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. A method is described to classify cloud mixtures of cloudtop types, termed cloud scenes, using cloud type classification derived from the CloudSatradar (2B-CLDCLASS). The scale dependence of the cloud scenes is quantified.For spatial scales at 45 km (15 km), only 18 (10) out of 256 possible cloudscenes account for 90 % of all observations and contain one, two,or three cloud types. The number of possible cloud scenes is shown to dependon spatial scale with a maximum number of 210 out of 256 possible scenes ata scale of 105 km and fewer cloud scenes at smaller and larger scales. Thecloud scenes are used to assess the characteristics of spatially collocatedAtmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloudproperty retrievals within scenes of varying cloud type complexity. Thelikelihood of ice and liquid-phase detection strongly depends on theCloudSat-identified cloud scene type collocated with the AIRS footprint.Cloud scenes primarily consisting of cirrus, nimbostratus, altostratus, anddeep convection are dominated by ice-phase detection, while stratocumulus,cumulus, and altocumulus are dominated by liquid- and undetermined-phasedetection. Ice cloud particle size and optical thickness are largest forcloud scenes containing deep convection and cumulus and are smallest forcirrus. Cloud scenes with multiple cloud types have small reductions ininformation content and slightly higher residuals of observed and modeledradiance compared to cloud scenes with single cloud types. These resultswill help advance the development of temperature, specific humidity, andcloud property retrievals from hyperspectral infrared sounders that includecloud microphysics in forward radiative transfer models.

Footprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievals