Section: Application Domains


Meteorological forecasting constitutes a major applicative challenge for image assimilation. Although satellite data are operationally assimilated within models, this is mainly done on an independent pixel basis: the observed radiance is linked to the state variables via a radiative transfer model, that plays the role of an observation operator. Indeed, because of their limited spatial and temporal resolutions, numerical weather forecast models fail to exploit image structures, such as precursors of high impact weather:

  • cyclogenesis related to the intrusion of dry stratospheric air in the troposphere (a precursor of cyclones),

  • convective systems (supercells) leading to heavy winter time storms,

  • low-level temperature inversion leading to fog and ice formation, etc.

To date, there is no available method for assimilating such data, which are characterized by a strong coherence in space and time. Meteorologists have developed qualitative Conceptual Models (CMs), for describing the high impact weathers and their signature on images, and tools to detect CMs on image data. The result of this detection is used for correcting the numerical models, for instance by modifying the initialization. The aim is therefore to develop a methodological framework allowing to assimilate the detected CMs within numerical forecast models. This is a challenging issue given the considerable impact of the related meteorological events.