206 citations · 966 across the 31 of their papers we have counts for
10 papers · 1 filter
Hybrid physics-data-driven modeling for sea ice thermodynamics and transfer learning
Giovanni De Cillis, Alberto Carrassi, Julien Brajard +5
This study explores a physics-data driven hybrid approach for sea-ice column physics models, in which a machine learning (ML) component acts as a state-dependent parameterization o…
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…
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…
Tailoring data assimilation to discontinuous Galerkin models
Ivo Pasmans, Yumeng Chen, Alberto Carrassi +1
During the last few years discontinuous Galerkin (DG) methods have received increased interest from the geophysical community. In these methods the solution in each grid cell is ap…
Probabilistic forecasts of sea ice trajectories in the Arctic: impact of uncertainties in surface wind and ice cohesion
Sukun Cheng, Ali Aydoğdu, Pierre Rampal +2
We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in the sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM is based on a brittle-li…
On temporal scale separation in coupled data assimilation with the ensemble Kalman filter
Maxime Tondeur, Alberto Carrassi, Stephane Vannitsem +1
Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward worldwide. The core issue is the…