Articles | Volume 29, issue 2
https://doi.org/10.5194/npg-29-171-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.Using neural networks to improve simulations in the gray zone
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
Data-driven methods to estimate the committor function in conceptual ocean models
Downscaling of surface wind forecasts using convolutional neural networks
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
A two-folds deep learning strategy to correct and downscale winds over mountains
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Nonlin. Processes Geophys., 30, 195–216,
2023Nonlin. Processes Geophys. Discuss.,
2023Revised manuscript accepted for NPG
Nonlin. Processes Geophys., 30, 167–181,
2023Nonlin. Processes Geophys. Discuss.,
2023Revised manuscript accepted for NPG
Nonlin. Processes Geophys., 29, 301–315,
2022Cited 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