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Status: Unchecked ×Keyword: 4D-Var ×Clear all

3 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Learning earth system models from observations: machine learning or data assimilation?

   Geer · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2021

   The article sets out how data assimilation and machine learning can be unified through Bayesian networks, so as to make better use of observations to improve earth system models.

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   1. UncheckedData assimilation and machine learning are both inverse methods and can be brought together within a Bayesian (probabilistic) framework.“DA and ML have many similarities: they are both inverse methods that can be united under a Bayesian (probabilistic) framework.”
   2. UncheckedBayesian networks can't be solved fully, but approximate methods from data assimilation and machine learning could make them a practical way to unite the two.“Their full Bayesian solution is not computationally feasible but these networks can be solved with approximate methods already used in DA and ML, so they could provide a practical framework for the unification of the two.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation

   Wang, Duan, Ni et al. · Journal of Advances in Modeling Earth Systems · 2025

   The authors propose 4DVarGAN, which combines a conditional generative adversarial network with 4DVar prior knowledge to produce weather initial fields faster and with lower errors than other deep-learning methods.

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   1. UncheckedThe 4DVarGAN data assimilation method is reported to work stably when started from background fields of differing quality.“Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results.”