Solar surface radiation data of high quality is essential for the appropriate monitoring and analysis of the Earth's radiation budget and the climate system. Further, they are crucial for the efficient planning and operation of solar energy systems. However, well maintained surface measurements are rare in many regions of the world and over the oceans. There, satellite derived information is the exclusive observational source. This emphasizes the important role of satellite based surface radiation data. Within this scope, the new satellite based CM-SAF SARAH (Solar surfAce RAdiation Heliosat) data record is discussed as well as the retrieval method used. The SARAH data are retrieved with the sophisticated SPECMAGIC method, which is based on radiative transfer modeling. The resulting climate data of solar surface irradiance, direct irradiance (horizontal and direct normal) and clear sky irradiance are covering 3 decades. The SARAH data set is validated with surface measurements of the Baseline Surface Radiation Network (BSRN) and of the Global Energy and Balance Archive (GEBA). Comparison with BSRN data is performed in order to estimate the accuracy and precision of the monthly and daily means of solar surface irradiance. The SARAH solar surface irradiance shows a bias of 1.3 \(W/m^2\) and a mean absolute bias (MAB) of 5.5 \(W/m^2\) for monthly means. For direct irradiance the bias and MAB is 1 \(W/m^2\) and 8.2 \(W/m^2\) respectively. Thus, the uncertainty of the SARAH data is in the range of the uncertainty of ground based measurements. In order to evaluate the uncertainty of SARAH based trend analysis the time series of SARAH monthly means are compared to GEBA. It has been found that SARAH enables the analysis of trends with an uncertainty of 1 \(W/m^2/dec\); a remarkable good result for a satellite based climate data record. SARAH has been also compared to its legacy version, the satellite based CM-SAF MVIRI climate data record. Overall, SARAH shows a significant higher accuracy and homogeneity than its legacy version. With its high accuracy and temporal and spatial resolution SARAH is well suited for regional climate monitoring and analysis as well as for solar energy applications.

With an increased share of solar and wind energy e.g. in the German and European energy systems it is becoming increasingly important to analyze the impact of weather variability on the reliability of the energy production. In this study, we calculate solar PV and wind power capacity factors using two recently developed climatological datasets that provide information with high spatial and temporal details on the continental (European) scale and are of sufficient length for assessments at climatological time scales: Surface radiation derived from meteorological satellites (SARAH-2) and wind speed from a high-resolution regional reanalysis (COSMO-REA6). Balancing effects are analysed: On average, the seasonal cycles of PV and wind power production complement each other in Germany as well as in Europe. The frequency of events with a risk of low electricity generation is analyzed under different assumptions. When using wind energy over German land areas as a reference case, the results illustrate that the number of low production events is reduced when Germany's Exclusive Economic Zone is included into the analysis, or when a combined system of PV and wind energy is considered. A European-wide analysis also leads to a distinct reduction of such events.

The amount of energy reaching Earth's surface from the Sun is a quantity of high importance for the climate system and for renewable energy applications. SARAH-3 (SurfAce Radiation DAtaset Heliosat, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003, Pfeifroth et al., 2023) is a new version of a satellite-based climate data record of surface solar radiation parameters, generated and distributed by the European Organisation of Meteorological Satellites (EUMETSAT) Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 provides data from 1983 onwards, i.e. more than 4 decades of data, and has a spatial resolution of 0.05° × 0.05°, a temporal resolution of 30 min and daily and monthly means for the region covered by the Meteosat field of view (65° W to 65° E and 65° S to 65° N). SARAH-3 consists of seven parameters: surface irradiance, direct irradiance, direct normal irradiance, sunshine duration, daylight, photosynthetically active radiation and effective cloud albedo. SARAH-3 data between 1983 and 2020 have been generated with stable input data (i.e. satellite and auxiliary data) to ensure a high temporal stability; these data are temporally extended by operational near-real-time processing – the so-called Interim Climate Data Record. The data record is suitable for various applications, from climate monitoring to renewable energy. The validation of SARAH-3 shows good accuracy (deviations of ∼ 5 W m⁻² from surface reference measurements for monthly surface irradiance), stability of the data record and further improvements over its predecessor SARAH-2.1. One reason for this improved quality is the new treatment of snow-covered surfaces in the algorithm, reducing the misclassification of snow as clouds. The SARAH-3 data record reveals an increase in the surface irradiance (∼ +3 W m⁻² per decade) during recent decades in Europe, in line with surface observations.

Precipitation plays a major role in the energy and water cycles of the earth. Because of its variable nature, consistent observations of global precipitation are challenging. Satellite-based precipitation datasets present an alternative to in situ–based datasets in areas sparsely covered by ground stations. These datasets are a unique tool for model evaluations, but the value of satellite-based precipitation datasets depends on their application and scale. Numerous validation studies considered monthly or daily time scales, while less attention is given to subdaily scales. In this study subdaily satellite-based rainfall data are analyzed in West Africa, a region with strong diurnal variability. Several satellite-based precipitation datasets are validated, including Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), TRMM 3G68 products, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) morphing technique (CMORPH) data. As a reference, highly resolved in situ data from the African Monsoon Multidisciplinary Analysis–Couplage de l’Atmosphere Tropical et du Cycle Hydrologique (AMMA-CATCH) are used. As a result, overall the satellite products capture the diurnal cycles of precipitation and its variability as observed on the ground reasonably well. CMORPH and TMPA data show overall good results. For locally induced convective rainfall in the evening most satellite data show slight delays in peak precipitation of up to 2 h.

Accurate solar surface irradiance (SSI) data are a prerequisite for efficient planning and operation of solar energy systems. Respective data are also essential for climate monitoring and analysis. Satellite-based SSI has grown in importance over the last few decades. However, a retrieval method is needed to relate the measured radiances at the satellite to the solar surface irradiance. In a widespread classical approach, these radiances are used directly to derive the effective cloud albedo (CAL) as basis for the estimation of the solar surface irradiance. This approach was already introduced and discussed in the early 1980s. Various approaches are briefly discussed and analysed, including an overview of open questions and opportunities for improvement. Special emphasis is placed on the reflection of fundamental physical laws and atmospheric measurement techniques. In addition, atmospheric input data and key applications are briefly discussed. It is concluded that the well-established observation-based CAL approach is still an excellent choice for the retrieval of the cloud transmission. The coupling with lookup-table-based clear-sky models enables the estimation of solar surface irradiance with high accuracy and homogeneity. This could explain why, despite its age, the direct CAL approach is still used by key players in energy meteorology and the climate community. For the clear-sky input data, it is recommended to use ECMWF forecast and reanalysis data.