• 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 ...