| Summary: | Numerical weather prediction centres have started assimilating directly cloudy radiances from hyperspectral infrared sounders (e.g. Infrared Atmospheric Sounding Interferometer, IASI), by using channels sensitive to the atmosphere above the clouds. Radiative effects are usually modelled with a simple cloud scheme in which clouds are assumed to be single-layer grey bodies of negligible depth. In particular, one of those techniques makes use of a onedimensional variational retrieval (lD-Var) cloud analysis to provide the cloud parameters that are then utilised to constrain the forward calculations within the global assimilation. In this thesis we investigate an extension of this technique by increasing the complexity of the cloud model to allow a more effective use of the sounding measurements. A new cloud scheme enabling an additional cloud layer to be treated in a ID-Var setting has been proposed. In this scheme the infrared effects are modelled by means of four cloud parameters and are retrieved simultaneously with temperature and humidity profiles. A validation of the new scheme using both simulated and observed IASI radiances showed that the two-layer-cloud representation reduces significantly the bias in the mean profiles of retrieved minus background temperature differences, particularly in multilayer cloud formations. However, the bias is still too large to allow useful assimilation of channels below the cloud. Nevertheless, providing a better estimate of cloud position is valuable as it helps to prevent the assimilation of channels sensitive to the atmosphere below the cloud. Furthermore, the statistical analysis of the retrievals from IASI measurements - complemented by the Advanced Very High Resolution Radiometer cluster information - showed that the model is more accurate in less heterogeneous scenes. This suggests that an a priori assessment of cloud layers present in a scene may be used to constrain appropriately the data leading to a more effective use of cloudy radiances within the full assimilation.
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