A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets

DOI10.1016/j.jcp.2021.110484OpenAlexW3167344324WikidataQ114183735 ScholiaQ114183735MaRDI QIDQ2131005

Christian Lessig, Boyko Dodov, Themistoklis P. Sapsis, Henk A. Dijkstra, Zhong Yi Wan

Publication date: 25 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110484

zbMATH Keywords

extreme eventsclimate dataconditionally Gaussian stochastic modelsmachine learning statisticsstochastic downsampling of turbulence

Mathematics Subject Classification ID

Turbulence (76Fxx) Artificial intelligence (68Txx) Multivariate analysis (62Hxx)

Related Items (1)

Structure preservation for the deep neural network multigrid solver

Uses Software

• WaveNet

Cites Work

• Stochastic superparameterization in a one-dimensional model for wave turbulence
• Cyclostationarity: half a century of research
• Neural network modeling for near wall turbulent flow.
• The nature of triad interactions in homogeneous turbulence
• Efficient stochastic superparameterization for geophysical turbulence
• Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers
• Geophysical flows under location uncertainty, Part I Random transport and general models
• Geophysical flows under location uncertainty, Part II Quasi-geostrophy and efficient ensemble spreading
• Geophysical flows under location uncertainty, Part III SQG and frontal dynamics under strong turbulence conditions
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