Toward an Operational Machine-Learning-Based Model for Deriving the Real-Time Gapless Diurnal Cycle of Ozone Pollution in China with CLDAS Data

An operational real-time surface ozone (O3) retrieval (RT-SOR) model was developed that can provide a gapless diurnal cycle of O3 retrievals with a spatial resolution of 6.25 km by integrating Chinese Land Data Assimilation System (CLDAS) data and multisource auxiliary information. The model robustly captures the hourly O3 variability, with a sample-based (station-based) cross-validation R2 of 0.88 (0.85) and RMSE of 14.3 μg/m3 (16.1 μg/m3). An additional hindcast-validation experiment demonstrated that the generalization ability of the model is robust (R2 = 0.75; RMSE = 21.9 μg/m3). Compared with previous studies, the model performs comparably or even better at the daily scale and fills the gaps in terms of missing hourly O3 data within the 24-hour cycle. More importantly, underpinned by the RT availability of CLDAS data, the hourly concentration of O3 can be updated in RT, which is expected to advance our understanding of the diurnal cycle of O3 pollution in China