Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation-Reference-Cited by-同舟云学术

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

Published:2022-03 Issue:3 Volume:14 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Penny S. G.¹²^ORCID,Smith T. A.¹²^ORCID,Chen T.‐C.¹²^ORCID,Platt J. A.³^ORCID,Lin H.‐Y.¹²^ORCID,Goodliff M.¹²^ORCID,Abarbanel H. D. I.³⁴^ORCID

Affiliation:

1. Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder Boulder CO USA

2. Physical Sciences Laboratory (PSL) National Oceanic and Atmospheric Administration (NOAA) Boulder CO USA

3. Department of Physics University of California San Diego (UCSD) La Jolla CA USA

4. Scripps Institution of Oceanography (SIO) La Jolla CA USA

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002843

Reference66 articles.

1. Analysis of Observed Chaotic Data

2. The Statistical Physics of Data Assimilation and Machine Learning

3. Local Lyapunov exponents computed from observed data

4. Data assimilation with regularized nonlinear instabilities

5. Machine Learning: Deepest Learning as Statistical Data Assimilation Problems

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