Nansen Environmental and Remote Sensing Center（南森环境与遥感中心）；Sorbonne Université（索邦大学）；Universitetet i Bergen（卑尔根大学）；Bjerknes Center for Climate Research（比耶克内斯气候研究中心）
Sébastien Barthélémy, Julien Brajard, Laurent Bertino, François Counillon
Increasing the resolution of a model can improve the performance of a data assimilation system: first because model field are in better agreement with high resolution observations, then the corrections are better sustained and, with ensemble data assimilation, the forecast error covariances are improved. However, resolution increase is associated with a cubical increase of the computational costs. Here we are testing an approach inspired from images super-resolution techniques and called "Super-resolution data assimilation" (SRDA). Starting from a low-resolution forecast, a neural network (NN) emulates a high-resolution field that is then used to assimilate high-resolution observations. We apply the SRDA to a quasi-geostrophic model representing simplified surface ocean dynamics, with a model resolution up to four times lower than the reference high-resolution and we use the Ensemble Kalman Filter data assimilation method. We show that SRDA outperforms the low-resolution data assimilation approach and a SRDA version with cubic spline interpolation instead of NN. The NN's ability to anticipate the systematic differences between low and high resolution model dynamics explains the enhanced performance, for example by correcting the difference of propagation speed of eddies. Increasing the computational cost by 55\% above the LR data assimilation system (using a 25-members ensemble), the SRDA reduces the errors by 40\% making the performance very close to the HR system (16\% larger, compared to 92\% larger for the LR EnKF). The reliability of the ensemble system is not degraded by SRDA.
Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Department of Applied Mathematics, University of California, Santa Cruz, CA 95064, USA（应用数学系，加州大学圣克鲁兹分校）；Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA（电气与计算机工程系，加州大学圣克鲁兹分校）；Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA（海洋、地球与大气科学系，北卡罗来纳州立大学）
U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted Ensemble Data Assimilation
University Cooperation of Atmospheric Research（大学大气研究合作组织）；National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局/地球物理流体动力学实验室）；Princeton University（普林斯顿大学）
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）
For many years, strongly and weakly constrained approaches were the only options to deal with errors in four-dimensional variational data assimilation (4DVar), with the aim of balancing the degrees of freedom and model constraints. Strong model constraints were imposed to reduce the degrees of freedom encountered when optimizing the strongly constrained 4DVar problem, and it was assumed that the models were perfect. The weakly constrained approach sought to distinguish initial errors from model errors, and to correct them separately using weak model constraints. Our proposed i4DVar* method exp…
This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematically rigorous technique that has been recently introduced to constrain the large-scale features of high-resolution atmospheric models with coarse spatial scale data. It is similar to spectral nudging but does not require any spectral decomposition for scales separation. This is expected to be particularly relevant for ISM, which involves various interactions between large-scale circulations and regional physical proc…
Generative data assimilation highlights fronts as key regulators of ocean energy cascade
University of Washington（华盛顿大学）；California Institute of Technology（加州理工学院）；ENS, PSL Université, Ecole Polytechnique, Sorbonne Université, CNRS（法国高等师范学院、巴黎文理研究大学、巴黎综合理工学院、索邦大学、法国国家科学研究中心）
EDF R&D（法国电力集团研发中心）；European Center for Research and Advanced Training in Scientific Computation (CERFACS)（欧洲科学计算高级研究与培训中心）；French National Research Center (CNRS)（法国国家科学研究中心）；Université de Toulouse（图卢兹大学）；Saint-Venant Laboratory for Hydraulics (LHSV)（圣维南水力学实验室）
Data assimilation (DA) is widely used to combine physical knowledge and observations. It is nowadays commonly used in geosciences to perform parametric calibration. In a context of climate change, old calibrations can not necessarily be used for new scenarios. This raises the question of DA computational cost, as costly physics-based numerical models need to be reanalyzed. Reduction and metamodelling represent therefore interesting perspectives, for example proposed in recent contributions as hybridization between ensemble and variational methods, to combine their advantages (efficiency, non-l…
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）；Shanghai Normal University（上海师范大学）；Jiangsu Meteorological Observatory（江苏省气象台）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Nanjing Meteorological Observatory（南京市气象台）
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
IMT Atlantique（大西洋高等电信学院）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；Ifremer（法国海洋开发研究院）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）
