The quest for an accurate retrieval of vertically complex cloud layers from passive instruments.

Abstract

Accurate cloud properties retrievals from passive instruments are particularly challenging in presence of vertically highly in-homogeneous and/or multiple cloud layers because of the inherent lack of observational constraints for the vertical profile below the cloud top. The Optimal estimation algorithm for Cloud Analysis (OCA) developed at EUMETSAT is capable of retrieving cloud properties of up to two overlapping layers using radiances from imaging instruments. Example of such instruments are the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG), the Flexible Combined Imager (FCI) aboard the Meteosat Third Generation and METimage aboard the Metop-Second Generation. Since 2013 OCA retrievals have been operational as demonstrational product for MSG-SEVIRI and are now ready to be disseminated as operational products for MTG-FCI. A number of improvements to the baseline OCA algorithm have been implemented recently including a better initialisation of cloud phase and multi-layer flag and a new forward model to enable the use of solar channels not only in single-layer but also in multi-layer cloud conditions. We also introduced a cloud model with a more complete representation of the vertical inhomogeneity in optical properties. Using observations from the MSG-SEVIRI and MTG-FCI in the visible/near-infrared and infrared channels, we tested the updated algorithm to retrieve simultaneously a set of cloud microphysical and optical properties. To evaluate the accuracy of the retrieval we employ a number of retrieved cloud vertical profiles from Lidar/Radar measurements from both collocated A-Train orbits and in-situ data from the ACTRIS-Cloudnet network. The use of the solar channels in both single and multi-layer clouds enables a more consistent retrieval of their microphysical properties (effective radius and optical thickness). The addition of the vertical inhomogeneity has a significant impact on the retrieved cloud top pressure, bringing it closer to the estimates from the cloud Lidar. The new version of OCA allows for a more complete and consistent retrieval of single- and two-layer cloud profiles and provides some further insights on the vertical distribution of cloud parameters. This in turn can be helpful for various applications such as the height assignment of Atmospheric Motion Vectors and specific visualisations of cloud products for forecasters.