Conférenciers invités

• Interactions atmosphère-terre-océan-cryosphère
  • Gianpaolo Balsamo, Centre européen pour les prévisions météorologiques à moyen terme.
    Representing Earth Surface Processes and Uncertainties in Global Forecasting: which way to errors' reduction?
  • Irina Sandu, Centre européen pour les prévisions météorologiques à moyen terme.
    How uncertainties in surface drag impact the large-scale circulation.
• Nuages et précipitation
  • Stephen Klein, PCMDI, Lawrence Livermore National Laboratory.
    Climate Modeling Challenges Related to Global Cloud Feedbacks.
  • David Neelin, University of California at Los Angeles.
    Convective transition statistics for climate model diagnostics.
  • Masashi Ujiie, Japan Meteorological Agency.
    Recent activities for fixing compensating errors in parametrisation schemes of the JMA operational global model.
• Problèmes de résolution
  • Christoph Schär, Institute for Atmospheric and Climate Science, ETH Zürich.
    Towards Convection-Resolution Climate Modeling.
  • Prashant Sardeshmukh, University of Colorado at Boulder.
    Is ultra-high model resolution necessary to improve probabilistic predictions?
• Téléconnexions
  • John Fyfe, Environnement et Changement climatique Canada.
    Links between low, mid, and high latitudes.
  • David Straus, George Mason University.
    Understanding Tropical – Extratropical Interactions and the MJO.
• Métriques et diagnostics
  • Peter Gleckler, PCMDI, Lawrence Livermore National Laboratory.
    Systematic errors across space and time scales and their relevance to future projections of climate change.
  • Marion Mittermaier, UK Met Office.
    Ensemble versus deterministic performance at km-scale.
• Erreur modèle dans les simulations/prévisions ensemblistes
  • Mark Rodwell, Centre européen pour les prévisions météorologiques à moyen terme.
    Improving flow-dependent reliability - a route to more useful ensemble forecasts.
  • Emilia Sanchez-Gomez, Centre européen de recherche et de formation avancée en calcul scientifique (CERFACS).
    Model drift analysis to understand the causes of systematic errors in climate prediction systems.

Date de modification :

2017-06-13