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Shortwave Radiance to Irradiance Conversion for Earth Radiation Budget Satellite Observations: A Review

Authors :
Maria Z. Hakuba
Peter Pilewskie
Jacqueline E. Russell
Thomas H. Vonder Haar
Norman G. Loeb
K. Sebastian Schmidt
Wenying Su
Jake J. Gristey
Florian Tornow
Source :
Remote Sensing, Vol 13, Iss 2640, p 2640 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Observing the Earth radiation budget (ERB) from satellites is crucial for monitoring and understanding Earth’s climate. One of the major challenges for ERB observations, particularly for reflected shortwave radiation, is the conversion of the measured radiance to the more energetically relevant quantity of radiative flux, or irradiance. This conversion depends on the solar-viewing geometry and the scene composition associated with each instantaneous observation. We first outline the theoretical basis for algorithms to convert shortwave radiance to irradiance, most commonly known as empirical angular distribution models (ADMs). We then review the progression from early ERB satellite observations that applied relatively simple ADMs, to current ERB satellite observations that apply highly sophisticated ADMs. A notable development is the dramatic increase in the number of scene types, made possible by both the extended observational record and the enhanced scene information now available from collocated imager information. Compared with their predecessors, current shortwave ADMs result in a more consistent average albedo as a function of viewing zenith angle and lead to more accurate instantaneous and mean regional irradiance estimates. One implication of the increased complexity is that the algorithms may not be directly applicable to observations with insufficient accompanying imager information, or for existing or new satellite instruments where detailed scene information is not available. Recent advances that complement and build on the base of current approaches, including machine learning applications and semi-physical calculations, are highlighted.

Details

ISSN :
20724292
Volume :
13
Database :
OpenAIRE
Journal :
Remote Sensing
Accession number :
edsair.doi.dedup.....c21e3484f247521b7f857735adcedcea
Full Text :
https://doi.org/10.3390/rs13132640