The Land Surface Analysis Satellite Applications Facility (LSA SAF) project radiation fluxes, derived from the Meteosat Second Generation (MSG) geostationary satellite, were used in the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM), which is a component of the Surface Externalisée (SURFEX) modeling platform. The Système d’Analyze Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) atmospheric analysis provides high-resolution atmospheric variables used to drive LSMs over France. The impact of using the incoming solar and infrared radiation fluxes [downwelling surface shortwave (DSSF) and longwave (DSLF), respectively] from either SAFRAN or LSA SAF, in ISBA, was investigated over France for 2006. In situ observations from the Flux Network (FLUXNET) were used for the verification. Daily differences between SAFRAN and LSA SAF radiation fluxes averaged over the whole year 2006 were 3.75 and 2.61 W m−2 for DSSF and DSLF, respectively, representing 2.5% and 0.8% of their average values. The LSA SAF incoming solar radiation presented a better agreement with in situ measurements at six FLUXNET stations than the SAFRAN analysis. The bias and standard deviation of differences were reduced by almost 50%. The added value of the LSA SAF products was assessed with the simulated surface temperature, soil moisture, and the water and energy fluxes. The latter quantities were improved by the use of LSA SAF satellite estimates. As many areas lack a high-resolution meteorological analysis, the LSA SAF radiative products provide new and valuable information.
Abstract. Downwelling surface shortwave flux (DSSF) is a key parameter to addressing many climate, meteorological, and solar energy issues. Under clear sky conditions, DSSF is particularly sensitive to the variability both in time and space of the aerosol load and chemical composition. Hitherto, this dependence has not been properly addressed by the Satellite Application Facility on Land Surface Analysis (LSA-SAF), which operationally disseminates instantaneous DSSF products over the continents since 2005 considering constant aerosol conditions. In the present study, an efficient method is proposed for DSSF retrieval that will overcome the limitations of the current LSA-SAF product. This method referred to as SIRAMix (Surface Incident Radiation estimation using Aerosol Mixtures) is based upon an accurate physical parameterization coupled with a radiative transfer-based look up table of aerosol properties. SIRAMix considers a tropospheric layer composed of several major aerosol species that are conveniently mixed to reproduce real aerosol conditions as best as possible. This feature of SIRAMix allows it to provide not only accurate estimates of global DSSF but also the direct and diffuse DSSF components, which are crucial radiative terms in many climatological applications. The implementation of SIRAMix is tested in the present article using atmospheric analyses from the European Center for Medium-Range Weather Forecasts (ECMWF). DSSF estimates provided by SIRAMix are compared against instantaneous DSSF measurements taken at several ground stations belonging to several radiation measurement networks. Results show an average root mean square error (RMSE) of 23.6, 59.1, and 44.9 W m −2 for global, direct, and diffuse DSSF, respectively. These scores decrease the average RMSE obtained for the current LSA-SAF product by 18.6 %, which only provides global DSSF for the time being, and, to a lesser extent, for the state of the art in the matter of DSSF retrieval (RMSE decrease of 10.9, 6.5, and 19.1 % for global, direct, and diffuse DSSF with regard to the McClear algorithm). The main limitation of the proposed approach is its high sensitivity to the quality of the ECMWF aerosol inputs, which is proved to be sufficiently accurate for reanalyses but not for forecast data. Given the proximity of DSSF retrieval to the modeling of the atmospheric direct effect, SIRAMix is also able to quantify the direct radiative forcing at the surface due to a given atmospheric component (e.g., gases or aerosols).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.