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. We present here the key features of the Community Cloud retrieval for CLimate(CC4CL) processing algorithm. We focus on the novel features of theframework: the optimal estimation approach in general, explicit uncertaintyquantification through rigorous propagation of all known error sources intothe final product, and the consistency of our long-term, multi-platformtime series provided at various resolutions, from 0.5 to0.02 ∘ . By describing all key input data and processing steps, we aim to inform theuser about important features of this new retrieval framework and itspotential applicability to climate studies. We provide an overview of theretrieved and derived output variables. These are analysed for four, partlyvery challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar withOrthogonal Polarization) observations in the high latitudes and over the Gulfof Guinea–West Africa. The results show that CC4CL provides very realistic estimates of cloud topheight and cover for optically thick clouds but, where optically thin cloudsoverlap, returns a height between the two layers. CC4CL is a unique,coherent, multi-instrument cloud property retrieval framework applicable topassive sensor data of several EO missions. Through its flexibility, CC4CLoffers the opportunity for combining a variety of historic and current EOmissions into one dataset, which, compared to single sensor retrievals, isimproved in terms of accuracy and temporal sampling.

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors