Browsing Geophysical Institute by Author "Carrassi, Alberto"
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Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Bocquet, Marc; Brajard, Julien; Carrassi, Alberto; Bertino, Laurent (Peer reviewed; Journal article, 2019)Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how ... -
Data assimilation using adaptive, non-conservative, moving mesh models
Aydogdu, Ali; Carrassi, Alberto; Guider, Colin T.; Jones, Chris K.R.T.; Rampal, Pierre (Peer reviewed; Journal article, 2019)Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures ... -
Improving weather and climate predictions by training of supermodels
Schevenhoven, Francine Janneke; Selten, Frank; Carrassi, Alberto; Keenlyside, Noel (Peer reviewed; Journal article, 2019-11-28)Recent studies demonstrate that weather and climate predictions potentially improve by dynamically combining different models into a so-called “supermodel”. Here, we focus on the weighted supermodel – the supermodel's time ... -
Training a supermodel with noisy and sparse observations: A case study with CPT and the synch rule on SPEEDO-v.1
Schevenhoven, Francine Janneke; Carrassi, Alberto (Journal article; Peer reviewed, 2022)As an alternative to using the standard multi-model ensemble (MME) approach to combine the output of different models to improve prediction skill, models can also be combined dynamically to form a so-called supermodel. The ...