Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day Albedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a climate data record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: One for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition, that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

In the present study we compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in southern India (Gadanki, 13.45° N, 79.18° E) from April 2008 to March 2011. While the comparison between total (coarse mode + fine mode) AODs shows correlation coefficient (R) value of about 0.71 for Terra and 0.77 for Aqua, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R value of 0.44 for both Terra and Aqua. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. The seasonal variation is also well captured by both ground-based and satellite measurements. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less than one-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India. Instead of selecting a moderately absorbing aerosol model (as being done currently in the MODIS retrieval) a more absorbing aerosol model could be a better fit for the fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively absorbing type, usage of coarse sea-salt particles which is less absorbing is more appropriate. However, not all the differences could be accounted based on aerosol model, other factors like errors in retrieval of surface reflectance may also be significant in causing underestimation of AOD by MODIS.

Geostationary satellites are increasingly used for the detection and tracking of atmospheric aerosols and, in particular, of the aerosol optical depth (AOD). The main advantage of these spaceborne platforms in comparison with polar orbiting satellites is their capability to observe the same region of the Earth several times per day with varying geometry. This provides a wealth of information that makes aerosol remote sensing possible when combined with the multi-spectral capabilities of the on-board imagers. Nonetheless, the suitability of geostationary observations for AOD retrieval may vary significantly depending on their spatial, spectral, and temporal characteristics. In this work, the potential of geostationary satellites was assessed based on the concept of critical surface albedo (CSA). CSA is linked to the sensitivity of each spaceborne observation to the aerosol signal, as it is defined as the value of surface albedo for which a varying AOD does not alter the satellite measurement. In this study, the sensitivity to aerosols was determined by estimating the difference between the surface albedo of the observed surface and the corresponding CSA (referred to as dCSA). The values of dCSA were calculated for one year of observations from the Meteosat Second Generation (MSG) spacecraft, based on radiative transfer simulations and information on the satellite acquisition geometry and the properties of the observed surface and aerosols. Different spectral channels from MSG and the future Meteosat Third Generation-Imager were used to study their distinct capabilities for aerosol remote sensing. Results highlight the significant but varying potential of geostationary observations across the observed Earth disk and for different time scales (i.e., diurnal, seasonal, and yearly). For example, the capability of sensing multiples times during the day is revealed to be a notable strength. Indeed, the value of dCSA often fluctuates significantly for a given day, which makes some instants of time more suitable for aerosol retrieval than others. This study determines these instants of time as well as the seasons and the sensing wavelengths that increase the chances for aerosol remote sensing thanks to the variations of dCSA. The outcomes of this work can be used for the development and refinement of AOD retrieval algorithms through the use of the concept of CSA. Furthermore, results can be extrapolated to other present-day geostationary satellites such as Himawari-8/9 and GOES-16/17.

Several studies have shown that changes in incoming solar radiation and variations of the diffuse fraction can significantly modify the vegetation carbon uptake. Hence, monitoring the incoming solar radiation at large scale and with high temporal frequency is crucial for this reason along with many others. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis (LSA SAF) has operationally disseminated in near real time estimates of the downwelling shortwave radiation at the surface since 2005. This product is derived from observations provided by the SEVIRI instrument onboard the Meteosat Second Generation series of geostationary satellites, which covers Europe, Africa, the Middle East, and part of South America. However, near real time generation of the diffuse fraction at the surface level has only recently been initiated. The main difficulty towards achieving this goal was the general lack of accurate information on the aerosol particles in the atmosphere. This limitation is less important nowadays thanks to the improvements in atmospheric numerical models. This study presents an upgrade of the LSA SAF operational retrieval method, which provides the simultaneous estimation of the incoming solar radiation and its diffuse fraction from the satellite every 15 min. The upgrade includes a comprehensive representation of the influence of aerosols based on physical approximations of the radiative transfer within an atmosphere-surface associated medium. This article explains the retrieval method, discusses its limitations and differences with the previous method, and details the characteristics of the output products. A companion article will focus on the evaluation of the products against independent measurements of solar radiation. Finally, the access to the source code is provided through an open access platform in order to share the expertise on the satellite retrieval of this variable with the community.

Land surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. This article presents the algorithm concepts for the remote sensing of this variable based on the heritage of several developments which were performed at Méteo France over the last decade and described in several papers by Carrer et al. The scientific algorithm comprises four steps: an atmospheric correction, a sensor harmonisation (optional), a BRDF (Bidirectional Reflectance Distribution Function) inversion, and the albedo calculation. At the time being, the method has been applied to 11 sensors in the framework of two European initiatives (Satellite Application Facility on Land Surface Analysis—LSA SAF, and Copernicus Climate Change Service—C3S): NOAA-7-9-11-14-16-17/AVHRR2-3, SPOT/VGT1-2, Metop/AVHRR-3, PROBA-V, and MSG/SEVIRI. This work leads to a consistent archive of almost 40 years of satellite-derived albedo data (available in 2020). From a single sensor, up to three different albedo products with different characteristics have been developed to address the requirements of both, near real-time (NRT) (weather prediction with a demand of timeliness of 1 h) and climate communities. The evaluation of the algorithm applied to different platforms was recently made by Lellouch et al. and Sánchez Zapero et al. in 2020 which can be considered as companion papers. After a summary of the method for the retrieval of these surface albedos, this article describes the specificities of each retrieval, lists the differences, and discusses the limitations. The plan of continuity with the next European satellite missions and perspectives of improvements are introduced. For example, Metop/AVHRR-3 albedo will soon become the medium resolution sensor product with the longest NRT data record, since MODIS is approaching the end of its life-cycle. Additionally, Metop-SG/METimage will ensure its continuity thanks to consistent production of data sets guaranteed till 2050 by the member states of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In the end, the common strategy which we proposed through the different programmes may offer an unprecedented opportunity to study the temporal trends affecting surface properties and to analyse human-induced climate change. Finally, the access to the source code (called PYALUS) is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable.

High frequency knowledge of the spatio-temporal distribution of the downwelling surface shortwave flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface–atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (MSG Downwelling Surface Short-wave radiation Fluxes—Total and Diffuse fraction) product with an operational status since the year 2019. The method for retrieval was presented in a companion paper. Part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents a comparison of the corresponding outputs, i.e., total DSSF and diffuse fraction (fd) components, against in situ measurements acquired at four Baseline Surface Radiation Network (BSRN) stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20 Wm⁻² for DSSF < 200 Wm⁻², and 10% for DSSF ≥ 200 Wm⁻². The mean bias error (MBE) and relative mean bias error (rMBE) compared to the ground measurements are 3.618 Wm⁻² and 0.252%, respectively. For fd, the requirements are 0.1 for fd < 0.5, and 20% for fd ≥ 0.5. The MBE and rMBE compared to the ground measurements are −0.044% and −17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud–aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the aerosol optical depth (AOD) forecasts currently available is accurate enough to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.