Articles | Volume 28, issue 1
https://doi.org/10.5194/npg-28-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.Training a convolutional neural network to conserve mass in data assimilation
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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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2023Cited articles
Bishop, C. H., Etherton, B. J., and Majumdar, S.: Adaptive sampling with the
ensemble transform Kalman filter. Part I: Theoretical aspects., Mon.
Weather Rev., 129, 420–436, 2001. a
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and
expectation-maximization, Foundations of Data Science, 2, 55–80,
https://doi.org/10.3934/fods.2020004, 2020. a
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data
assimilation and machine learning to emulate a dynamical model from sparse
and noisy observations: A case study with the Lorenz 96 model, J.
Comput. Sci.-Neth, 44, 101171,
https://doi.org/10.1016/j.jocs.2020.101171, 2020a. a, b
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data
assimilation and machine learning to infer unresolved scale parametrisation, arXiv [preprint], arXiv:2009.04318, 9 September 2020b. a
Brenowitz, N. D. and Bretherton, C. S.: Spatially Extended Tests of a Neural
Network Parametrization Trained by Coarse-Graining, J. Adv. Model. Earth Sy., 11, 2728–2744, https://doi.org/10.1029/2019MS001711, 2019. a