Solar photovoltaics (PV) plays an essential role in decarbonizing the European energy system. However, climate change affects surface solar radiation and will therefore directly influence future PV power generation. We use scenarios from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) for a mitigation (SSP1-2.6) and a fossil-fuel-dependent (SSP5-8.5) pathway in order to quantify climate risk for solar PV in Europe as simulated by the Global Solar Energy Estimator (GSEE). We find that PV potential increases by around 5 % in the mitigation scenario, suggesting a positive feedback loop between climate change mitigation and PV potential. While increased clear-sky radiation and reduced cloud cover go hand in hand in SSP1-2.6, the effect of a decrease in clear-sky radiation is outweighed by a decrease in cloud cover in SSP5-8.5, resulting in an increase in all-sky radiation. Moreover, we find that the seasonal cycle of PV generation changes in most places, as generation grows more strongly in winter than in summer (SSP1-2.6) or increases in summer and declines in winter (SSP5-8.5). We further analyze climate change impacts on the spatial variability of PV power generation. Similar to the effects anticipated for wind energy, we report an increase in the spatial correlations of daily PV production with large inter-model agreement yet relatively small amplitude, implying that PV power balancing between different regions in continental Europe will become more difficult in the future. Thus, based on the most recent climate simulations, this research supports the notion that climate change will only marginally impact renewable energy potential, while changes in the spatiotemporal generation structure are to be expected and should be included in power system design.

Solar energy has found increasing applications in recent years, and the demand will continue to grow as society redirects to a more renewable development path. However, the required high-frequency solar irradiance data are not yet readily available everywhere. There have been endeavors to improve its forecasting in order to facilitate grid integration, such as with photovoltaic power planning. The objective of this study is to develop a hybrid approach to improve the accuracy of solar nowcasting with a lead time of up to one hour. The proposed method utilizes irradiance data from the Copernicus Atmospheric Monitoring Service for four European cities with various cloud conditions. The approach effectively improves the prediction accuracy in all four cities. In the prediction of global horizontal irradiance for Berlin, the reduction in the mean daily error amounts to 2.5 Wh ${\mathrm{m}}^{-2}$ over the period of a month, and the relative monthly improvement reaches nearly 5% compared with the traditional persistence method. Accuracy improvements can also be observed in the other three cities. Furthermore, since the required model inputs of the proposed approach are solar radiation data, which can be conveniently obtained from CAMS, this approach possesses the potential for upscaling at a regional level in response to the needs of the pan-EU energy transition.

We used aerosol data from surface-based AErosol RObotic NETwork (AERONET) and day-ahead aerosol optical depth (AOD) forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) to examine the spatiotemporal variations in AOD at selected sites worldwide. We evaluated three methods for day-ahead AOD forecasting: AERONET 1 d (and 2 d) persistence or monthly mean, along with CAMS forecast. High values of daily mean AOD indicates larger day-to-day variability in AOD and lower predictability. Using radiative transfer modeling, we quantify deviations in forecasts of cloud-free direct normal irradiance (DNI) induced by errors in AOD forecasts. The performance of each AOD forecast method in DNI forecast is assessed and compared. Taking into account the characteristic aerosol types at selected locations, we also draw quantitative implications about the reliability and usability of CAMS AOD forecasts for DNI forecasts as alternatives to AOD forecasts based on approaches using ground-based measurements. For example, CAMS forecasts perform better at more sites than AERONET persistence approaches do, among them many urban-industrial aerosol sites. AERONET persistence forecasts AOD with lower errors at dust aerosol sites. To date, none of the forecast methods for AOD discussed here reliably achieve an accuracy of < 5 % deviation in day-ahead forecasts of direct normal irradiation (daily sum), but most of the sites can expect better DNI forecasts with a threshold of 20 % DNI deviation.

Rooftop solar photovoltaic (PV) systems have the advantage of producing electricity without air pollution and greenhouse gas emissions, at the same time reducing carbon footprint as well as urban heat island effect. This study aims to assess physical, geographical and economic levels of solar energy potentials in actual atmospheric conditions on urban rooftops, using two municipalities in Athens, Greece as an example. We utilize very high-resolution digital surface models for the computation of clear-sky solar irradiance considering surrounding shadows. For all-sky conditions, cloud and aerosol data from 2012 to 2021 are obtained from the Copernicus Atmosphere Monitoring Service radiation service and the ECMWF Atmospheric Composition Reanalysis 4 product, respectively. The goals are to quantify the effect of solar elevation, the shadowing effect from adjacent buildings and constructions, and the effects of clouds and aerosols on the solar radiation availability on the rooftops and to investigate their interconnections. The spatio-temporal resolution of the analyses ranges from individual rooftop to neighborhood scale (approximately 3000 buildings) and from hourly intervals to ten years periods.The results of the solar potential assessment are made available as a web GIS map for potential public access, intended to aid urban planning and encourage widespread adoption of solar energy in the public and private sectors.