AIFS-COMPO：一个全球数据驱动的气溶质和反应性气体预报系统
AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System
- European Centre for Medium-Range Weather Forecasts (ECMWF)（欧洲中期天气预报中心（ECMWF））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出AIFS-COMPO，基于ECMWF AI预报系统，采用Transformer架构联合建模气象与大气组分变量，利用CAMS数据训练，实现高效准确的全球大气组分预测。
AI中文摘要：
我们介绍了AIFS-COMPO，一个技能良好的中等范围数据驱动的全球预报系统，用于气溶质和反应性气体。基于ECMWF人工智能预报系统（AIFS），AIFS-COMPO采用基于Transformer的编码器-处理器-解码器架构，联合建模气象和大气组分变量。模型在Copernicus大气监测服务（CAMS）再分析、分析和预报数据上进行训练，以学习天气、排放、输送和大气化学的耦合动态。我们评估AIFS-COMPO与多种大气组分观测数据，并将其性能与操作性的CAMS全球预报系统IFSCOMPO进行比较。结果表明，AIFS-COMPO在几种关键物种上实现了与现有系统相当或更优的预报技能，同时仅需极小的计算资源。此外，该方法的效率使预报范围超越当前的操作范围，展示了基于AI的系统在快速准确全球大气组分预测方面的潜力。
英文摘要：
We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases. Building on the ECMWF Artificial Intelligence Forecast System (AIFS), AIFS-COMPO employs a transformer-based encoder-processor-decoder architecture to jointly model meteorological and atmospheric composition variables. The model is trained on Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, analysis, and forecast data to learn the coupled dynamics of weather, emissions, transport, and atmospheric chemistry. We evaluate AIFS-COMPO against a range of atmospheric composition observations and compare its performance with the operational CAMS global forecasting system IFS-COMPO. The results show that AIFS-COMPO achieves comparable or improved forecast skill for several key species while requiring only a fraction of the computational resources. Furthermore, the efficiency of the approach enables forecasts beyond the current operational horizon, demonstrating the potential of AI-based systems for fast and accurate global atmospheric composition prediction.
