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.

翻译：PM2.5预报对于公共卫生、空气质量管理和政策制定至关重要。传统的基于物理的模型计算量大，且难以快速适应实时条件。深度学习模型在效率上展现出潜力，但由于误差累积，其准确性会随时间推移而下降。为解决这些挑战，我们提出了一种双深度神经网络（D-DNet）预测与数据同化系统，该系统能高效整合实时观测数据，确保可靠的业务化预报。D-DNet在PM2.5和AOD550的全球业务化预报方面表现卓越，在2019年全年保持了稳定的准确性。与哥白尼大气监测服务（CAMS）4D-Var业务化预报系统相比，它在保持相当准确性的同时，展现出显著更高的效率。这种高效性有益于集合预报、不确定性分析和大规模任务。