Project-Team Clime

Scientific context

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 environmental sciences, modeling is used to provide, at a given date (past or future), the state of the environment. This is done through the development and application of appropriate models. In the case of atmospheric modeling, studying interaction of underlying events (meteorology, atmospheric chemistry in the various phases of matter, radiative transfer) is a scientific objective in itself (notably for describing aerosols) and it leads to complex models. For these models, the problem of parameterization (change of scale, description of phenomena at subgrid scales) is very important.

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:

  1. How to process data, notably satellite data?

    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:

    • All the available physical information should be accounted for. This information concerns the specifically observed phenomena (e.g. evolution laws) and the process of image formation (e.g. radiative transfer and sensor models).
    • The information is extracted from images in view of data assimilation within a numerical model: the nature of information, as well as the process used to extract it, must therefore be compatible with data assimilation.

  2. How to perform data and model coupling?

    Although methods presently exist (notably data assimilation with the variational approach), many questions are still open:

    • Among a family of models (differing by their physical approximations or their discretization parameters) what is the optimal model for a given set of observations?
    • Which observation network to set up if the goal is to make forecasting with a fixed model and a given margin of error (taking into account observation costs)?
    • What is the optimal location of sensors? And if these sensors are mobile, how to operate their trajectories in function of the situation's evolution?
    • How to perform estimation of uncertainties propagation?

  3. How to build integrated chains data/models/outputs?

    Beyond listed themes above, the development of software chains that allow closely coupling of models and data, raise specific problematics with respect to input (databases) and output (visualization of results). These two points are crucial in the context of environmental applications. For example, in atmospheric chemistry, databases needed by models are very heterogeneous and distributed. Moreover, model outputs fields can be of large dimensions (3D spatial fields evolving in time, with potentially dozens of chemical species); presently, models output representation is poor and with no comparison with potentially available data.
Last update: 05.11.2014