Sensitivity analysis of a novel hyper-spectral infrared cloud and atmosphere property retrieval based on an optimal estimation framework

This work introduces a novel hyper-spectral cloud and atmosphere property retrieval using an optimal estimation framework (FEANOR). The flexibility of the algorithm allows to retrieve cirrus cloud parameters alone (optical depth, effective radius and cloud top height) or simultaneously derive also atmospheric information (water vapor and temperature profiles) of the scene. Moreover, with this framework the uncertainties associated with the retrieved quantities are computed as well. When only the cloud parameters are derived, FEANOR offers the opportunity to investigate the sensitivity of the retrieval to atmospheric parameters. This aims at providing the scientific community with useful guidelines for developing and improving cloud retrievals in the infrared, and also shows the advantages of using hyper-spectral measurements instead of broadband data.