CCClim - A machine-learning powered cloud class climatology

CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud\_cci dataset, adding relative occurrences of eight major cloud types as defined by the World Meteorological Organization (WMO) at 1° resolution.  The cloud types are predicted using a two stage machine learning framework, in which a 1 km pixel-level classifier is followed up with a grid-box scale Random Forest regression model. CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological time scale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models. The compressed tarball contains 35 netCDF files, each covering one calendar year. Each file provides daily averages of nine cloud-related variables and the nine classes (eight cloud types+undetermined) as per 1° grid box fractional amounts. Cloud-related variables: cloud water path ice water path liquid water path cloud optical depth effective liquid droplet radius at cloud top effective ice particle radius at cloud top cloud top pressure surface temperature cloud area fraction Cloud types: Ci: Cirrus/Cirrostratus As: Altostratus Ac: Altocumulus St: Stratus Sc: Stratocumulus Cu: Cumulus Ns: Nimbostratus Dc: Deep convective