Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation

Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation

Abstract Data assimilation (DA) is integrated with machine learning in order to perform entirely data‐driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as pretrained surrogate models to replace key components of the DA cycle in numerical weather predic...

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Bibliographic Details
Main Authors: S. G. Penny, T. A. Smith, T.‐C. Chen, J. A. Platt, H.‐Y. Lin, M. Goodliff, H. D. I. Abarbanel
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-03-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
data assimilation
recurrent neural networks
machine learning
artificial intelligence
ensemble kalman filter
4D‐var
Online Access:https://doi.org/10.1029/2021MS002843

Internet

https://doi.org/10.1029/2021MS002843

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