NASA GEO Imager Research Algorithm Dataset for Cloud Optical Properties

The advanced capabilities of the new generation of operational weather satellite imagers in low-Earth orbit (LEO; e.g., VIIRS) and geostationary (GEO; e.g., ABI, AHI, etc.), having spectral and spatial capabilities analogous to the NASA Earth Observing System (EOS) MODIS, offer the opportunity to extend the high impact EOS MODIS dataset for clouds into the next decade and into the time domain. Such a merged, and consistent, LEO/GEO cloud product Program of Record (PoR) can enable enhanced climate and process studies by NASA investigators and the broader research community. In addition, this PoR is desired to provide critical synergy with the NASA Atmosphere Observing System (AOS), which is currently in formulation and is designed to address the Aerosols, Clouds, Convection, and Precipitation Designated Observables identified by the 2018 NASA Earth Science Decadal Survey. We will give an overview of a NASA research cloud optical property algorithm for the new GEO imagers that was developed to provide consistency with the NASA MODIS/VIIRS cloud continuity products CLDPROP. Here, we show results of our efforts to evaluate the consistency of this new GEO cloud dataset against the MODIS/VIIRS continuity dataset. We also will discuss ongoing challenges towards achieving LEO/GEO product consistency, including the impacts of fundamental differences in sensor specifications and/or orbits (e.g., spectral channel differences, spatial resolution/swath, viewing/solar geometries), differences in relative radiometric calibration, and forward radiative model issues.