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MOISE Research team

Modelling, Observations, Identification for Environmental Sciences

  • Leader : Eric Blayo-nogret
  • Research center(s) : CRI Grenoble - Rhône-Alpes
  • Field : Digital Health, Biology and Earth
  • Theme : Earth, Environmental and Energy Sciences
  • Partner(s) : Université Joseph Fourier (Grenoble),Institut polytechnique de Grenoble,CNRS
  • Collaborator(s) : UJF (GRENOBLE), GRENOBLE INP, CNRS, UPMF (GRENOBLE)

Team presentation

MOISE is a research project-team in applied mathematics and scientific computing, focusing on the development of mathematical and numerical methods for direct and inverse modelling in environmental applications (mainly geophysical fluids). The scientific backdrop of this project-team is the design of complex forecasting systems, our overall applicative aim being to contribute to the improvement of such systems, especially those relating to natural hazards : climate change, regional forecasting systems for the ocean and the atmosphere, decision tools for floods, snow avalanches, and mud or lava flows. A number of specific features are shared by these different applications : interaction of different scales, multi-component aspects, necessity of combining heterogeneous sources of information (models, measurements, images), uniqueness of each event. The development of efficient methods for these applications therefore requires taking these features into account, a goal which covers several aspects, namely:
  • mathematical and numerical modelling
  • data assimilation (determistic and stochastic approaches)
  • quantification of forecast uncertainties.
Pluridisciplinarity is a key aspect of the project, so that the part of our work that is more related to applications is performed in close collaboration with specialists from the different fields involved (geophysicists, etc).

Research themes

  • 1. Design and optimization of complex forecasting systems : Forecasting geophysical systems require complex models, which sometimes need to be coupled, and which make use of data assimilation.
  • 2. Processing of heterogeneous data : Earth observations from space have provided us with an important source of information for several decades. Up to now this information has been used in more of a qualitative than quantitative way. An important avenue of research is therefore the assimilation of single images and sequences of images, which is of potential interest for numerous applications. This objective requires complex image processing and new data assimilation schemes.
  • 3. Quantification of uncertainties : Given the present strong development of forecast systems in geophysics, the ability to provide an estimate of the uncertainty of the forecast is of course a ma jor issue. However, the systems under consideration are very complex, and providing such an estimation is very challenging. Several mathematical approaches are possible, using either variational or stochastic tools.

International and industrial relations

  • School of Computational Sciences, Florida State University
  • Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow
  • Several meteorologic, oceanographic and data assimilation research centres worldwide

Keywords: Modelling Inverse methods Data assimilation Scientific computing Environmental sciences