Progress towards a multi-sensor NASA Program of Record dataset (CLDPROP) for cloud properties from MODIS, VIIRS, and beyond

The NASA CLDPROP cloud-top and optical properties products were designed to bridge the early afternoon data records of MODIS on NASA’s Aqua satellite and VIIRS on the new generation of advanced NOAA operational weather satellites, with the goal of creating a multi-sensor, multi-decadal continuous climate data record for clouds. The CLDPROP algorithms follow a “continuity of approach” paradigm – applying a common algorithm to a subset of channels available on both MODIS and VIIRS, thus mitigating to the extent possible the impacts of differing spectral information content, science algorithm approaches, and ancillary input dataset usage. As the decommissioning of NASA’s venerable Earth Observing System (EOS) missions, including Aqua and its morning afternoon sibling Terra, draws near, sustaining continuity algorithm efforts into the VIIRS era is an essential enabling activity for climate studies and trend monitoring.

Here, we provide details on current CLDPROP product status and evaluation efforts. Currently, Version 1.1 (V1.1) of CLDPROP is available for Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS, with the full data records of each publicly archived and distributed by the LAADS DAAC at NASA Goddard Space Flight Center. Efforts to extend CLDPROP to NOAA-21 VIIRS, including assessing the relative radiometric calibration of the key shortwave channels used for cloud optical property retrievals, is ongoing. We also present planned major science updates for the next iteration of CLDPROP, Version 2 (V2), including pixel-level SW and LW broadband TOA and surface flux calculations enabling radiative studies consistent with the cloud-top/optical property retrievals; a new IR cloud-top properties algorithm that also provides day/night retrievals of ice cloud optical properties; ingesting co-located IR sounder observations (VIIRS+CrIS, MODIS+AIRS) to mitigate the lack of IR absorbing channels on VIIRS that have enhanced information content for high, thin clouds; and a new cloud thermodynamic phase classification algorithm leveraging machine learning.