lower. We find that chemistry becomes less important than meteorology over time, indicating their different roles in short-term and long-term forecasting. Most high ozone episodes can be simulated, though capturing extremely high ozone values remains a challenge. Observations from China are trained and tested to demonstrate our model.