Research article
| Highlight paper
02 May 2022
Research article
| Highlight paper
| 02 May 2022
Using neural networks to improve simulations in the gray zone
Raphael Kriegmair et al.
Related authors
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Predicting sea surface temperatures with coupled reservoir computers
The blessing of dimensionality for the analysis of climate data
Producing realistic climate data with generative adversarial networks
Identification of droughts and heatwaves in Germany with regional climate networks
Nonlin. Processes Geophys., 29, 301–315,
2022Nonlin. Processes Geophys., 29, 255–264,
2022Nonlin. Processes Geophys., 28, 409–422,
2021Nonlin. Processes Geophys., 28, 347–370,
2021Nonlin. Processes Geophys., 28, 231–245,
2021Cited articles
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
Bolton, T. and Zanna, L.: Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization, J. Adv. Model. Earth Sy., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. 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., 44, 101171, https://doi.org/10.1016/j.jocs.2020.101171, 2020. a
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to infer unresolved scale parametrization, Philos. T. Roy. Soc. A, 379, 20200086, https://doi.org/10.1098/rsta.2020.0086, 2021. 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, b