Learning Variational Data Assimilation Models and Solvers
Author:
Affiliation:
1. IMT Atlantique UMR CNRS Lab‐STICC Brest France
2. Ifremer UMR CNRS LOPS Brest France
3. INRIA Rennes UMR CNRS IRMAR Rennes France
4. INPT‐ENM UMR CNRS CNRM CERFACS Toulouse France
5. IMT Atlantique UMR INSERM Latim Brest France
Publisher
American Geophysical Union (AGU)
Subject
General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002572
Reference38 articles.
1. A review of operational methods of variational and ensemble‐variational data assimilation
2. Data Assimilation for Geophysical Fluids
3. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
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