no code implementations • 22 Jan 2021 • Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner
We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids.
no code implementations • 26 Apr 2021 • Maike Sonnewald, Redouane Lguensat, Daniel C. Jones, Peter D. Dueben, Julien Brajard, Venkatramani Balaji
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
1 code implementation • 21 Jun 2021 • Bryan E. Kaiser, Juan A. Saenz, Maike Sonnewald, Daniel Livescu
The advent of big data has vast potential for discovery in natural phenomena ranging from climate science to medicine, but overwhelming complexity stymies insight.
1 code implementation • 30 Apr 2022 • Mariana C. A. Clare, Maike Sonnewald, Redouane Lguensat, Julie Deshayes, Venkatramani Balaji
The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network.
1 code implementation • 21 Oct 2023 • William Yik, Maike Sonnewald, Mariana C. A. Clare, Redouane Lguensat
In this region, THOR specifically reveals a shift in dynamical regime under climate change driven by changes in wind stress and interactions with bathymetry.
no code implementations • 21 Feb 2024 • Simon Dräger, Maike Sonnewald
Machine Learning has become a pervasive tool in climate science applications.