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This study introduces a new method for nowcasting UV Index maps developed within the framework of the Austrian Solar UV Measurement Network. While we focus on the DACH region (Germany, Austria, and Switzerland) in this study, the same methods are routinely applied to nowcast UV Index maps for Europe. The primary objective is to improve public health measures by providing timely and area-wide UV Index values. The UV Index maps are based on clear-sky calculations using data from the Copernicus Atmosphere Monitoring Service. Cloud effects are integrated using cloud modification factors determined from Meteosat Second Generation satellite imagery. To assess the representativeness of the calculated UV Index maps, the corresponding pixel values are compared to ground-based measurements for the year 2022 at 27 locations in the DACH region. For all sky conditions, the satellite-derived UV Index values are within ±1.0 UV Index of the ground-measured UV Index for at least 91% of the data at stations below 500 m a.s.l. and in flatter landscapes. For high-altitude sites and in more pronounced topographies, the values for U1.0 decrease, with the lowest agreement of 74.8% found for the Sonnblick station located at 3109 m a.s.l. Discrepancies arise due to differences in the measurement methods: ground-based measurements capture the local conditions, while satellite-derived values represent the average values over larger areas. The clear-sky deviations are most pronounced at high-altitude, snow-covered sites due to uncertainties in the surface albedo. Under all sky conditions, cloud variability adds further uncertainties, particularly in complex terrain or broken cloud cover scenarios, where satellite cloud data lack the resolution to capture local fluctuations. This study discusses these uncertainties while also highlighting the potential of the generated UV Index maps to provide area-wide information to the population as a valuable complement to ground-based measurements.

The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient (R2) of 0.99. Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to −2% and 9%, respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded R2=0.95, with an MBE of around 2% and an SD of approximately 26%. Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model’s accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to 0.99, and the MBE and SD are about 3% and 11%, respectively, when the CNN-CMF model is used to estimate daily SW irradiation values.

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective.

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.

Solar blue-violet wavelengths (380−455 nm) are at the high energy end of the visible spectrum; referred to as “high energy visible” (HEV). Both chronic and acute exposure to these wavelengths has been often highlighted as a cause for concern with respect to ocular health. The sun is the source of HEV which reaches the Earth’s surface either directly or after scattering by the atmosphere and clouds. This research has investigated the effect of clouds on HEV for low solar elevation (solar zenith angles between 60° and 80°), simulating time periods when the opportunity for ocular exposure in global populations with office jobs is high during the early morning and late afternoon. The enhancement of “bluing” of the sky due to the influence of clouds was found to increase significantly with the amount of cloud. A method is presented for calculating HEV irradiance at sub-tropical latitudes from the more commonly measured global solar radiation (300–3000 nm) for all cases when clouds do and do not obscure the sun. The method; when applied to global solar radiation data correlates well with measured HEV within the solar zenith angle range 60° and 80° (R2 = 0.82; mean bias error (MBE) = −1.62%, mean absolute bias error (MABE) = 10.3% and root mean square error (RMSE) = 14.6%). The technique can be used to develop repeatable HEV hazard evaluations for human ocular health applications

Scattering of solar radiation by clouds can reduce or enhance solar global irradiance compared to cloudless-sky irradiance at the Earth’s surface. Cloud effects to global irradiance can be described by Cloud Modification Factors (CMF). Depending on strength and duration, irradiance enhancements affect the energy balance of the surface and gain of solar power for electric energy generation. In the ultraviolet region, they increase the risk for damage to living organisms. Wavelength-dependent CMFs have been shown to reach 1.5 even in the UV-B region at low altitudes. Ground-based solar radiation measurements in the high Andes region at altitudes up to 5917 m a.s.l showed cloud-induced irradiance enhancements. While UV-A enhancements were explained by cloud scattering, both radiation scattering from clouds and Negative Ozone Anomalies (NOA) have been discussed to have caused short-time enhancement of UV-B irradiance. Based on scenarios using published CMF and additional spectroradiometric measurements at a low-altitude site, the contribution of cloud scattering to the UV-B irradiance enhancement in the Andes region has been estimated. The range of UV index estimates converted from measured UV-B and UV-A irradiance and modeled cloudless-sky ratios UV-B/erythemal UV is compatible with an earlier estimate of an extreme UV index value of 43 derived for the high Andes.

The long‐term UV climatology and trend analysis in the COST‐Action 726 requires daily solar cloud modification factors (SOL‐CMFs) as input of algorithms, transforming them into UV‐CMFs. A CMF is the ratio of all‐sky to clear‐sky downwelling irradiation. A complete spatial and temporal coverage is achieved by calculating daily SOL‐CMFs on the 1° × 1° COST‐726 grid (31°N to 80°N, 25°W to 35°E) using the ERA‐40 shortwave net all‐sky and clear‐sky irradiation. Known deficiencies in ERA‐40 SOL‐CMFs (especially the bias due to clouds) are corrected using SOL‐CMFs derived from pyranometer observations. These are determined based on measured daily sums of solar global irradiation from up to 152 European sites from several data sources. An analysis of clear‐sky days during 1981–1993 and a comparison with results from high‐quality data enabled the selection of appropriate sites in the Mediterranean and southeast Europe from data that has not been classified as that of the group of best quality. For these sites and the period 1964–1980, a homogenization is performed. A cross‐validation of all daily SOL‐CMFs from observations is performed prior to their gridding. Gridding uses ordinary Kriging. Bias‐corrected ERA‐40 and SOL‐CMFs from observations are merged using a distance‐dependent weight derived from overall structural analysis. The COST‐726 database offers daily SOL‐CMF fields of complete spatial coverage from 1958 to 2002. They are unbiased and of known quality. The aerosol direct radiative effect included in the SOL‐CMFs from observations is retained and accounts for long‐term aerosol trends in agreement with the trends of dimming and brightening.

The present study describes the effect of clouds (macro-physical parameters) on global solar radiation (G). Data from four years of hourly measurements of G on a horizontal surface were used. These data were collected at the South Valley University (SVU) meteorological research station (26.2°N, 32.7°E, 96 m above mean see level. In addition, the cloud modification factor for G (CMF G ) was estimated in three cases: high-level, mid-level, and low-level clouds. For every level, the variation of hourly CMF G as a function of cloud amount (CA) was illustrated. A third-order polynomial between hourly values of CMF G and CA was established. Furthermore, the effect of CA in the attenuation of G relative to its corresponding value in cloudless conditions is discussed. For cloud cover > 88%, G was reduced by 54%, 34%, and 28% by low-, mid-, and high-level clouds, respectively.

This book goes to the heart of the unfolding reality of the twenty-first century: international efforts to reduce greenhouse gas emissions have all failed, and before the end of the century Earth is projected to be warmer than it has been for 15 million years. The question \"can the crisis be avoided?\" has been superseded by a more frightening one, \"what can be done to prevent the devastation of the living world?\" And the disturbing answer, now under wide discussion both within and outside the scientific community, is to seize control of the very climate of the Earth itself. Clive Hamilton begins by exploring the range of technologies now being developed in the field of geoengineering--the intentional, enduring, large-scale manipulation of Earth's climate system. He lays out the arguments for and against climate engineering, and reveals the extent of vested interests linking researchers, venture capitalists, and corporations. He then examines what it means for human beings to be making plans to control the planet's atmosphere, probes the uneasiness we feel with the notion of exercising technological mastery over nature, and challenges the ways we think about ourselves and our place in the natural world.

Aggregation is a key microphysical process for the formation of precipitable ice particles. Its theoretical description involves many parameters and dependencies among different variables that are either insufficiently understood or difficult to accurately represent in bulk microphysics schemes. Previous studies have demonstrated the valuable information content of multi-frequency Doppler radar observations to characterize aggregation with respect to environmental parameters such as temperature. Comparisons with model simulations can reveal discrepancies, but the main challenge is to identify the most critical parameters in the aggregation parameterization, which can then be improved by using the observations as constraints. In this study, we systematically investigate the sensitivity of physical variables, such as number and mass density, as well as the forward-simulated multi-frequency and Doppler radar observables, to different parameters in a two-moment microphysics scheme. Our approach includes modifying key aggregation parameters such as the sticking efficiency or the shape of the size distribution. We also revise and test the impact of changing functional relationships (e.g., the terminal velocity–size relation) and underlying assumptions (e.g., the definition of the aggregation kernel). We test the sensitivity of the various components first in a single-column “snowshaft” model, which allows fast and efficient identification of the parameter combination optimally matching the observations. We find that particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while the sticking efficiency and the cloud ice habit have less influence. The setting which optimally matches the observations is then implemented in a 3D model using the identical scheme setup. Rerunning the 3D model with the new scheme setup for a multi-week period revealed that the large overestimation of aggregate size and terminal velocity in the model could be substantially reduced. The method presented is expected to be applicable to constrain other ice microphysical processes or to evaluate and improve other schemes.