In this paper we propose a novel approach to realize forecast verification. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive non-occurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a novel definition of confusion matrix and skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce a deep ensemble learning procedure for binary classification, in which the probabilistic outcomes of a neural network are clustered via optimization of these value-weighted skill scores. We finally show the performances of this approach in the case of three applications concerned with pollution, space weather and stock prize forecasting.
Constrained Ensemble Exploration for Unsupervised Skill Discovery
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen Research Institute of Northwestern Polytechnical University（西北工业大学深圳研究院）；Hong Kong University of Science and Technology（香港科技大学）；Tencent（腾讯）；East China University of Science and Technology（华东理工大学）；The Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification
Applied Machine Learning Group（应用机器学习组）；Fraunhofer Heinrich-Hertz Institute（弗劳恩霍夫海因里希-赫兹研究所）；Signal Theory and Communications Dept.（信号理论与通信系）；Universidad Carlos III de Madrid (UC3M)（马德里卡洛斯三世大学）
Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
Machine Learning for Postprocessing Ensemble Streamflow Forecasts
Earth and Environmental Systems Institute, The Pennsylvania State University（宾夕法尼亚州立大学地球与环境系统研究所）；Environmental Sciences Division, Oak Ridge National Laboratory（橡树岭国家实验室环境科学部）；Energy Systems and Climate Analysis, Electric Power Research Institute (EPRI)（电力研究院（EPRI）能源系统与气候分析部）
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）
Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
Centro de Tecnologías de la Imagen Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES) University of Las Palmas de Gran Canaria, Spain（图像技术中心大学计算机科学、企业和社会研究院（IUCES）大学拉斯帕尔马斯德Gran Canaria分校）
Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France
Atos Inno’Lab TS Bezons（阿托斯创新实验室TS贝宗）；EDF R&D Lab（EDF研发实验室）；Laboratoire des Sciences du Climat & de l’Environnement（气候与环境科学实验室）；CEA（法国国家科学研究中心）；CNRS（法国国家科学研究中心）；UVSQ（法国大学巴黎-萨克雷）；Université Paris-Saclay（巴黎-萨克雷大学）；Laboratoire de Mathématiques d’Orsay（奥赛数学实验室）
Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Cornell University, Department of Statistics and Data Science（康奈尔大学统计与数据科学系）；Washington University in St. Louis, Department of Statistics and Data Science（圣路易斯华盛顿大学统计与数据科学系）；Cornell University, Department of Earth and Atmospheric Sciences（康奈尔大学地球与大气科学系）
A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting
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）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Fudan University（复旦大学）；Henan Meteorological Observatory（河南省气象台）；Tianjin Meteorological Information Center（天津市气象信息中心）
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））
Revealing the Potential of Learnable Perturbation Ensemble Forecast Model for Tropical Cyclone Prediction
Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）；Department of Atmospheric and Oceanic Sciences（大气科学系）；Institute of Atmospheric Sciences（大气科学研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
Lawrence Berkeley National Laboratory (LBNL)（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；NVIDIA Corporation（英伟达公司）；National Energy Research Scientific Computing Center (NERSC)（国家能源研究科学计算中心）；Indiana University（印第安纳大学）；University of California, Irvine（加州大学欧文分校）
