利用人工智能推进全球气溶胶预报
Advancing global aerosol forecasting with artificial intelligence
AI总结：
针对气溶胶预报因理化过程与大气动力学复杂相互作用导致的不确定性高、计算成本大的问题，开发结合Vision Transformer和U-Net的AI-GAMFS系统，实现高分辨率5天预报且性能优于现有系统，推动全球气溶胶预报精度提升。
AI中文摘要：
气溶胶预报对于空气质量预警、健康风险评估和气候变化减缓至关重要。然而，由于气溶胶理化过程与大气动力学之间的复杂相互作用，其比天气预报更为复杂，导致显著的不确定性和高昂的计算成本。本文开发了一个人工智能驱动的全球气溶胶-气象预报系统（AI-GAMFS），可提供可靠的5天、每3小时一次的气溶胶光学组分和地表浓度预报，分辨率为0.5°×0.625°。AI-GAMFS在骨干网络中结合了Vision Transformer和U-Net，通过全局注意力和时空编码稳健捕捉复杂的气溶胶-气象相互作用。该系统基于42年的高级气溶胶再分析数据训练，并以GEOS Forward Processing（GEOS-FP）分析结果初始化，可在一分钟内生成5天的业务预报。在预报包括气溶胶光学厚度和沙尘组分在内的大多数气溶胶变量时，其性能优于哥白尼大气监测服务（CAMS）全球预报系统、GEOS-FP预报以及多个区域沙尘预报系统。我们的结果标志着利用人工智能改进基于物理的气溶胶预报的重要一步，有助于为沙尘风暴和野火等气溶胶污染事件提供更准确的全球预警。
英文摘要：
Aerosol forecasting is essential for air quality warnings, health risk assessment, and climate change mitigation. However, it is more complex than weather forecasting due to the intricate interactions between aerosol physicochemical processes and atmospheric dynamics, resulting in significant uncertainty and high computational costs. Here, we develop an artificial intelligence-driven global aerosol-meteorology forecasting system (AI-GAMFS), which provides reliable 5-day, 3-hourly forecasts of aerosol optical components and surface concentrations at a 0.5° x 0.625° resolution. AI-GAMFS combines Vision Transformer and U-Net in a backbone network, robustly capturing the complex aerosol-meteorology interactions via global attention and spatiotemporal encoding. Trained on 42 years of advanced aerosol reanalysis data and initialized with GEOS Forward Processing (GEOS-FP) analyses, AI-GAMFS delivers operational 5-day forecasts in one minute. It outperforms the Copernicus Atmosphere Monitoring Service (CAMS) global forecasting system, GEOS-FP forecasts, and several regional dust forecasting systems in forecasting most aerosol variables including aerosol optical depth and dust components. Our results mark a significant step forward in leveraging AI to refine physics-based aerosol forecasting, facilitating more accurate global warnings for aerosol pollution events, such as dust storms and wildfires.
