Section: Scientific Foundations
Data assimilation and inverse modelling
This activity is one of the present major stake in environmental sciences. It matches up the setting and the use of data assimilation methods, notably variational methods (4D-var). An emerging point lies in uncertainties propagation in models, notably through ensemble prevision methods.
Although modeling is not part of the scientific objectives of the Clime team, we have a complet access to models developed by CEREA (joint ENPC/EDF R&D laboratory): Polair3D (photochemical pollution forecasting at continental and regional scales) and MERCURE (urban scale) for air quality problems. Concerning other modelling domains, the Clime team accesses models through co-operations. For instance, a shallow model of the Black Sea circulation developped at Marine Hydrophysical Institut (MHI, Ukrain), the radiative transfer model LBLRTM developped at Atmospheric Environment Research (AER), etc .
The research activities tackle scientific issues such as:
Which observational network must be set up for performing a better forecast, taking into account additional criteria such as observation cost? What are the optimal location, type and mode of deployment of sensors? How to operate the trajectories of mobile sensors, while the studied phenomenon is evolving in time? This issue is usually referred as 'network design'.
How to assess the quality of the prediction? How do data quality, missing data, data obtained from sub-optimal locations, affect the forecast? How to better include information on uncertainties (of data, of models) within the data assimilation system?
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?
How to perform forecast (and a better forecast!) by using several models coming from different institutes, or different parameterizations (corresponding to different physical configurations) of the same model? In both cases we have a set of models and it raises the question: how to assimilate data in this context?