Publication Abstracts
Russell et al. 2025
, , , W. Su, and , 2025: Global-scale seasonal variability profiles of EPIC-derived vs GISS ModelE-simulated all-cloud and ice-cloud fraction distributions. Front. Remote Sens., 6, 1691948, doi:10.3389/frsen.2025.1691948.
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. Clouds, being the principal contributors to Earth's planetary albedo, are also the controlling factor regulating the Earth's global energy balance. With the relatively short and time-variable atmospheric life-cycle, characteristic cloud-process signatures should be appearing in the reflected solar SW radiation from Earth. 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, the cloud maps generated from climate GCM output, and satellite data retrievals, can only provide qualitative information in model/data comparisons. Global integration suppresses meteorological 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 obliterated 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.
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BibTeX Citation
@article{ru08400w,
author={Russell, G. L. and Lacis, A. and Carlson, B. E. and Su, W. and Pilewskie, J. A.},
title={Global-scale seasonal variability profiles of EPIC-derived vs GISS ModelE-simulated all-cloud and ice-cloud fraction distributions},
year={2025},
journal={Frontiers in Remote Sensing},
volume={6},
pages={1691948},
doi={10.3389/frsen.2025.1691948},
}
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RIS Citation
TY - JOUR ID - ru08400w AU - Russell, G. L. AU - Lacis, A. AU - Carlson, B. E. AU - Su, W. AU - Pilewskie, J. A. PY - 2025 TI - Global-scale seasonal variability profiles of EPIC-derived vs GISS ModelE-simulated all-cloud and ice-cloud fraction distributions JA - Front. Remote Sens. JO - Frontiers in Remote Sensing VL - 6 SP - 1691948 DO - 10.3389/frsen.2025.1691948 ER -
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