Remote sensing of real and artificial clouds with Meteosat Second Generation

Abstract

Remote sensing of clouds is a challenging task, due to the inherent inhomogeneity of clouds on all spatial scales and in time. Cloud remote sensing is generally based on two assumptions: (1) that clouds and reflected or emitted radiance are homogeneous over the sensor's field-of-view; and (2), that individual pixels may be treated independently of each other. Both assumptions have been shown to cause systematic biases and random noise in he retrieved optical and microphysical properties. Here we present for the first time an extended satellite scene simulated with a three-dimensional radiative transfer code, based on the output of a cloud-resolving model. The thus created data is used as a tool to test and improve cloud detection algorithms as well as microphysical property retrievals.