Presentation | NG23A: Advances in Data Assimilation, Data Fusion, Machine Learning, Predictability, and Uncertainty Quantification in the Geosciences III: Developments in Machine Learning Across Earth System Modeling: Subgrid-Scale Parameterizations, Emulation, and Hybrid Modeling III Oral
Oral
NG23A-03: Integrating ideas from nonlinear data assimilation into machine learning
Author(s): Peter Jan van Leeuwen, Colorado State University (First Author, Presenting Author)
Data assimilation and machine learning have much in common. Here, we discuss how advances in nonlinear data assimilation can be used to make very powerful generative machine learning methods consistent data assimilation methods, solving an important long-standing machine-learning problem.
