The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe
Weichao Han, Tai‐Long He, Zhe Jiang, Rui Zhu, Dylan B. A. Jones, Kazuyuki Miyazaki, Yanan Shen
Abstract
Abstract Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O 3 ) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O 3 observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O 3 over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m 3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m 3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available.