Daily Downward Surface Shortwave Flux (DIDSSF)

NRT Product available since Jul 2010


Product Documentation

This operational product is documented in the Algorithm Theoretical Basis Document (ATBD), Product User Manual document (PUM) and the Product Output Format document (POF) The validation results for this product are available in the (VR) document.


Data Policy

The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
DSSF and DIDSSF were provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)

Trigo, I. F., C. C. DaCamara, P. Viterbo, J.-L. Roujean, F. Olesen, C. Barroso, F. Camacho-de Coca, D. Carrer, S. C. Freitas, J. García-Haro, B. Geiger, F. Gellens-Meulenberghs, N. Ghilain, J. Meliá, L. Pessanha, N. Siljamo, and A. Arboleda, 2011: The Satellite Application Facility on Land Surface Analysis. Int. J. Remote Sens., 32, 2725-2744, doi: 10.1080/01431161003743199



The Downward Surface Shortwave Flux (DSSF) refers to the radiative energy in the wavelength interval [0.3 µm, 4.0 µm] reaching the Earth's surface per time and surface unit. It essentially depends on the solar zenith angle, on cloud coverage, and to a lesser extent on atmospheric absorption and surface albedo. DSSF fields are crucial for a wide number of applications involving scientific domains like weather forecast, hydrology, climate, agriculture and environment-related studies. In numerical weather prediction and general circulation models of the atmosphere, satellite-derived DSSF estimates can either be used as a control variable or as a substitute to surface radiation measurement networks.


Product Description

The DSSF product is generated with a temporal frequency of 30 minutes at the full spatial resolution of the MSG/SEVIRI instrument. It is based on the three short-wave MSG/SEVIRI channels (VIS 0.6 µm, NIR 0.8 µm, SWIR 1.6 µm). Information on cloud cover is obtained from the output of the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) software. Dynamic information on the atmospheric water vapour content comes from the ECMWF numerical weather prediction model. Climatologic values are currently used for ozone concentration, aerosol properties, and surface albedo.


Algorithm Description

The method for the retrieval of DSSF that is implemented in the LSA SAF system largely follows previous developments achieved at Météo-France in the framework of the SAF on Ocean & Sea-Ice (Brisson et al., 1999; OSI SAF, 2002). Separate algorithms are applied for clear sky and cloudy sky situations. In the presence of clouds the downward radiation reaching the ground is considerably reduced. The DSSF is strongly anti-correlated with the observable top-of-atmosphere reflectances: The brighter the clouds appear on the satellite images, the more radiation is reflected by them and the less radiation reaches the surface. In this case the top-of-atmosphere albedo is first calculated from the observed directional reflectance values by applying a broadband conversion and an angular dependence model. The top-of-atmosphere albedo then serves as the most important input information for a simple physical parameterisation of the radiation transfer in the cloud-atmosphere-surface system. In the clear sky method the DSSF estimate is directly determined with an empirical parameterisation for the effective transmittance of the atmosphere as a function of the concentration of atmospheric constituents.


Data Characteristics

The DSSF product is computed within the area covered by the MSG disk, over four specific geographical regions (Europe, Africa - N_Africa and S_Africa- and South America). For each time-slot and geographical region the DSSF estimate and a corresponding processing flag are disseminated in HDF5 format. The relevant information concerning the data fields is included in the HDF5 attributes.


Product Uncertainties

In addition to inherent uncertainties due to methodological simplifications, the quality of the DSSF product also depends on the suitability of the cloud mask, the sensor performance (calibration uncertainty), and the accuracy of input information concerning the atmospheric constituents and the surface albedo. For each of the different processing flags defined (mainly clear sky and cloudy sky) an estimate for the product accuracy in terms of bias and standard deviation will be given in the Validation Report and Product User Manual.



Brisson A., LeBorgne P., Marsouin A., 1999, Development of Algorithms for Surface Solar Irradiance retrieval at O&SI SAF low and Mid Latitude, Météo-France/CMS, Lannion.
Manalo-Smith N., G.L. Smith, S.N. Tiwari, W. F. Staylor, 1998, Analytic forms of bi-directional reflectance functions for application to Earth radiation budget studies, Journal of Geophysical Research, Vol. 103, D16, pp. 19, 733-19, 751.
>Ocean & Sea Ice SAF, 2002, Surface Solar Irradiance Product Manual, Version 1.2

Example of Product