In this article, a possible way to improve one of the techniques currently used by the numerical weather prediction (NWP) community to assimilate observations from hyperspectral infrared sounders (e.g. the Infrared Atmospheric Sounding Interferometer, IASI) in the presence of clouds is investigated. In particular, attention is focused on increasing the complexity of the cloud radiative model – where clouds are considered as single-layer grey bodies of negligible depth – used by a one-dimensional variational retrieval (1D-Var) cloud analysis that provides cloud parameters used in forward calculations within the global data assimilation system. A new two-layer cloud scheme is tested in the 1D-Var and results are compared with a single layer approach. In the new scheme four cloud-analysis control variables are included and are analysed simultaneously with temperature and humidity profiles. A validation of the new scheme using both simulated and observed IASI radiances shows that the two-layer cloud representation reduces significantly the bias in the mean profiles of retrieved minus background temperature differences, particularly in less homogeneous scenes. 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 – shows that the new scheme is more accurate in more heterogeneous scenes. This suggests that an a priori assessment of the degree of inhomogeneity in a scene may be useful to constrain the data appropriately, leading to a more effective use of cloudy radiances within the global 4D-Var assimilation.