  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.
