Improved retrieval of direct and diffuse downwelling surface shortwave flux in cloudless atmosphere using dynamic estimates of aerosol content and type: application to the LSA-SAF project

Abstract

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).

On the Importance of Aerosol Composition for Estimating Incoming Solar Radiation: Focus on the Western African Stations of Dakar and Niamey during the Dry Season

“…The recent SIRAMix approach developed by Ceamanos et al in [14] was used as the keystone of the second and third methods. The advanced physical parameterization (analytical formulae) employed in this method allows it to retrieve the direct and diffuse DSSF through the use of time-evolving aerosol properties (load and type) as input.…”

“…Ground measurements were used as input. The second and third approaches (SIRAMix1 and SIRAMix2) were based on the SIRAMix methodology [14], which allows the direct and diffuse surface fluxes forming the global DSSF to be calculated by using aerosol properties as input. Under clear sky conditions, aerosols are the major cause of the scattering in the atmosphere that results in the splitting of global DSSF into its direct and diffuse components.…”

“…For example, comparisons with ground observations taken during the African Monsoon Multi-disciplinary Analysis (AMMA) field campaign [13] revealed that the LSA-SAF DSSF product had been overestimated by approximately 10% (30 W/m 2 ) on average during June 2006 [12]. Ceamanos et al showed in [14] that these biases mainly arose from the assumption of constant aerosol properties (AOD and type) in the DSSF calculation. To reduce this bias, a new algorithm, referred to as SIRAMix (Surface Incident Radiation estimation using Aerosol Mixtures), was developed to compute DSSF, together with its direct and diffuse components, using dynamic aerosol properties and considering the coexistence of different aerosol types [14,15].…”

“…Likewise, the regular definition of DSSF (the sum of direct DSSF and diffuse DSSF) will be called "global DSSF". In [14], SIRAMix was tested successfully with aerosol data from the MACC-II (Monitoring Atmospheric Composition and Climate-Interim Implementation) project [16]. However, MACC-II data proved to be insufficiently accurate to enable a detailed investigation of the impact of aerosol composition on the estimation of direct and diffuse DSSF.…”

“…Ceamanos et al showed in [14] that these biases mainly arose from the assumption of constant aerosol properties (AOD and type) in the DSSF calculation. To reduce this bias, a new algorithm, referred to as SIRAMix (Surface Incident Radiation estimation using Aerosol Mixtures), was developed to compute DSSF, together with its direct and diffuse components, using dynamic aerosol properties and considering the coexistence of different aerosol types [14,15]. DSSF can be decomposed into direct and diffuse fluxes, which will be called "direct DSSF" and "diffuse DSSF" hereafter for the sake of simplicity.…”