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A Model for Estimating the Earth's Outgoing Radiative Flux from A Moon-Based Radiometer.

Authors :
Zhang, Yuan
Dewitte, Steven
Bi, Shengshan
Source :
Remote Sensing. Aug2023, Vol. 15 Issue 15, p3773. 23p.
Publication Year :
2023

Abstract

A Moon-based radiometer can provide continuous measurements for the Earth's full-disk broadband irradiance, which is useful for studying the Earth's Radiation Budget (ERB) at the height of the Top of the Atmosphere (TOA). The ERB describes how the Earth obtains solar energy and emits energy to space through the outgoing broadband Short-Wave (SW) and emitted thermal Long-Wave (LW) radiation. In this work, a model for estimating the Earth's outgoing radiative flux from the measurements of a Moon-based radiometer is established. Using the model, the full-disk LW and SW outgoing radiative flux are gained by converting the unfiltered entrance pupil irradiances (EPIs) with the help of the anisotropic characteristics of the radiances. Based on the radiative transfer equation, the unfiltered EPI time series is used to validate the established model. By comparing the simulations for a Moon-based radiometer with the satellite-based data from the National Institute of Standards and Technology Advanced Radiometer (NISTAR) and the Clouds and the Earth's Radiant Energy System (CERES) datasets, the simulations show that the daytime SW fluxes from the Moon-based measurements are expected to vary between 194 and 205 Wm−2; these simulations agree well with the CERES data. The simulations are about 5 to 20 Wm−2 smaller than the NISTAR data. For the simulated Moon-based LW fluxes, the range is 251~287 Wm−2. The Moon-based and NISTAR fluxes are consistently 5~15 Wm−2 greater than CERES LW fluxes, and both of them also show larger diurnal variations compared with the CERES fluxes. The correlation coefficients of SW fluxes for Moon-based data and NISTAR data are 0.97, 0.63, and 0.53 for the months of July, August, and September, respectively. Compared with the SW flux, the correlation of LW fluxes is more stable for the same period and the correlation coefficients are 0.87, 0.69, and 0.61 for July to September 2017. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
15
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
169923167
Full Text :
https://doi.org/10.3390/rs15153773