Articles | Volume 13, issue 7
Hydrol. Earth Syst. Sci., 13, 969–986, 2009
https://doi.org/10.5194/hess-13-969-2009
Hydrol. Earth Syst. Sci., 13, 969–986, 2009
https://doi.org/10.5194/hess-13-969-2009

  07 Jul 2009

07 Jul 2009

High-resolution satellite-based cloud-coupled estimates of total downwelling surface radiation for hydrologic modelling applications

B. A. Forman and S. A. Margulis B. A. Forman and S. A. Margulis
  • Department of Civil and Environmental Engineering, University of California at Los Angeles, Los Angeles, California, 90095, USA

Abstract. 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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