206 citations · 966 across the 31 of their papers we have counts for
4 papers · 1 filter
Generative AI models capture realistic sea-ice evolution from days to decades
Tobias Sebastian Finn, Marc Bocquet, Pierre Rampal +4
Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invarian…
Multi-Scale Data Assimilation in Turbulent Models
Francesco Fossella, Luca Biferale, Alberto Carrassi +2
We explore the potential of Data-Assimilation (DA) within the multi-scale framework of a shell model of turbulence, with a focus on the Ensemble Kalman Filter (EnKF). The central o…
Hybrid machine learning data assimilation for marine biogeochemistry
Ieuan Higgs, Ross Bannister, Jozef Skákala +2
Marine biogeochemistry models are critical for forecasting, as well as estimating ecosystem responses to climate change and human activities. Data assimilation (DA) improves these…
Ensemble Kalman filter in latent space using a variational autoencoder pair
Ivo Pasmans, Yumeng Chen, Tobias Sebastian Finn +2
Popular (ensemble) Kalman filter data assimilation (DA) approaches assume that the errors in both the a priori estimate of the state and those in the observations are Gaussian. For…