3S-GEOPROF-COMB: A Global Gridded Dataset for Cloud Vertical Structure from combined CloudSat and CALIPSO observations

<p>Global cloud dataset from combined spaceborne radar and lidar.</p> <p>This repository contains the 3S-GEOPROF-COMB product, a globally-gridded dataset for cloud vertical structure retrieved from hybrid active remote sensing (CloudSat radar and CALIPSO lidar) reported at 240 m vertical resolution. Science variables include vertical cloud fraction and vertically-integrated cloud cover for various geometrical criteria (i.e. high, middle, low, and thick clouds, along with with unique high, middle, and low cloud cover variants).</p> <p>A Python notebook showing how to work with the dataset is available <a href="https://github.com/bertrandclim/3S-GEOPROF-COMB/blob/main/notebooks/brief_intro.ipynb">on GitHub</a>, as is the source code used to produce the data product.</p> <p>Our product is calculated from the latest release (R05) of per-orbit (level 2) combined cloud mask profiles in 2B-GEOPROF-LIDAR with additional data from 2B-GEOPROF. Validation and a complete description of the data product is given in the paper <a href="https://doi.org/10.5194/essd-16-1301-2024">"A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations"</a> (Earth System Science Data).</p> <p>Please cite "Bertrand, L., Kay, J. E., Haynes, J., and de Boer, G.: A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations, Earth Syst. Sci. Data, 16, 1301&ndash;1316, https://doi.org/10.5194/essd-16-1301-2024, 2024."</p> <p>The files contained in each folder are given via the following format:</p> <p><strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; instruments_frequency_resolution.zip</strong></p> <ul> <li><strong>instruments:</strong> <ul> <li><strong>radarlidar:</strong> the standard product, computed from merged geometrical profiles of hydrometeor occurrence</li> <li><strong>radaronly:</strong> computed solely from CloudSat radar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.</li> <li><strong>lidaronly: </strong>computed solely from CALIPSO lidar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.</li> </ul> </li> <li><strong>frequency:</strong> <ul> <li><strong>monthly:</strong> data files report fields aggregated over a 1-month period</li> <li><strong>seasonal:</strong> data files report fields aggregated over a 3-month period (DJF, MAM, JJA, SON)</li> </ul> </li> <li><strong>resolution:</strong> <ul> <li><strong>2.5x2.5: </strong>each grid box spans 2.5 degrees latitude and 2.5 degrees longitude</li> <li><strong>5x5:</strong> each grid box spans 5 degrees latitude and 5 degrees longitude</li> <li><strong>10x10:</strong> each grid box spans 10 degrees latitude and 10 degrees longitude</li> </ul> </li> </ul> <p>Each folder contains a netCDF data file and a cloud cover quicklook plot image file for each time period over the 2006-2019 data record. Individual files are named according to the following format:</p> <p><strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; timeperiod_instruments_datastream_version.nc (or .png)</strong></p> <ul> <li><strong>timeperiod: </strong>the time step at the given frequency, either e.g. 2006-08 (August 2006) or 2012-DJF (December 2012 to February 2013).</li> <li><strong>instruments</strong><strong>:</strong> the instruments used in the data product as a whole, always CSCAL (CloudSat and CALIPSO).</li> <li><strong>datastream:</strong> either 3S-GEOPROF-COMB (COMBined radar and lidar), 3S-GEOPROF-COMB-RO (the auxiliary Radar Only variant of the product), or 3S-GEOPROF-COMB-LO (the auxiliary Lidar Only variant of the product)</li> <li><strong>version:</strong> current release is v8.4</li> </ul> <p>The product handles the 2011 CloudSat battery anomaly, after which the satellite only collects data in the sunlit portion of its orbit, by allowing users to subsample the pre-anomaly period to mimic the post-anomaly collection patterns. This allows users to estimate the effect of the reduced sampling on their analyses or apply a consistent sampling mode to the entire dataset. This option is provided to users via the "<strong>doop</strong>" dimension. Dimension coordinate value "All cases" reports variables computed using all observations, while "DO-OP observable" reports variables using only input data that either were or would have been collected in DO-OP mode (i.e. the pre-DO-OP period is subsampled to DO-OP collection patterns).</p>