Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian Pinault, Baudouin Raoult, Mariana C. A. Clare, Christian Lessig, Michael Maier-Gerber, Linus M… 展开作者
Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian Pinault, Baudouin Raoult, Mariana C. A. Clare, Christian Lessig, Michael Maier-Gerber, Linus Magnusson, Zied Ben Bouallègue, Ana Prieto Nemesio, Peter D. Dueben, Andrew Brown, Florian Pappenberger, Florence Rabier
Machine learning-based weather forecasting models have quickly emerged as a promising methodology for accurate medium-range global weather forecasting. Here, we introduce the Artificial Intelligence Forecasting System (AIFS), a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, and is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses. It has a flexible and modular design and supports several levels of parallelism to enable training on high-resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses and direct observational data. We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and tropical cyclone tracks. AIFS is run four times daily alongside ECMWF's physics-based NWP model and forecasts are available to the public under ECMWF's open data policy.
Diffusion posterior sampling enables zero shot kilometre scale wind forecasting over complex terrain
China Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute（中国电力科学研究院）；State Grid Shanxi Electric Power Research Institute（国网山西省电力公司电力科学研究院）
A Data-Driven Regional Model for Skillful Medium-Range Typhoon Prediction
Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration（上海台风研究所，中国气象局台风数值模式重点实验室）；Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University（复旦大学大气与海洋科学系和大气科学研究院）；Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校大气科学系）
Sensitivity of the ECMWF seasonal forecast model to CO2 and anthropogenic aerosol forcings: Experimental design and impact on climate trends
ECMWF（欧洲中期天气预报中心）；Department of Meteorology and Geophysics, University of Vienna（维也纳大学气象与地球物理系）；NCAS, University of Oxford, Atmospheric, Oceanic and Planetary Physics（牛津大学大气、海洋和行星物理国家环境科学研究中心）
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
Advancing Subseasonal Forecasting with Machine Learning
Harvard College（哈佛学院）；University of Toronto（多伦多大学）；Instituto de Matemática Pura e Aplicada（数学与应用数学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Atmospheric and Environmental Research（大气与环境研究）；Microsoft Corporation（微软公司）；Rhiza Research（Rhiza研究）；Microsoft Research New England（微软新英格兰研究院）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
Improving Typhoon Predictions by Integrating Data-Driven Machine Learning Models with Physics Models Based on the Spectral Nudging and Data Assimilation
Global Evaluation of AI and NWP Precipitation Forecasts During Atmospheric River Events
University of Oklahoma（俄克拉荷马大学）；NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（美国国家科学基金会天气、气候与沿海海洋学可信人工智能研究人工智能研究所）；Brightband
Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
Microsoft Corporation（微软公司）；Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies（教育部地球系统科学部门地球系统建模重点实验室，全球变化研究机构）；Tsinghua University（清华大学）
A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
UiT The Arctic University of Norway（挪威北极大学）；The Norwegian Meteorological Institute（挪威气象研究所）；Norwegian Computing Center（挪威计算中心）；University of South Brittany（南布列塔尼大学）；University of Copenhagen（哥本哈根大学）
Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions
Fraunhofer Heinrich-Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））
FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究院）；University of Gothenburg（哥德堡大学）；Scripps Institution of Oceanography, University of California San Diego（加州大学圣迭戈分校斯克里普斯海洋研究所）；School of Data Science, Fudan University（复旦大学数据科学学院）；Institute for Big Data, Fudan University（复旦大学大数据研究院）
