Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)
This work addresses air quality forecasting for public health and policy, offering an incremental improvement in efficiency over existing deep learning and physics-based models.
The paper tackled PM2.5 forecasting by proposing a dual deep neural network (D-DNet) system that integrates real-time observations, achieving comparable accuracy to the CAMS 4D-Var system with notably higher efficiency for the year 2019.
PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to real-time conditions. Deep learning models show potential in efficiency but still suffer from accuracy loss over time due to error accumulation. To address these challenges, we propose a dual deep neural network (D-DNet) prediction and data assimilation system that efficiently integrates real-time observations, ensuring reliable operational forecasting. D-DNet excels in global operational forecasting for PM2.5 and AOD550, maintaining consistent accuracy throughout the entire year of 2019. It demonstrates notably higher efficiency than the Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var operational forecasting system while maintaining comparable accuracy. This efficiency benefits ensemble forecasting, uncertainty analysis, and large-scale tasks.