Global Solar Radiation Research Articles

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

This article delves into the seasonal variation of solar radiation patterns across Nigeria’s four geo-political zones, exploring their complex, scale-dependent behaviors. By employing chaotic quantifiers, the study characterizes irregular and self-similar patterns, enhancing our understanding of solar radiation variability and heterogeneity. The research uniquely fits meteorological parameters onto a global solar radiation model and focuses on the underexplored nonlinear aspects within tropical regions, specifically in the Nigerian context. Utilizing daily data from ERA INTERIM satellite archives for representative stations, the study employs nonlinear time series analysis methods like recurrence plots, recurrence qualitative analysis, and multifractal spectral analysis to comprehensively explore the unpredictable behaviors observed. The quantifier spectrums play a key role in revealing intricate scaling behaviors and correlation structures among environmental variables, shedding light on patterns often concealed by conventional statistical methods. For instance, we found that solar radiation variability in the northern region increases more significantly during the dry season compared to the wet season, unlike in the southern region. Additionally, the multifractal spectral analysis revealed a higher degree of complexity in solar radiation patterns during transition periods between seasons. The findings reveal a low recurrence quantitative analysis, long right tail, and truncations at both ends of the spectrum. This suggests instability in solar radiation across different seasons and locations. Nonetheless, the results also demonstrate that solar radiation is consistently available throughout the year, which is typical of tropical regions.

Doped SnO2 thin films fabricated at low temperature by atomic layer deposition with a precise incorporation of niobium atoms

Nb-doped SnO2 (NTO) thin films were synthesized by atomic layer deposition technique at low temperature (100 °C). For an efficient incorporation of the Nb atoms, i.e. fine control of their amount and distribution, various supercycle ratios and precursor pulse sequences were explored. The thin film growth process studied by in-situ QCM revealed that the Nb incorporation is highly impacted by the surface nature as well as the amount of species available at the surface. This was confirmed by the actual concentration of the Nb atom incorporated inside the thin film as determined by XPS. Highly transparent thin films which transmit more than 95% of the AM1.5 global solar irradiance over a wide spectral range (300–1000 nm) were obtained. In addition, the Nb atoms influenced the optical band gap, conduction band, and valence band levels. While SnO2 thin film were too resistive, films tuned to conductive nature upon Nb incorporation with controlled concentration. Optimal incorporation level was found to be ⩽1 at.% of Nb, and carrier concentration reached up 2.5 × 1018 cm−3 for the as-deposited thin films. As a result, the high optical transparency accompanied with tuned electrical property of NTO thin films fabricated by ALD at low temperature paves the way for their integration into temperature-sensitive, nanostructured optoelectrical devices.

Solar global irradiance from actinometric degree data for Filaret Observatory (Bucharest), 1892–1903

Long-term series of solar irradiation measured at ground level are not available in the old times. However, long-term series of actinometric degree data obtained by using the Arago-Davy instrument have been recorded in the second half of the 19th century in several locations of the world. We have developed recently a method to estimate global solar irradiance on horizontal surface from actinometric degree data (doi.org/10.1007/s00704-023-04485-2). This opens a way of finding proxy information about the incident solar irradiance on various areas of the globe before the 20th century. Hourly actinometric degree data for the years 1892–1903 are available at the Filaret Observatory (Bucharest, Romania, South-Eastern Europe). The observed series have a systematic decreasing tendency, which has been removed by using a correction procedure. The proposed method is used here to evaluate solar global irradiance at Filaret Observatory. Solar irradiance data provided by the Twentieth Century Reanalysis Project version 3 (20CRv3) are used as a reference. The expected hourly and daily average solar irradiance values show reasonable qualitative consistency with the 20CRv3 data. This consistency is notably stronger during the warmer months from April to September. Much better agreement is found for the monthly averaged solar global irradiance values. At this level, the proposed method and the procedure of the 20CRv3 project seem to validate each other.

Photosynthetically Active Radiation Temperature Dependent Models and Its Variability with Climatic Variables over Akure, Nigeria

Photosynthetically Active Radiation (PAR) is an essential input for applications pertaining to biomass production, plant physiology, and natural lighting in greenhouses. It is the amount of light energy necessary for photosynthesis to occur, and the wavelengths of this light are typically between 400 and 700 nm. In this study, the photosynthetically active radiation for Akure (latitude 7.28°N, longitude 5.30°E, and 375 m asl.), located in the coastal region of Nigeria, was estimated and investigated using data on the monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature from the National Aeronautics and Space Administration (NASA) archives over a thirty-eight-year period (1984–2021). In order to determine the accuracy of the models, eight (8) new temperature PAR-based models were developed for the location and statistical tests were conducted using the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), t-test, and index of agreement (IA). The findings demonstrated that PAR for Akure is lowest in the month of August with 6.5320 MJm-2day-1 during the rainy season (April to October) and highest during the dry season (November to March), with 8.6670 MJm-2day-1 in March. The best model equation for calculating PAR for Akure was found to be the one that links the PAR and natural logarithm of temperature changes; its R2, MBE, RMSE, MPE, t-test, and IA values were 68.1%, 0.0032 MJm-2day-1, 0.5208 MJm-2day-1, -0.4479%, 0.0204, and 82.8013%, respectively.

Prediction of Global Solar Radiation across the Region of Northern Sudan

Available data for solar resources indicates that northern Sudan has solar potentials among the highest in world. However, in this vast area (about 471×103 km2) only two stations, at Hudeiba and Dongola, have records for global solar radiation. Four other stations (at Atbara, Karima, Abu-Hamed and Wadi-Halfa) in this region have records for sunshine duration. In this study, recorded data at Hudeiba and Dongola stations have been used to develop 15 models for estimation of monthly average daily global solar radiation on a horizontal surface in northern Sudan. The developed models have been extensively evaluated by using 14 statistical indicators. All developed models revealed good performance (percentage errors within ±4.5%). Then, the most accurate regional model (Model M3) has been used to predict the global solar radiation at Atbara, Karima, Abu-Hamed and Wadi-Halfa stations. Results showed that the whole region receives abundant solar energy throughout the year. The yearly average of daily global solar radiation over the entire region is 23.16 MJ/m2. Furthermore, the developed models can be used to predict global radiation at adjacent places that lack such data.

Short-Term Forecasting of Photovoltaic Power Using Multilayer Perceptron Neural Network, Convolutional Neural Network, and k-Nearest Neighbors’ Algorithms

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers’ favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power.

Empirical models and artificial intelligence for estimating hourly diffuse solar radiation in the state of Alagoas, Northeastern Brazil

Diffuse solar irradiation (HD) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, HD observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (HDh) and global solar irradiation (HGh) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating KDh(=HDh/HGh) (r2 > 0.726, modified Willmott – dm > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating KDh for hourly atmospheric transmittance (KTh) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, dm > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, dm > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating KDh superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for HD estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation.

Evaluation of a grid-connected PV power plant: performance and agrivoltaic aspects

AbstractThe performance ratio, a globally recognized metric that correlates with reported global solar radiation values, serves as a crucial indicator for evaluating the efficiency of grid-connected PV plants. Also, a large scale PV power plant alone can afford some agricultural irrigation energy requirement of a region. In this study, the actual generation data from a power plant located in Bursa province in northwestern Türkiye, during its initial six years of operation have been analyzed. The analysis reveals that the annual electricity production of the power station reaches approximately 10 GWh. Notably, the time period between April and September witnesses a monthly electricity generation exceeding 1 GWh, with September emerging as the most productive month, characterized by an average performance ratio of 94.5% during this six-year period. However, over the span of six years, the highest average electricity generation occurs in July, peaking at 1.34 GWh. Also, the power plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%. From an environmental impact and global warming perspective, it is noteworthy that during the 36-month period in the summer season, the performance ratio exceeded 100% only three times. However, within the 32-month period in the winter season, the performance ratio exceeded 100% 19 times. This situation indicates that while the reported radiation rates by the managements are consistent with the actual values for the summer months, they need to be revised, especially for the winter months.

Temporal Evolution, Oscillation and Coherence Characteristics Analysis of Global Solar Radiation Distribution in Major Cities in China’s Solar-Energy-Available Region Based on Continuous Wavelet Transform

The majority of the energy required for human survival is derived either directly or indirectly from solar radiation, thus it is important to investigate the periodic fluctuations in global solar radiation over time. In this study, six cities—Harbin, Shenyang, Beijing, Shanghai, Wuhan, and Guangzhou—located in the utilizable areas of solar energy in China, were selected, and the periodicity of the daily global solar radiation of these six cities with time were investigated by means of wavelet power spectrum analysis. Furthermore, Harbin, which has the lowest monthly average of global solar radiation in the cold of winter, was selected to explore the temporal evolution relationship between global solar radiation and other meteorological factors, and air quality factors by wavelet coherence analysis. Among the meteorological factors, the correlation between global solar radiation and sunshine duration exhibits the highest level of consistency. Global solar radiation demonstrates a positive association with atmospheric temperature/wind speed/precipitation over the annual cycle. Conversely, it displays a negative correlation with atmospheric pressure during this time frame. Additionally, on shorter timescales, global solar radiation is negatively correlated with air humidity and precipitation. Among air quality factors, the relationship between global solar radiation and CO/NO2/O3/PM2.5/PM10/SO2 primarily manifests in the broader annual cycle time scale. Within this context, global solar radiation shows a positive correlation with O3, while displaying negative associations with the other five air quality indicators. These findings contribute to urban environmental planning and the effective management and utilization of solar radiation, thereby providing valuable insights to guide the future development of cross-regional comprehensive energy utilization projects under diverse climatic and geographical conditions. Additionally, they serve as a reference for subsequent research aimed at enhancing the accuracy of global solar radiation prediction models.

Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye

Today’s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

An experimental study of the effects of using cool materials at different building heights on the indoor and outdoor environment

Four building arrays, each comprising 25 building models, were utilized to investigate the effects of cool materials on building surface temperatures, indoor temperatures, and street microclimates in Guangzhou, China. The experiments are divided into three groups. The first group of the experimental models is used to investigate the cooling effect of the roof using cool materials on the roof surface and indoor environment. The results show that using cool materials on roofs can significantly reduce roof surface and indoor temperatures of the top floor by 13.5 °C and 3.9 °C at noon under an average global solar radiation (GSR) of 727 W/m2. The second group of experiments included four arrays: one control group without cool materials and three other arrays applying cool materials on the third, second, and first floors, respectively. Using cool materials on the façades of the third, second, and first floors reduced the indoor temperature by an average of 1.9 °C, 0.8 °C, and 0.5 °C (average GSR = 404 W/m2), respectively. As a result, higher floors receive more solar radiation, and cool materials show better cooling capacity. The third group is used to investigate the effect on street air temperatures when cool materials are used on all building façades. Overall, the effect of cool materials on the street air temperature is very small, with a maximum reduction of only 0.2 °C in the street air temperature.

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.)

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years’ data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations—Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

Assessing the Effectiveness of Reflective and Diffusive Polyethylene Films as Greenhouse Covers in Arid Environments

The application of diffusive and reflective polyethylene (PE) films as greenhouse coverings in arid climates presents an opportunity to improve the microclimate of the greenhouse and achieve consistent light distribution within the crop canopy. Nevertheless, there is still a lack of understanding regarding the properties of these covers and their impact on the microclimate and the growth parameters of crops. This study aimed to assess the impact of different covers on the diffusion of beam radiation during transmission, microclimatic parameters, and growth parameters of cucumbers in each of the greenhouses they covered. In the study, three PE covers were evaluated: a reflective cover (RC), a diffusive film (DC), and a locally produced cover (LPC) as the control treatment. The covers were installed on three identical, single-span, evaporatively cooled greenhouses named GH1/LPC, GH2/RC, and GH3/DC, which were utilized for cultivating cucumber crops. The results indicated that the diffusive nature of the tested films increased the ratio of diffuse to global solar radiation (D/G) from 0.22 outside the greenhouses to 0.49, 0.42, and 0.41 inside GH1/LPC, GH3/DC, and GH2/RC, respectively. Similarly, the ratio of diffuse to direct beam radiation (D/B) showed an increase, with values of 0.95, 0.70, and 0.68 inside GH1/LPC, GH3/DC, and GH2/RC, respectively, compared to the outside value of 0.28. The DC used in GH3 showed a favorable microclimate by reducing the air temperature and improving the relative humidity. Accordingly, the vegetative growth of the cucumbers was significantly improved in GH3/DC, reflected in increases in their biomass, followed by GH2/RC and GH1/LPC. The highest crop yield (p ≤ 0.05) of 12.3 kg/m2 was achieved in GH3/DC, followed by 10.2 kg/m2 in GH2/RC and 10.1 kg/m2 in GH1/LPC. Interestingly, the LPC not only stood out as a low-cost option but also displayed excellent diffusive–radiative properties, and demonstrated reasonable growth development and productivity for the cucumber crops. Consequently, the LPC emerges as a practical and cost-effective greenhouse covering material for crop production in arid climates.

Ratio of photosynthetically active radiation to global solar radiation above forest canopy in complex terrain: measurements and analyses based on Qingyuan Ker Towers

BackgroundUnderstanding of the ratio of photosynthetic photon flux density (Qp) to global solar radiation (Rs) (Qp/Rs) is crucial for applying Rs to ecology-related studies. Previous studies reported Qp/Rs and its variations based on measurements from a single observatory tower, instead of multi-site-based measurements over complex terrains. This may neglect spatial heterogeneity in the terrain, creating a gap in an understanding of how terrain affects Qp/Rs and how this effect interacts with meteorological factors.MethodsHere the Qingyuan Ker Towers (three towers in a valley with different terrains: T1, T2, and T3) were utilized to measure Qp and Rs over mountainous forests of Northeast China. An airborne LiDAR system was used to generate a digital elevation model, and sky view factor of sectors (SVFs) divided from the field of view of tower’s pyranometer was calculated as a topographic factor to explain the variations of Qp/Rs.ResultsThe results identified significant differences in Qp/Rs of the three towers at both daily and half-hour scales, with larger differences on clear days than on overcast days. Qp/Rs was positively correlated with SVFs of T1 and T3, while this correlation was negative with that of T2. The effect of SVFs on Qp/Rs interacted with clearness index, water vapor pressure and solar zenith angle. Random forest-based importance assessment demonstrated that explanation (R2) on Qp/Rs was improved when SVFs was included in the predictor variable set, indicating that incorporating terrain effects enhances the prediction accuracy of Qp/Rs. The improvement in the R2 values was more pronounced on clear days than on overcast days, suggesting that the effect of terrain on Qp/Rs depended on sky conditions.ConclusionsAll findings suggested that Qp/Rs is affected by terrain, and integrating terrain information into existing Qp/Rs models is a feasible solution to improve Qp/Rs estimates in mountainous areas.

I-V response test of 60–150 W mono-crystalline solar panel

This work investigates the discrepancies in electrical parameters of mono-crystalline solar panels between Ago-Iwoye weather conditions and the manufacturer’s specified ideal conditions. Manufacturer’s specifications are typically based on 1,000 W/m2 global solar irradiance, AM 1.5, and 25°C operating temperature, while actual weather conditions at installation sites can vary significantly. Mono-crystalline (single-crystal) silicon solar panels of capacities 60, 80, 100, and 150 W were evaluated through current-voltage (I-V) response tests at an installation site in Ago-Iwoye, Nigeria, with solar irradiance exposure from 11 a.m. to 3 p.m. The analysis of I-V and P-V curves revealed a significant reduction in maximum power output by 28.6%, 25.9%, 28.9%, and 19.36%, respectively, compared to the manufacturer’s stated values. This deviation underscores the importance of considering local weather conditions during solar PV projects, and we recommend adding an additional 20%–30% of the total solar panel capacity during installations to account for variations in solar irradiance and operating temperatures, ensuring optimal performance and effective solar power generation in Ago-Iwoye and similar areas.

Examining UV Radiation Patterns in Relation to Particulate Matter and Atmospheric Conditions in Arid, Unclouded Skies

This study investigates the influences of air temperature (T), relative humidity (RH), wind speed (WS), clearance index (KT), particulate matter (PM) concentrations (PM10 and PM2.5), and aerosol optical depth (AOD) on ultraviolet (UV) radiation during clear skies in Riyadh, central Saudi Arabia. The observational dataset utilized in this study comprises global solar radiation (G), UVA radiation, AOD measurements, and PM concentrations. The data were collected from 2014 to 2015 at the King Abdulaziz City for Science and Technology (KACST) campus in Riyadh. Regression analyses were conducted to investigate the relationships between UV radiation and the considered variables. The methodology is based on the least square method and associated statistical tests. The findings of this study provide valuable insights into the impacts of meteorological variables and aerosols on UV radiation, contributing to the understanding of environmental and industrial applications in the Arabian Peninsula. The analyses showed that the strengths and magnitudes differed from one variable to another. No significant correlations between UVA radiation (315–400 nm) and hourly and daily values of PM2.5 were found. Moreover, no significant correlations were seen between daily values of the UVA radiation and RH and between the UVB (280–315 nm) and PM10. The rest of the correlations (between UV radiation and the PM10 and meteorological variables) were found to be significant. While WS, the ratio of the PM concentrations (PM2.5/PM10), KT, and T exhibited positive correlations with UV radiation, the rest of the variables had anti-correlations with UV radiation. The influences of T, WS, and RH on ambient PM concentrations during the considered period were taken into account, and it was found that the PM concentrations correlate, with different magnitudes and strengths, positively with T and negatively with RH and WS.

Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional downscaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2, and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation systems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting.

Correction to: Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Correction to: Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Visualization of Solar Radiation Using Three-Dimensional Computer Graphics Technologies

Currently, the prediction, calculation and rationing of solar radiation is a serious problem that has a wide impact on the spheres of human activity. Creating and maintaining a comfortable living environment, as well as solving the world's energy problems, are the fundamental criteria that determine its relevance. This article discusses the possibility of using three-dimensional graphics technologies to calculate insolation. The paper considers an algorithm for calculating the four main components of the visualization of global solar radiation: direct beam radiation, diffuse radiation, reflected beam radiation and reflected diffuse radiation. The freely distributed open source software Blender 3D is used as a tool for working with computer graphics. In the course of the study, calculations based on the principle of bidirectional path tracing, which is used in the visualization algorithms of the Cycles graphics engine, are presented. The use of the technology of transferring complex data to the raster image format allows you to create a separate texture map for each component of the simulated lighting. Based on the obtained texture maps, it is possible to calculate the global solar radiation for all created or imported 3D models in the three-dimensional space of Blender 3D.

Sedimentary provenance and paleoenvironmental evolution since the Last Glacial Maximum off the Xisha Platform, northern South China Sea

The complexity of retrieving paleoenvironmental variability from hemipelagic sedimentary archives in the marginal seas has been exacerbated by intricate interactions between sedimentary sources and climatic fluctuations, modulated by sea-level shifts and oceanic currents. These challenges are particularly pronounced in the northern South China Sea (SCS). This study introduces new data, incorporating SrNd isotopic, clay minerals, grain-size, and calcareous nannofossil proxies, to assess the sedimentary provenance and paleoenvironmental changes in the northwestern SCS since the Last Glacial Maximum. The sedimentary provenance highlights the primary source from the northern SCS, particularly the Red River drainage system, with potential contributions from the Indochina drainage systems. The combined analysis of multiple proxies suggests that sedimentary processes and biotic activity in the examined core were mainly influenced by sea-level fluctuations, concerning global ice dynamics. Variations of grain sizes and kaolinite / (illite + chlorite) (K/(I + C)) ratio largely correspond to the terrestrial flux variation from northern SCS associated with sea-level changes during glacial-interglacial cycles. Furthermore, the smectite / (illite + chlorite) (S/(I + C)) ratio in this study appears to have been primarily influenced by the activity of the Kuroshio current in the northern SCS. The possible interference of intensified northeastward warm currents during relatively warm intervals caused the depletion of the S/(I +C) ratio and was responsible for the depletion of soluble calcareous nannofossils. Additionally, this study deduces that sedimentary processes and biotic activity in the northern SCS, in response to mainly sea-level changes and surface currents, were largely controlled by global ice dynamics and solar radiation. This study also underscores the need for further interdisciplinary research, combining various proxies, to enhance our understanding of paleoenvironmental assessments in hemipelagic sediments in marginal seas.