On the satellite determination of multi-layered multi-phase cloud properties

Passive satellite-retrieved cloud properties are subject to large uncertainties in the presence of multi-layered and/or multi-phase clouds. Such large uncertainties are mainly attributed to the conventional assumption of a single-layered and single-phase cloud employed in current satellite cloud retrieval algorithms. That is, cloud properties such as height, temperature, optical depth, emissivity and hydrometeor phase and size are deduced for a single layer of cloud, assuming single cloud phase, using the satellite-measured radiance data. Knowledge of the properties of multi-layered multi-phase clouds has been limited by the interpretation of passive satellite radiances based on such a single-layer cloud assumption. Active satellite sensors of NASA's A-Train Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite and the 94-GHz Cloud Profiling Radar onboard the CloudSat are ideal for determining the multi-layered multi-phased cloud vertical structure, but are limited by their nadir-only pointing observations and their Sun-synchronous orbits. Until the challenges of actively sensing clouds on large spatial scales are overcome, it is necessary to develop and test new techniques for retrieving multilayered cloud properties using passively sensed radiances. This study exploits a combination of a few earlier and recently developed cloud property retrieval techniques for a feasible enhancement in detecting and retrieving multi-layered multi-phase cloud properties using the passive Moderate-resolution Imaging Spectroradiometer (MODIS) data. Relative sensitivities and limitations of the passive retrievals to the CALIPSO/CloudSat multilayered cloud retrievals will be evaluated using the A-Train matched satellite observations.