Hybrid attention based deep learning for forecasting boundary layer ozone using satellite derived profiles

Ground level ozone is considered a major air pollutant. It is formed when nitrogen oxides and volatile organic compounds react under sunlight. It harms human health and damages plants and materials. It also contributes to climate change. It is a photochemically formed compound that is extremely hazardous to the environment and human health. Proper forecasting of the boundary layer ozone has been a challenge due to the nonlinearly related to both meteorological and chemical conditions and the scarcity of fine-scale vertical ozone patterns. This study uses the OMPROFOZ ozone profile product which is a product of the Ozone Monitoring Instrument (OMI) on the Aura satellite to estimate the ozone concentrations in the boundary layer. A set of deep learning models, i.e., RNN, CNN, GRU, LSTM, and hybrid forms i.e., GRU-CNN and LSTM-CNN, is evaluated to benchmark forecasting accuracy. The first, ConvBiGRU-AttentionNet, integrates attention mechanisms within a convolutional gated recurrent structure. The second, EMD-ConvBiGRU-AttentionNet, adds Empirical Mode Decomposition to extract multi-scale temporal features before modeling. The proposed models outperform conventional methods across metrics such as RMSE, MAE, R², and skill scores. EMD-ConvBiGRU-AttentionNet achieves the highest prediction accuracy. Visual analyses, i.e., residual plots, cumulative error distributions, and attention maps, confirm the capacity of the model to capture spatio-temporal patterns in atmospheric data.