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.

提出了一种双重深度神经网络（D-DNet）预测与数据同化系统，能够高效整合实时观测，确保可靠的运营预测。D-DNet 在 PM2.5 和 AOD550 的全球运营预测方面表现优秀，在 2019 年整年内保持一致的准确性，并比 Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var 运营预测系统具有显著的高效性，同时保持可比较的准确性。这种高效性有利于集合预测、不确定性分析和大规模任务。