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Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

This study used realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE mission, was applied to CloudSat, CALIPSO and MODIS satellite data thus producing 3D clou...

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Autores principales: Barker, H. W., Kato, S., Wehr, T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456078/
https://www.ncbi.nlm.nih.gov/pubmed/26069353
http://dx.doi.org/10.1007/s10712-011-9164-9
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author Barker, H. W.
Kato, S.
Wehr, T.
author_facet Barker, H. W.
Kato, S.
Wehr, T.
author_sort Barker, H. W.
collection PubMed
description This study used realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE mission, was applied to CloudSat, CALIPSO and MODIS satellite data thus producing 3D cloudy atmospheres measuring 61 km wide by 14,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances were then computed by a Monte Carlo photon transfer model run in both full 3D and 1D independent column approximation modes. Results were averaged into 1,303 (50 km)(2) domains. For domains with total cloud fractions A (c) < 0.7 top-of-atmosphere (TOA) albedos tend to be largest for 3D transfer with differences increasing with solar zenith angle. Differences are largest for A (c) > 0.7 and characterized by small bias yet large random errors. Regardless of A (c), differences between 3D and 1D transfer rarely exceed ±30 W m(−2) for net TOA and surface fluxes and ±10 W m(−2) for atmospheric absorption. Horizontal fluxes through domain sides depend on A (c) with ∼20% of cases exceeding ±30 W m(−2); the largest values occur for A (c) > 0.7. Conversely, heating rate differences rarely exceed ±20%. As a cursory test of TOA radiative closure, fluxes produced by the 3D model were averaged up to (20 km)(2) and compared to values measured by CERES. While relatively little attention was paid to optical properties of ice crystals and surfaces, and aerosols were neglected entirely, ∼30% of the differences between 3D model estimates and measurements fall within ±10 W m(−2); this is the target agreement set for EarthCARE. This, coupled with the aforementioned comparison between 3D and 1D transfer, leads to the recommendation that EarthCARE employ a 3D transport model when attempting TOA radiative closure.
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spelling pubmed-44560782015-06-09 Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data Barker, H. W. Kato, S. Wehr, T. Surv Geophys Article This study used realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE mission, was applied to CloudSat, CALIPSO and MODIS satellite data thus producing 3D cloudy atmospheres measuring 61 km wide by 14,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances were then computed by a Monte Carlo photon transfer model run in both full 3D and 1D independent column approximation modes. Results were averaged into 1,303 (50 km)(2) domains. For domains with total cloud fractions A (c) < 0.7 top-of-atmosphere (TOA) albedos tend to be largest for 3D transfer with differences increasing with solar zenith angle. Differences are largest for A (c) > 0.7 and characterized by small bias yet large random errors. Regardless of A (c), differences between 3D and 1D transfer rarely exceed ±30 W m(−2) for net TOA and surface fluxes and ±10 W m(−2) for atmospheric absorption. Horizontal fluxes through domain sides depend on A (c) with ∼20% of cases exceeding ±30 W m(−2); the largest values occur for A (c) > 0.7. Conversely, heating rate differences rarely exceed ±20%. As a cursory test of TOA radiative closure, fluxes produced by the 3D model were averaged up to (20 km)(2) and compared to values measured by CERES. While relatively little attention was paid to optical properties of ice crystals and surfaces, and aerosols were neglected entirely, ∼30% of the differences between 3D model estimates and measurements fall within ±10 W m(−2); this is the target agreement set for EarthCARE. This, coupled with the aforementioned comparison between 3D and 1D transfer, leads to the recommendation that EarthCARE employ a 3D transport model when attempting TOA radiative closure. Springer Netherlands 2011-12-29 2012 /pmc/articles/PMC4456078/ /pubmed/26069353 http://dx.doi.org/10.1007/s10712-011-9164-9 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
Barker, H. W.
Kato, S.
Wehr, T.
Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data
title Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data
title_full Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data
title_fullStr Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data
title_full_unstemmed Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data
title_short Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data
title_sort computation of solar radiative fluxes by 1d and 3d methods using cloudy atmospheres inferred from a-train satellite data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4456078/
https://www.ncbi.nlm.nih.gov/pubmed/26069353
http://dx.doi.org/10.1007/s10712-011-9164-9
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