New cloud property datasets based on measurements from the passive imaging
satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two
retrieval systems were developed that include components for cloud detection
and cloud typing followed by cloud property retrievals based on the optimal
estimation (OE) technique. The OE-based retrievals are applied to
simultaneously retrieve cloud-top pressure, cloud particle effective radius
and cloud optical thickness using measurements at visible, near-infrared and
thermal infrared wavelengths, which ensures spectral consistency. The
retrieved cloud properties are further processed to derive cloud-top height,
cloud-top temperature, cloud liquid water path, cloud ice water path and
spectral cloud albedo. The Cloud_cci products are pixel-based retrievals,
daily composites of those on a global equal-angle latitude–longitude grid, and
monthly cloud properties such as averages, standard deviations and histograms,
also on a global grid. All products include rigorous propagation of the
retrieval and sampling uncertainties. Grouping the orbital properties of the
sensor families, six datasets have been defined, which are named AVHRR-AM,
AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each
comprising a specific subset of all available sensors. The individual
characteristics of the datasets are presented together with a summary of the
retrieval systems and measurement records on which the dataset generation were
based. Example validation results are given, based on comparisons to well-
established reference observations, which demonstrate the good quality of the
data. In particular the ensured spectral consistency and the rigorous
uncertainty propagation through all processing levels can be considered as new
features of the Cloud_cci datasets compared to existing datasets. In addition,
the consistency among the individual datasets allows for a potential
combination of them as well as facilitates studies on the impact of temporal
sampling and spatial resolution on cloud climatologies