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CLIME Research team

Coupling environmental data and simulation models for software integration

  • Leader : Isabelle Herlin
  • Research center(s) : CRI de Paris
  • Field : Digital Health, Biology and Earth
  • Theme : Earth, Environmental and Energy Sciences

Team presentation

The international political and scientific context is indicating the serious potential risks related to environmental problems, and is also pointing out the role that can be played by models and observation systems for the evaluation and forecasting of these risks. At the political level, agreements such as the Kyoto protocol, European directives on air quality or on major accident hazards involving dangerous substances (Seveso directive) establish objectives for the mitigation of environmental risks. These objectives are supported at a scientific level by international initiatives like the European GMES program (Global Monitoring of Environment and Security), or national programmes such as the Air Chemistry programme, which will give a long term structure to environmental research. These initiatives emphasize the importance of observational data and also the exploitation of satellite acquisitions.

The complexity of the environmental phenomena, as well as the operational objectives, necessitate a growing interweaving between physical models, data processing, simulation and database tools.

This situation is met for instance in atmospheric pollution, an environmental domain whose modelling is gaining a widening importance, either at small (air quality), regional (transboundary pollution) or global scale (greenhouse effect). In this domain, modelling systems are used for operational forecast (short or long term), detailed case studies, impact studies for industrial sites, management of different spatial and temporal scales, coupled modelling (e.g. pollution and health, pollution and economy). These scientific subjects strongly require coupling the model with all available data; these data being either of numerical origin (e.g. models outputs), or coming from raw observations (e.g. satellite acquisitions or information measured on a spatial grid), or obtained by processing and analysis of these observations (e.g. chemical concentrations retrieved by inversion of a radiative transfer model).

The CLIME project-team has been created for studying these questions by joining researchers in data assimilation and modelling from the CEREA laboratory (ENPC, Ecole Nationale des Ponts et Chaussées) and INRIA researchers in environmental data and image processing.

Research themes

The CLIME project-team carries out research in three directions:

  • Environmental data processing, notably satellite data, by means of computer vision techniques and by accounting for the physical information on the acquisition process and on the dynamics of the observed phenomena.
  • Data and model coupling, by means of data assimilation techniques and related issues (optimality problems, uncertainties propagation, ...).
  • Integrated chains of data/models/outputs (system architecture, workflows, databases, visualisation).

International and industrial relations

International Relations:
Argentina (University of Cordoba, National Centre for Nuclear Energy), Brazil (State University of Rio de Janeiro, Federal University of Rio de Janeiro and Embrapa Solos), Chile (Centre for Mathematical Modelling, Meteorological Office), Germany (Fraunhofer-FIRST), Greece (FORTH-IACM), Russia (Institute of Numerical Analysis), Ukraine (Marine Hydrophysical Institute).

Industrial Relations:

  • INERIS: source diffusion modeling (SO2, NOx) at european scale, ozone forecasting (probabilistic methods)
  • IRSN: radinuclide dispersion forecasting and source inverse modeling.
  • LYNX: estimation of damages caused by hail events to vineyards using satellite images.

Keywords: Remote sensing Data assimilation Inverse problems Software integration