CCClim - A machine-learning powered cloud class climatology

<p>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.&nbsp;</p><p>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.</p><p>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.</p><p>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.</p><p>Cloud-related variables:</p><ul><li>cloud water path</li><li>ice water path</li><li>liquid water path</li><li>cloud optical depth</li><li>effective liquid droplet radius at cloud top</li><li>effective ice particle radius at cloud top</li><li>cloud top pressure</li><li>surface temperature</li><li>cloud area fraction</li></ul><p>Cloud types:</p><ul><li>Ci: Cirrus/Cirrostratus</li><li>As: Altostratus</li><li>Ac: Altocumulus</li><li>St: Stratus</li><li>Sc: Stratocumulus</li><li>Cu: Cumulus</li><li>Ns: Nimbostratus</li><li>Dc: Deep convective</li></ul>