1 citations · 2 across the 4 of their papers we have counts for
4 papers
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
Marc Bocquet, Alban Farchi, Tobias S. Finn +5
We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, fr…
Towards diffusion models for large-scale sea-ice modelling
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi +2
We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by d…
Towards hourly three-dimensional ensemble data assimilation of screen-level observations into coupled atmosphere-land models
Tobias Finn, Gernot Geppert, Felix Ament
We explore the potential of three-dimensional data assimilation for assimilating sparsely-distributed 2-metre temperature observations across the coupled atmosphere-land interface…
Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models
Tobias Sebastian Finn
Ensemble data from Earth system models has to be calibrated and post-processed. I propose a novel member-by-member post-processing approach with neural networks. I bridge ideas fro…