Dataset for AMT-2017-261 by DeSouza-Machado et. al. 1D-variational retrievals of temperature and moisture fields from hyperspectral infrared satellite sounders use cloud-cleared radiances as their observation. These derived observations allow the use of clear-sky only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud-clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that when using multi-layer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2-4 degrees of freedom of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from Numerical Weather Prediction (NWP) models as a first guess, together with a simple cloud representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (typically one for ice and one for water clouds). From these, one run of our fast cloud representation model allows an improvement of the \emph{a-priori} cloud state by comparing the observed and model simulated radiances in the thermal window channels. The retrieval yield is over 90\%, while the degrees of freedom correlate with the observed window channel brightness temperature which itself depends on the cloud optical depth. The cloud representation/scattering package is bench-marked against radiances computed using a Maximum Random Overlap cloud scheme. All-sky infrared radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Center for Medium Range Weather Forecasting (ECMWF) forecast model are used in this paper.