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| Titel |
High-resolution satellite-based cloud-coupled estimates of total downwelling surface radiation for hydrologic modelling applications |
| VerfasserIn |
B. A. Forman, S. A. Margulis |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 13, no. 7 ; Nr. 13, no. 7 (2009-07-07), S.969-986 |
| Datensatznummer |
250011924
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| Publikation (Nr.) |
 copernicus.org/hess-13-969-2009.pdf |
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| Zusammenfassung |
| A relatively simple satellite-based radiation model yielding high-resolution
(in space and time) downwelling longwave and shortwave radiative fluxes at
the Earth's surface is presented. The primary aim of the approach is to
provide a basis for deriving physically consistent forcing fields for
distributed hydrologic models using satellite-based remote sensing data. The
physically-based downwelling radiation model utilises satellite inputs from
both geostationary and polar-orbiting platforms and requires only
satellite-based inputs except that of a climatological lookup table derived
from a regional climate model. Comparison against ground-based measurements
over a 14-month simulation period in the Southern Great Plains of the United
States demonstrates the ability to reproduce radiative fluxes at a spatial
resolution of 4 km and a temporal resolution of 1 h with good accuracy
during all-sky conditions. For hourly fluxes, a mean difference of −2 W m−2
with a root mean square difference of 21 W m−2 was found for
the longwave fluxes whereas a mean difference of −7 W m−2 with a root
mean square difference of 29 W m−2 was found for the shortwave fluxes.
Additionally, comparison against advanced downwelling longwave and solar
insolation products during all-sky conditions showed comparable uncertainty
in the longwave estimates and reduced uncertainty in the shortwave
estimates. The relatively simple form of the model enables future usage in
ensemble-based applications including data assimilation frameworks in order
to explicitly account for input uncertainties while providing the potential
for conditioning estimates from other readily available products derived
from more sophisticated retrieval algorithms. |
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