Section: Overall Objectives
Clime in short
The international politic, economic and scientific contexts are pointing out the role that is played by models and observation systems for forecasting and evaluating environmental risks.
The complexity of environmental phenomena as well as the operational objectives of risk mitigation necessitate an intensive interweaving between geophysical models, data processing, simulation, visualization and database tools.
For illustration purpose, we observe that this situation is met in the domain of atmospheric pollution, whose modeling is gaining an ever-increasing significance and impact, either at local (air quality), regional (transboundary pollution) or global scale (greenhouse effect). In this domain, numerical modeling systems are used for operational forecasts (short or long term), detailed case studies, impact studies for industrial sites, as well as coupled modeling, such as pollution and health or pollution and economy. These scientific subjects strongly require linking/coupling the models with all available data 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 with researchers in data assimilation, image processing, and modeling.
Clime carries out research activities in three main areas:
Image assimilation: assimilation of structures in environmental forecasting models, study of ill-posed image processing problems with data assimilation technics, definition of dynamic models from images, reduction of models.