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) and the French Grenelle de l’Environnement 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 programs such as the Air Chemistry program which give a long term structure to environmental research. These initiatives emphasize the importance of observational data and also the potential 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 modeling is gaining an ever-increasing significance, either at local (air quality), regional (transboundary pollution) or global scale (greenhouse effect). In this domain, modeling systems are used for operational forecasts (short or long term), detailed case studies, impact studies for industrial sites, as well as coupled modeling (e.g. pollution and health, pollution and economy). These scientific subjects strongly require linking the models with all available data; these data being either of physical origin (e.g. models outputs), coming from raw observations (satellite acquisitions and/or information measured in situ by an observation network), or obtained by processing and analysis of these observations (e.g. chemical concentrations retrieved by inversion of a radiative transfer model).
Clime has been created for studying these questions by joining in one single team researchers in data assimilation, modeling, and image processing.
In the meantime, the question of models' representativeness is surfacing: how to build models with low degrees of freedom from the description of these complex processes? How to make these models useful for impact studies and able to perform data assimilation?
Models alone are generally insufficient because they need unknown input (initial and boundary conditions, parameters, ...). These unknown inputs require complimentary data. Consequently, there is presently a growing interweaving between models and observation systems (data providing systems). Among these observation systems, satellite data will play a major role in the short term, through acquisitions from all the environmental missions started these latter years. Data assimilation procedures, whatever their methodological nature, allow this data and models coupling.
In such a context, the whole data/models/output chain must be considered, with many resulting problematics:
The overall objective is the extraction of dynamic information from sequences of images -and notably satellite images- for provision to environmental forecast or monitoring systems. Extraction of information must satisfy the following constraints:
Although methods presently exist (notably data assimilation with the variational approach), many questions are still open: