In many cases, these biases could be spotted and corrected by the simultaneous exploitation of measured solar radiances. In turn, these biases can lead to an erroneous estimation of the radiative effect of ice clouds. Since most radar–lidar retrieval algorithms rely heavily on universal mass–size relationships to parameterize the prevalent ice particle shape, biases in ice water content and ice water path can be expected in individual cloud regimes. While active backscatter retrieval techniques surpass the information content of most passive, vertically integrated retrieval techniques, their accuracy is limited by essential assumptions about the ice crystal shape. This study focuses on the well-established variational approach VarCloud to retrieve ice cloud microphysics from radar–lidar measurements. This knowledge can be significantly improved by active remote sensing, which can help to explore the vertical profile of ice cloud microphysics, such as ice particle size and ice water content. The uncertainty in predicting ice cloud feedbacks in a warming climate arises due to uncertainties in measuring and explaining their current optical and microphysical properties as well as from insufficient knowledge about their spatial and temporal distribution. Ice clouds and their effect on earth's radiation budget are one of the largest sources of uncertainty in climate change predictions.
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