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