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. The Kyoto agreement or Johannesburg meeting mark some political moves. At a more scientific level, initiatives like the European GMES program (Global Monitoring of Environment and Security) will give a long term structure to environmental research by insisting notably on the importance of observational data and satellite measures exploitation.
A typical example is met in atmospheric pollution, which is gaining a widening importance, either at small (air quality), regional (transboundary pollution) or global scale (greenhouse effect). Atmospheric pollution problems motivate important needs for decision tools based on models and computer softwares. From a scientific viewpoint, the complexity of the phenomena taken into consideration and the operational objectives necessitate a growing interweaving between physical models, data processing, simulation and database tools.
It seems fundamental to couple all available data, these data being either of numerical origin (e.g. models outputs), or coming from raw observation (e.g. satellite data or spatial grids of acquired data), or obtained by processing and analysis of the observations (e.g. chemical concentrations retrieved by inversion of a radiative transfer model). This side of the research: collaboration between models and data, is the substantial scientific input of this proposition in comparison with the thematics studied by the two original teams: data assimilation and modeling (CEREA laboratory), environmental data processing (AIR project).