AUTHOR=Russell Gary L. , Lacis Andrew A. , Carlson Barbara E. , Su Wenying , Pilewskie Juliet A. 
  
TITLE=Global-scale seasonal variability profiles of EPIC-derived vs. GISS ModelE-simulated all-cloud and ice-cloud fraction distributions
  
JOURNAL=Frontiers in Remote Sensing
  
VOLUME=Volume 6 - 2025
  
YEAR=2025
  
URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1691948
  
DOI=10.3389/frsen.2025.1691948
  
ISSN=2673-6187
  
ABSTRACT=Detailed cloud information over the Earth’s sunlit hemisphere is an important EPIC-image biproduct stemming from reflected solar shortwave (SW) flux determination from EPIC-image backscattered radiances. Using MODIS and CERES satellite retrievals EPIC spectral radiances are transformed into pixel-level broadband radiances. Cloud property information gathered from low-Earth-orbit and geostationary retrievals coincident with EPIC-view geometry are selected. CERES angular distribution models (ADMs) are utilized to accomplish the EPIC radiances-to-flux conversion. Cloud, being the principal contributors to Earth’s planetary albedo, are also the controlling factor regulating the Earth’s global energy balance. The prime focus here is to study the global-scale variability of the terrestrial energy balance using global-scale EPIC-derived reflected fluxes and cloud property information obtained with daily time resolution, a unique capability specific only to DSCOVR Mission EPIC data acquired from the Lissajous orbital vantage point around the Lagrangian L1-point. One major sticking point in model/data comparisons is that climate GCMs and the real-world exhibit quasi-chaotic variability. Thus, cloud maps generated from climate GCM output, and satellite data retrievals, can only provide qualitative information in model/data comparisons. Global integration suppresses the ubiquitous weather noise, but issues with viewing geometry, diurnal cycle, and space-time resolution incompatibilities persist in model/data comparisons utilizing traditional monthly-mean GCM output formats and traditional monthly-mean satellite data displays. DSCOVR Mission EPIC data, coupled with DSCOVR satellite ephemeris-enabled GCM data aggregation provide a promising new approach. In this approach, integration over the sunlit hemisphere eliminates the quasi-chaotic weather noise, while assuring identical viewing geometry and consistent diurnal cycle sampling. The Earth’s rotation provides precise longitudinal alignment of the variability. Moreover, this approach also makes possible day-by-day model/data comparisons, and brings into model/data scrutiny relevant cloud process timescales that are otherwise excluded in traditional monthly-mean model/data comparisons. Results to-date show that DSCOVR Mission measurements from the Lagrangian L1 vantage point, including the use of ancillary and biproduct data assembled within this format, constitute a new and powerful capability for model/data variability profile comparisons operating with a 1-day time resolution.