Eugenia Paulescu

Solar irradiance decomposition models are essential for estimating direct normal irradiance and diffuse horizontal irradiance when only global horizontal irradiance measurements are available. While recent studies have increasingly relied on machine learning techniques, analytical models remain attractive because of their transparency, low computational cost, and ease of implementation. This study proposes a rational function model for the decomposition of global horizontal irradiance into its direct and diffuse components. The model parameters were calibrated using four years of high-quality measurements from four stations of the Baseline Surface Radiation Network (BSRN) representing different climatic conditions. The resulting parameterization combines mathematical simplicity with the ability to reproduce the nonlinear dependence of irradiance components on atmospheric conditions. The model was subsequently validated using independent BSRN datasets not involved in the calibration stage. Its performance was assessed through standard statistical indicators and compared with results reported in the recent literature, including both classical analytical decomposition models and machine-learning approaches. The results demonstrate that carefully calibrated analytical formulations remain a viable alternative to more complex data-driven methods. The proposed rational model provides an effective compromise between accuracy, interpretability, and computational efficiency, making it suitable for solar energy applications requiring reliable irradiance component estimates.

Achieving a balance between accuracy and computational efficiency in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Given the high computational cost of spectral models, a widely used simplification strategy consists of parameterizing atmospheric spectral transmittances through wavelength-averaging formulations. This study introduces a Clear-Sky Multivariable (CSMV) broadband parametric model derived from the Leckner spectral model for estimating the three components of solar irradiance under clear-sky conditions: direct normal irradiance (DNI), diffuse irradiance (Gd), and global irradiance (G). The model development follows a two-stage procedure. First, discrete broadband transmittances are obtained by applying an independent spectral integration scheme to the transmittances of the source spectral model. In the second stage, these discrete values are fitted with analytical functions expressed solely in terms of atmospheric state parameters, yielding wavelength-independent broadband formulations. While the overall development framework follows a classical parameterization approach, the calculation of the diffuse component introduces a novel way of estimating the fraction of aerosol scattering directed toward the ground. The model was tested against data collected from eight radiometric stations distributed across six continents and benchmarked against two well-established reference models. Overall, the results indicate a high level of accuracy and demonstrate the practical applicability of the model.

Balancing accuracy and accessibility in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Since spectral models require computationally intensive spectral calculations, a widely adopted simplification strategy is to parameterize atmospheric spectral transmittances using various wavelength-averaging formulations. This work introduces a broadband parametric model derived from a spectral model that accurately estimates the three components of solar irradiance, direct normal, diffuse, and global under clear-sky conditions. The procedure used to develop the model is structured in two stages. Initially, discrete broadband transmittances are obtained by applying an independent integration scheme to the spectral transmittances provided by the source spectral model. The second stage involves fitting these results to obtain continuous broadband atmospheric transmittances, expressed as analytical functions depending solely on atmospheric state parameters and remaining independent of wavelength. The model development procedure is relatively classical; however, the calculation of the diffuse component introduces a new approach for estimating the fraction of aerosol scattering directedtoward the ground. The model was tested against data collected fromeight radiometric stations distributed across six continents andbenchmarked against two well-established reference models. Overall, theresults indicate a high level of accuracy and demonstrate the practical applicability of the model.

Photovoltaic (PV) power forecasting has become a key tool for the intelligent management of electrical grids. Since the largest source of error in PV power forecasting originates from uncertainties in solar irradiance prediction, improving the accuracy of solar irradiance forecasts has emerged as an active research topic. This study evaluates multiple random tree-based model versions using a challenging dataset collected at globally distributed stations, spanning elevations from sea level to nearly 4000 m and covering a wide range of climate classes. The originality of the study lies in the synergistic contribution of two elements: the innovative inclusion of diffuse irradiance among the predictors and a comparative analysis of forecast quality across lowland and mountainous locations. In such environments, accurate solar resource forecasting is particularly important for the intelligent management of stand-alone PV systems deployed at high altitudes and in remote, off-grid areas. Overall, the results identify Extremely Randomized Trees (XTRc) as the best-performing model. XTRc achieves Skill Scores ranging from 0.087 to 0.298 across individual stations. The model accuracy remains high even at mountain stations, provided that sky-condition variability is low.

Most studies have focused on extending the applicability of the Ångström–Prescott equation and improving its accuracy in estimating solar irradiation. Only a limited number of studies have addressed atmospheric climatology using the Ångström–Prescott equation. In contrast, this study reformulates the Ångström–Prescott equation to explore its potential for extracting long-term atmospheric transparency information from radiometric measurements. It introduces a new annual formulation of the Ångström–Prescott equation derived from its common monthly version. While the formal structure is preserved, the equation shifts from its usual role, as a solar irradiation estimator, to a new role, as a predictor of long-term atmospheric transparency. The revised equation naturally defines an annual effective sunshine duration, which assigns greater weight to relative sunshine during summer months than during winter months. To enable prediction, the revised Ångström–Prescott equation is combined with Gaussian process regression. The equation provides the historical annual time series, while Gaussian process regression predicts future values and quantifies their associated uncertainty. To demonstrate the predictive capability of the method, it is applied to the analysis and prediction of four annual parameters characterizing atmospheric transparency: mean clear-sky atmospheric transparency, mean cloud transmittance, mean atmospheric transparency, and effective relative sunshine duration. The analysis is conducted using radiometric data collected at 14 stations distributed across Europe. Predictions for the upcoming decade (2024–2033) indicate that, at most stations, mean atmospheric transparency is expected to remain stable or change within approximate margins of −5% to +10%.

In the operation stage of a photovoltaic (PV) power plant, the output power often differs from the expected value. This deviation is sometimes caused by the so-called spectral mismatch, i.e. the difference between the actual solar radiation spectrum and the standard AM1.5G spectrum, under which the efficiency of a PV module is measured. Spectral mismatch appears as a hidden source of uncertainty in PV production, since for the same measured value of solar irradiance, the output power fluctuates depending on the atmospheric parameters. In this study, the magnitude of the uncertainty induced by the spectral mismatch is assessed. The study was conducted following an innovative methodology based on rigorous simulations of solar spectrum in real geometric-frames and various atmospheric conditions (with a focus on aerosols). The analysis is performed in terms of the familiar spectral factor. Furthermore, the influence of spectral mismatch on PV efficiency is evaluated. Overall findings indicate that under certain conditions spectral mismatch causes a gain or loss in PV efficiency within a rough margin of 10%. Marginal loss can be recorded even under mild conditions: small tilt angle, the Sun close to the horizon and an atmosphere moderately loaded with fine absorbing aerosol.

This study is devoted to investigating the effect of aerosols on solar UV radiation. In the ultraviolet range, scattering processes are dominant and lead to a substantial contribution of diffuse UV radiation to the global UV irradiance. The paper introduces a method for estimating solar UV Index. The proposed method is first compared with other UV Index estimation methods and is subsequently applied to examine the influence of aerosols and ozone on solar UV radiation and on its diffuse component. Human skin exposure to diffuse solar UV radiation can be potentially harmful to health.

This study is primarily devoted to investigating the effect of aerosols on solar UV radiation. In the ultraviolet range, scattering processes are dominant and lead to a substantial contribution of diffuse UV radiation to the global UV irradiance. The paper introduces a method for estimating solar UV radiation through the UV Index. The proposed technique is first compared with other UV Index estimation methods and subsequently applied to examine the influence of aerosols and ozone on solar UV radiation and on its diffuse component. The exposure of human skin also to diffuse solar UV radiation can be potentially harmful for health.