气候变化领域集成服务门户

A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar (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 cloud scenes account for 90% of all observations and contain one, two, or three cloud types. The number of possible cloud scenes is shown to depend on spatial scale with a maximum number of 210 out of 256 possible scenes at a scale of 105 km and fewer cloud scenes at smaller and larger scales. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity. The likelihood of ice and liquid-phase detection strongly depends on the CloudSat-identified cloud scene type collocated with the AIRS footprint. Cloud scenes primarily consisting of cirrus, nimbostratus, altostratus, and deep convection are dominated by ice-phase detection, while stratocumulus, cumulus, and altocumulus are dominated by liquid- and undetermined-phase detection. Ice cloud particle size and optical thickness are largest for cloud scenes containing deep convection and cumulus and are smallest for cirrus. Cloud scenes with multiple cloud types have small reductions in information content and slightly higher residuals of observed and modeled radiance compared to cloud scenes with single cloud types. These results will help advance the development of temperature, specific humidity, and cloud property retrievals from hyperspectral infrared sounders that include cloud microphysics in forward radiative transfer models.