Remote sensing of cloud optical and microphysical properties has always been challenging, even though numerous efforts relying on either passive or active sensor alone have been conducted. To better characterize these cloud properties, we developed two algorithms for liquid and ice cloud property retrievals using combined satellite passive–active measurements. The first algorithm takes into account vertical inhomogeneity of the droplet radius in liquid cloud property retrievals using CloudSat and MODIS measurements. The vertical profile of the droplet radius is parameterized with empirical orthogonal functions to accelerate computational efficiency of the retrieval process. It reveals that a conventional plane parallel homogeneous cloud assumption can cause cloud droplet effective radius retrievals to be underestimated by a maximum of 30%. The second algorithm takes into account horizontally oriented plates (HOP) in ice cloud property retrievals using CALIOP and IIR measurements. The forward model considers the single-scattering properties of a HOP. One-month global ice cloud retrievals demonstrate that the fraction of HOPs in cirrus clouds have both latitudinal variation and temperature dependence up to -70°C.
In the talk, I will show an application study using cloud retrievals based on these two algorithms.
Seminar Series Coordinators