Let's talk about the weather: A cluster-based approach to weather forecast accuracy
Jill Lundell, Brennan Bean, Juergen Symanzik
备注：
16 pages, 8 figures, presented at JSM 2018, submitted to and accepted at Statistical Computing (not published yet). Similar paper published in JSM 2018 conference proceedings
Improved understanding of characteristics related to weather forecast accuracy in the United States may help meteorologists develop more accurate predictions and may help Americans better interpret their daily weather forecasts. This article examines how spatio-temporal characteristics across the United States relate to forecast accuracy. We cluster the United States into six weather regions based on weather and geographic characteristics and analyze the patterns in forecast accuracy within each weather region. We then explore the relationship between climate characteristics and forecast accuracy within these weather regions. We conclude that patterns in forecast errors are closely related to the unique climates that characterize each region.
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（工业与系统工程系，佐治亚理工学院）；AI Institute for Advances in Optimization（优化前沿人工智能研究所）
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR China（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系）；School of Computer and Information Sciences, University of Newcastle, Newcastle, Australia（新castle大学计算机与信息科学学院）；Hangzhou Innovation Institute of Beihang University, Hangzhou, China（北京航空航天大学杭州创新研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
Numerical models outperform AI weather forecasts of record-breaking extremes
Research Institute for Statistics and Information Science, University of Geneva（统计与信息科学研究所，日内瓦大学）；Scientific Computing Center, Karlsruhe Institute of Technology（科学计算中心，卡尔斯鲁厄理工大学）；Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, ETH Zurich（大气与气候科学研究所，环境系统科学系，苏黎世联邦理工学院）；Department of Compound Environmental Risks,Helmholtz Centre for Environmental Research – UFZ（复合环境风险部门，环境研究中心——UFZ）；Department of Hydro Sciences, TUD Dresden University of Technology（水科学系，德累斯顿理工大学）
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
College of Engineering, Mathematics and Physical Sciences, University of Exeter（埃克塞特大学工程、数学与物理科学学院）；Met Office（英国气象局）；School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）
