An Ensemble of Data Assimilations (EDA) can provide valuable information on the analysis and short-range forecast uncertainties. The present ECMWF operational ocean analysis and reanalysis system, called ORAS5, produces an ensemble but does not exploit it for the specification of the background-error covariance matrix $\mathbf{B}$, a key component of the data assimilation system. In this article, we describe EDA developments for the ocean, which take advantage of the short-range forecast ensemble for specifying, in two distinct ways, parameters of a covariance model representation of $\mathbf{B}$. First, we generate a climatological ensemble over an extended period to produce seasonally varying climatological estimates of background-error variances and horizontal correlation length-scales. Second, on each assimilation cycle, we diagnose flow-dependent variances from the ensemble and blend them with the climatological estimates to form hybrid variances. We also use the ensemble to diagnose flow-dependent vertical correlation length-scales. We demonstrate for the Argo-rich period that this new, hybrid formulation of $\mathbf{B}$ results in a significant reduction of background errors compared to the parameterized formulation of $\mathbf{B}$ used in ORAS5. The new ocean EDA system will be employed in ORAS6, ECMWF's next generation ocean reanalysis system.
Improving Typhoon Predictions by Integrating Data-Driven Machine Learning Models with Physics Models Based on the Spectral Nudging and Data Assimilation
Long-window 4DVar for reanalysis using a differentiable weather model
University of Washington（华盛顿大学）；UCAR Cooperative Programs for the Advancement of Earth System Science (CPAESS)（UCAR地球系统科学促进合作计划）；NOAA/NWS Office of Modeling and Development（美国国家海洋和大气管理局/国家气象局建模与发展办公室）；Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学研究合作研究所）；NOAA Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）
Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation
Department of Geosciences, University of Oslo（奥斯陆大学地质科学系）；Instituto Pirenaico de Ecología, Spanish Research Council (IPE-CSIC)（西班牙研究委员会皮耶林生态研究所）；WSL Institute for Snow and Avalanche Research SLF（瑞士联邦雪崩与雪研究所）；Mountain Futures GmbH（山未来有限公司）；Potsdam Institute for Climate Impact Research (PIK)（波茨坦气候影响研究所）；Institute of Geophysics, Polish Academy of Sciences（波兰科学院地球物理研究所）
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）
DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space
Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong Shanghai AI Laboratory（香港中文大学上海人工智能实验室）；Shanghai AI Laboratory（上海人工智能实验室）
Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models
Meteorological Institute, University of Hamburg（汉堡大学气象研究所）；International Max Planck Research School on Earth System Modelling, Max Planck Institute for Meteorology（马克斯·普朗克气象研究所国际马克斯·普朗克地球系统建模研究学院）
Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
Department of Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）；Learning the Earth with Artificial Intelligence and Physics (LEAP) Center, Columbia University（人工智能与物理联合地球学习中心（LEAP）, 哥伦比亚大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
