• 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 ...
    • Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data 

      Brajard, Julien; Counillon, Francois Stephane; Wang, Yiguo; Kimmritz, Madlen (Journal article; Peer reviewed, 2023)
      Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time consuming and by the time the forecast is delivered, new observations ...
    • Super-resolution data assimilation 

      Barthelemy, Sebastien Jean-Claude; Brajard, Julien; Bertino, Laurent; Counillon, Francois Stephane (Journal article; Peer reviewed, 2022)
      Increasing model resolution can improve the performance of a data assimilation system because it reduces model error, the system can more optimally use high-resolution observations, and with an ensemble data assimilation ...
    • Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas 

      Silva, Edson; Counillon, François; Brajard, Julien; Korosov, Anton; Pettersson, Lasse H; Samuelsen, Annette; Keenlyside, Noel (Journal article; Peer reviewed, 2021)
      Phytoplankton blooms provide biomass to the marine trophic web, contribute to the carbon removal from the atmosphere and can be deadly when associated with harmful species. This points to the need to understand the phenology ...