Global Radiation Research Articles

On the Shift of Glacier Equilibrium Line Altitude (ELA) under the Changing Climate

Presently available information on the glacier equilibrium line altitude (ELA) is being collected and examined. The historical course of the world’s longest ELA series of 107 years at the Claridenfirn is reviewed together with climatic elements. Further, the changes in ELAs of 70 glaciers the world over are investigated, and a linear plane model for the speed of the ELA shift is proposed as a function of the changing rates of summer temperature and winter mass balance. The four glaciers in Europe, which diverge most from the plane, are investigated in detail. The cause of the divergence is likely due to be the change in solar global radiation. Although a precise quantification of the role of radiation is not possible at this stage for the entire world, the role of solar radiation is investigated for these glaciers. Globally viewed, ten, or 15% of the 70 investigated glaciers, are expected to lose their accumulation areas within the next ten years. Half of all studied glaciers will follow the same fate by the end of this century under the present climatic conditions. If climate change is accelerated, the disappearance of glaciers will occur sooner than presented in this study.

Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.

Early Development and Tuning of a Global Coupled Cloud Resolving Model, and its Fast Response to Increasing CO2

Since the dawn of functioning numerical dynamical atmosphere- and ocean models, their resolution has steadily increased, fed by an exponential growth in computational capabilities. However, because resolution of models is at all times limited by computational power a number of mostly small-scale or micro-scale processes have to be parameterised. Particularly those of atmospheric moist convection and ocean eddies are problematic when scientists seek to interpret output from model experiments. Here we present the first coupled ocean-atmosphere model experiments with sufficient resolution to dispose of moist convection and ocean eddy parameterisations. We describe the early development and discuss the challenges associated with conducting the simulations with a focus on tuning the global mean radiation balance in order to limit drifts. A four-month experiment with quadrupled CO2 is then compared with a ten-member ensemble of low-resolution simulations using MPI-ESM1.2-LR. We find broad similarities of the response, albeit with a more diversified spatial pattern with both stronger and weaker regional warming, as well as a sharpening of precipitation in the inter tropical convergence zone. These early results demonstrate that it is already now possible to learn from such coupled model experiments, even if short by nature.

Quantitative analysis of International Health Regulations Annual Reports to identify global disparities in the preparedness for radiation emergencies

ObjectivesRadiation emergencies are rare but can have minor confined effects to catastrophic consequences across the large geographical territories. Geographical disparities in the preparedness for radiation emergencies can negatively impact public-safety...

A case study of NeuralProphet and nonlinear evaluation for high accuracy prediction in short-term forecasting in PV solar plant

Prediction of the energy, active production from the PV solar plant is a challenge in cloudy weather or with clouds over the solar plant; therefore, it has impact in the planning of the power system, especially in the season analysis and prediction accuracy adjustments, for example in holidays. In 2022, some authors published some analysis associated to horizontal pyranometers and the limits in the evaluation of the data, the Mean Bias Error of Daily Solar Irradiation average (MIEave) ranges from 0.17% to 2.86% associated a sudden change in the weather, it increases the “risk of misestimating the potential electricity generation” with short-term error of more than 50% and the Global Horizontal Irradiance (GHI) has a mean bias error (MBE) of at least ±8% [1]. In this research article, a novel proposal for short-term forecasting combines the satellite with meteorological station data and statistical model associated to the new seasonality analysis by using two approaches: i) NeuralProphet, Ridge regression, ii) Long Short-Term Memory with convolutional neural networks. Besides, it requires three KPI as feedback, it is the mean absolute error (MAE), relative Root mean square error (RMSE), and mean absolute percentage error (MAPE). The results demonstrate a MAPE of 5.93% and a computational time 852.10 s and the comparison with new predictions methods from 2019 to 2021. This research article illustrates the new approach with the forecasting method in a case of the PV solar plant in Peru and proves the robustness and seasonality results, and new short-terms improvements associated to external influence as cloudy conditions and resource availability. Our findings are an improvement of the model MAPE 12.14%–5.93%; even compared with the literature and currently models as ARIMA-LSTM with 10.57%, LSTM with NN and G, SARIMA and SVM considering Gaussian White Noise with 8.14% and Prophet with SVM with 8.81%.

Evaluation of neutron irradiation-induced displacement damage in heat pipe reactor

• Neutron irradiation-induced displacement damage in a 5 MW HPR is evaluated. • 91 maximum DPA is obtained for the SS316 monolith after a reactivity-determined cycle length. • The more than 4% swelling of the monolith leads the refueling to be difficult. • The maximum neutron irradiation dose in the Al 2 O 3 reflector is beyond available experimental data. • Two factors are proposed to convert the fast neutron fluences into DPA for Al 2 O 3 . The present work evaluates the neutron irradiation-induced displacement damage in a 5 MWth Heat Pipe Reactor (HPR) based on the latest released ENDF/B-VIII.0 nuclear reaction data library. The evaluations are performed for the SS316 monolith in the reactor core and the inner surface of the radial Al 2 O 3 reflector, the two individual parts suffering the highest neutron irradiation. At the end of the fuel cycle length, i.e., after 90 years of full-power operation, the global maximum neutron irradiation damage in the SS316 monolith is about 91 Displacements per Atom (DPA). The corresponding more than 4% volume swelling makes the refueling difficult, even though the allowed 200 DPA in other fast neutron reactors implies two potential cycles for the HPR by considering the mechanical behaviors of SS316. In the Al 2 O 3 reflector, the maximum neutron irradiation dose ( Φ E > 0.1 M e V = 3.3 × 10 22 c m - 2 and Φ E > 1 M e V = 6.4 × 10 21 c m - 2 fast neutron fluence) is beyond the available observed data. Further experiments should be performed to study the behavior of Al 2 O 3 in the HPR reflector after more than 63 years of full-power operation. For this purpose, two factors are proposed to convert the fast neutron fluences into DPA with certain specific values for the threshold displacement energy of Al and O in Al 2 O 3 because they are still not well recognized.

Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region

Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data. Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant. The obtained results are compared with the output power generation data of a 350 kW solar power plant. The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE). FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.

Sun-blocking index from sky image to estimate solar irradiance

To optimize the balance between indoor comfort and minimal energy usage, several automated methods have been proposed for smart window control systems. However, owing to the high variability of solar irradiance, it is difficult for window controllers to operate properly. The variability of solar irradiance is mostly caused by cloud motion in the sky. Cloud information can be obtained from human observations, satellite images, and sky images, and parameters such as cloud amount, cloud fraction, and cloud cover (CC) have been conventionally used for considering cloud effects. These conventional parameters focus only on the amount of cloud over the entire sky while failing to account for the amount of cloud locally around the sun disk. Consequently, rapid changes in the cloud at short time scales have not been considered properly, and the high variability of solar irradiance has not been addressed accurately. Therefore, in this study, a new parameter, the sun-blocking index (SBI), is introduced to model the partial blocking of the sun by clouds and hence enhance the estimation accuracy of solar irradiance under fickle weather. In addition, an image processing model to calculate SBI using sky images was proposed. Once the correlation between the clearness index and SBI is established, the SBI-dependent clearness index is applied to solar radiation models. The results showed that the global horizontal irradiance was estimated with a relative root-mean-square error (rRMSE) of 16.21%. Remarkably, when direct normal irradiance (DNI) was estimated, the proposed model outperformed conventional models with rRMSE of less than 28%. • Partial blocking of the sun by cloud is considered for assessing solar irradiance. • The sun-blocking index (SBI) is proposed to measure the partial blocking. • A methodology for calculating the SBI from sky images is developed. • The relationship between SBI and clearness index is investigated. • Solar irradiance is more accurately estimated via the SBI-dependent clearness index.

Use of physics to improve solar forecast: Part II, machine learning and model interpretability

Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy’s Atmospheric Radiation Measurement’s Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.

Çift Yüzlü Fotovoltaik Panellerin Farklı Zemin Koşullarında Performansının İncelenmesi

Photovoltaic (PV) panels are generally used for monofacial applications due to the back surface coating materials. When the coating material is transparent, it is defined as bi-facial PV. In this study, the variable albedo effects on bi-facial PVs in different ground conditions were examined. The results were compared with monofacial PV panels in the same conditions for the Konya region. Bifacial PV panels were analyzed under white, sand, and asphalt ground conditions. Simulations were made by the PVsyst program, and the results were compared by global radiation value, the performance ratio (PR), and the produced energy results. An installed capacity of 54,6 kWp bifacial and monofacial PV panels with a horizontal angle of 35°, azimuth angle of 0°, and 6m intervals for roof installation is considered. It has been observed that the yearly total solar radiation value of 1969 kWh/m² occurs on the monofacial PV, which is higher as 6,4% for the white ground, 2,4% for the sand ground, and 0,8% for the asphalt ground conditions. The annual energy generated in the Konya region is calculated as 91,197 MWh, 94,404 MWh, and 97,730 MWh for asphalt, sand, and white ground conditions. It was only 105,690 MWh for monofacial PV panels. It has been determined that the performance ratio of the system in June, which is one of the months of the highest radiation occurred, 7,0% higher than the sand ground conditions, 10,4% compared to the asphalt ground, and 14,5% higher than the monofacial photovoltaic system. It was evaluated that the ground conditions of the bi-facial panels contributed significantly to the panel efficiency and performance ratio and could be applied with a small investment cost compared to the project's total cost.

20% of uncertainty in yield estimates could be caused by the radiation source.

Solar radiation is the energy for all biological, physical, and chemical processes of the earth's surface system, and affects the growth and development of crops at all stages. But the diverse data sources and fusion algorithms lead to large differences in the radiation values in various climate datasets. Accurate estimates of the radiation data is not an easy task, the uncertainty of which and the impact on crop yield simulation remains unknown. In this study, the total solar radiation amounts from four independent global radiation datasets were shown considerable heterogeneity across regions and cropping seasons. Forcing the dynamic crop models with the four radiation inputs produced similarly great uncertainties of simulated yield in most regions, with the greatest uncertainty up to 30% of average yield for wheat in Europe. The global-scale uncertainty of simulated yield is increasing during the past three decades and would reach up to 20% of its averages in the future, equivalent to 300 million tons when converting to the global crop production. The results of this study suggest that the previously projected crop yield changes with climate change have large uncertainties propagated from solar radiation data sources used for projections. These uncertainties may mislead the assessment of future food security.

Study of Four (4) Semi-Empirical Models for Estimating Direct Radiation from the Sun and Modeling for Application to the Solar Thermodynamic System

Concentrated solar power refers to all techniques aimed at transforming the energy of solar radiation into heat and converting it into electrical energy through a thermodynamic cycle. This sector is mainly advantageous for countries with a lot of sunshine, such as Niger. In opposition to photovoltaic solar technology which uses global radiation, thermodynamic solar technology uses direct radiation from the sun. To evaluate direct radiation, our study focuses on four semi-empirical models: Perin-Brichamlbault, Liu_Jordan, Kasten, and R_sun model. Our work focuses on the comparison of direct solar radiation results obtained experimentally with these semi-empirical models and the modeling of direct radiation. The measurements of direct solar radiation were compared with those of semi-empirical models. This comparison allowed us to identify the semi-empirical model that is closest to experimental results with an average relative error of 9.80%: it’s Perrin, followed by R_sun 15.11%, Liu_Jordan 18.26%, and Kasten model 31.69%. We have adapted the Perin-Brichamlbault model by adding two parameters: solar constant and correction Earth-Sun distance. By using these parameters average relative error was reduced to 4.93% over the day and instantaneous errors to around 1% between 9 am and 4 pm. That corrected Perin-Brichamlbault model can be considered one of best for the prediction of solar thermodynamic systems.

Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions

The global agenda to increase the renewable energy share has driven many countries and entities to harness solar energy from solar photovoltaic (PV) systems. However, the power generation of PV systems is strongly affected by climate conditions. Therefore, the main objective of this study is to analyze and predict the power generation of different PV technologies under arid desert climate conditions on an hourly basis. Two areas have been considered as case studies: Adrar in Algeria and Alice Springs in Australia. A total of nine physical models and input parameter combinations from six different power plants have been used and tested for the suitability of the proposed models for predicting the power yield of PV power plants depending on solar irradiance and other meteorological variables. Then, an ensemble learning technique is applied to improve the performance capabilities of the best-fit input combinations. The results reveal that the global irradiance, ambient air temperature, and relative humidity combination are the most related to the PV power output of all technologies under all-sky conditions and provide effective and efficient performance with the proposed ensemble learning, with an estimated accuracy of over 99%.

Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning

Using surface solar radiation (SSR) during 2015–2019 and the corresponding meteorological variables, pollution observations and temporal code, random forest models were built for the seven stations in North China Plain. The permutation feature importance (PFI) and partial dependence plot (PDP) reveal that, besides temporal code, total cloud cover (TCC) and aerosol optical depth (AOD) are the most important influencing feature on global radiation (GR) and diffuse radiation (DR), respectively. Single feature PDP visualized how the input variables affect the estimated SSR. The different order in PFI of boundary layer height (BLH) between urban and farmland stations and the distinct variations in PDP of BLH among urban stations suggest the interaction of BLH and aerosols has substantial impact on the SSR in metropolis areas. The joint PDP displayed the nonlinear relationships between SSR and the input feature pairs. The change range of SSR with the corresponding input features and feature pairs is also investigated, with the maximum variation of GR from 63.4 Wm−2 to 231.9 Wm−2 under the influence of TCC − BLH and of DR from 51.8 Wm−2 to 104.9 Wm−2 under the impact of TCC − AOD in Zhengzhou, respectively. These findings provide insight into the simulation and prediction of SSR, temperature and subsequent weather conditions in urban areas.

Empirical sunshine-based models vs online estimators for solar resources

Monthly averaged daily global solar irradiation data collected from four online platforms (NASA POWER, MERRA2, ERA5 and CAMS) are compared with ground measured data retrieved from World Radiation Data Center and Baseline Surface Radiation Network. Similarly, monthly averaged daily global solar irradiation estimated by five different Ångström-Prescott-type equations have been compared with the same ground measured data. The accuracy of the online platforms and Ångström – Prescott equations, respectively, is largely comparable, although some differences are noticed. Both the platforms and the Ångström-Prescott equations are sensitive to climates. A tendency of the Ångström-Prescott equations to exceed the platforms performance in the tropical and continental climates was noticed. Without exception the platforms and models perform better in summer than in winter. No Ångström – Prescott equation outperforms the platforms during all seasons, and, mutually, no platform outperforms the Ångström – Prescott equations during all seasons.

Assessment and performance analysis of roof-mounted crystalline stand-alone photovoltaic (SAPV) system at selected sites in South Africa

BackgroundTechnology is deployed to take the advantage of the ultimate energy from the sun (solar energy) to be used as heat or clean electricity. This energy is classified as “sustainable energy” or “renewable energy” because it requires a short period to naturally replenish the used energy. The application of solar energy involves the conversion of the natural energy resource into a usable form, either as heat or as electricity. The device consists of solar cells made from semiconductor materials, such as silicon, cadmium telluride, gallium arsenide, and so on. Solar potential is both location- and climate-dependent; it is characterised by low energy intensity and intermittency, which limit its application; an improvement in photovoltaic (PV) system performance will facilitate more deployment of the clean electricity system. Therefore, this study provides PV potential and system information required for reliable and optimised solar PV systems at chosen locations. This work uses a 5-stage solar PV system assessment and system performance evaluation utilising Solargis Prospect software. The PV potential and system performance of nine selected site locations in South Africa was conducted using this method. The nine PV site locations are Bloemfontein (Free State), Germiston (Gauteng), Mahikeng (North-West), Mbombela (Mpumalanga), Musgrave (Kwazulu-Natal), Musina (Limpopo), Port Nolloth (Northern Cape), Port Elizabeth (Eastern Cape), and Worcester (Western Cape).ResultThe results of the study were categorised into PV meteorological and system performance parameters as follows. Photovoltaic meteorological parameters—the site in Mahikeng has the highest global horizontal irradiance (GHI), 2156 kWh/m2, and a corresponding specific PV power output (1819.3 kWh/kWp), closely followed by Bloemfontein (2111.5 kWh/m2, 1819.4 kWh/kWp) and Port Nolloth (2003.2 kWh/m2, 1820.5 kWh/kWp). The lowest GHI (1645.1 kWh/m2) and specific PV power output (1436.6 kWh/kWp) were recorded in Musgrave. Photovoltaic system performance parameters—the range of performance ratio (PR) between 75.8 and 77.7% was reported across the nine sites. This ratio met the acceptable benchmark of PR. The highest specific PV power output loss, 118.8 kWh/kWp, was obtained at sites in Bloemfontein, Mahikeng, and Port Nolloth, while the lowest, 93.8 kWh/kWp, was in Musgrave.ConclusionsThe results of the solar PV potential assessment and the evaluation of PV systems performance in the chosen sites across the nine provinces of South Africa show huge PV potential and energy yield. From the results, it was observed that the range of the yearly average of: (1) GHI among the sites is 1645.1–2156 kWh/m2; (2) direct normal irradiation among the sites is 1785.3–2559.3 kWh/m2; (3) diffuse horizontal irradiation among the sites is 512.5–686kWh/m2; (4) global tilted irradiation among the sites is 1849.2–2397.1 kWh/m2; (5) the temperature (TEMP) among the sites is 16–23 °C; (6) specific PV power output (PVOUT specific) among the sites is 1436.6–1820.5 kWh/kWp; (7) total PV power output (PVOUT total) among the sites is 14.366–2397.1 MWh; and (8) the performance ratio among the sites is 75.8–77.7%. Based on the solar resource and performance results of the PV system obtained, the deployment of monocrystalline solar PV technology in all the considered sites across South Africa is technically viable.

Forecasting solar-to-hydrogen and solar-to-methane energy conversion efficiency using Si and IMM PV-modules: A case-study in Japan

The solar-to-gas (StG) conversion system is a potential pathway for accelerating zero-carbon energy sources. In our previous study, outdoor StG conversion was accomplished using triple-junction concentrator photovoltaic (CPV) modules. The system achieved the highest performance with sets of DC/DC converters and electrochemical (EC) cells. Therefore, we aimed to investigate the prospective StG conversion from PV systems if such a system is installed in Japan. A power generation prediction model considering all weather conditions, called the Miyazaki Spectrum to Energy (MS2E), has been developed using the METPV-11 meteorological database at the University of Miyazaki. Accordingly, the amount of energy generated by various PV systems could be forecasted nationwide from the global irradiance data. In this study, we applied the MS2E PV energy prediction method and approached the potential StG conversion efficiency of silicon (Si) PV and inverted metamorphic (IMM) PV systems across Japan. Based on the actual StG measurement data, we approximated the conversion of each subsystem. The simulation results show that IMM modules can provide higher StG conversion than Si modules.

Investigation of the feasibility and potential use of sun tracking solutions for concentrated photovoltaic Case study Fez Morocco

This paper investigates the requirements, the feasibility and reliability of concentrator photovoltaic (CPV) with trackers in Fez city (Morocco) climate. In a first step, an appropriate sun tracker for a CPV prototype is design-simulated and then, based on finite element analysis, the effect of wind velocity distribution induced charge on the global structure (tracker and CPV module) is investigated for several elevation angle of the CPV unit. For all the investigated configurations, the maximum simulated misalignment still negligible, and is estimated to 4.6 × 10−6°, for a wind speed of 3.3 m/s. As a result, it is anticipated that the examined high CPV system (HCPV) will works safely under these effects. In a second step, using a home-developed Python code, and geometrical solar equations, calculations have been performed to estimate the diffuse, the direct and the global irradiations in this location. Ultimately, the energy performance of the system using System Advisor Model (SAM) software is carried out and compared to some on site real measurements. This simulation process led to a rigorous evaluation of the potential energy production with a tracked CPV panel, proving its viability in Fez Morocco. It also shows the level of accuracy of at least 0.5° that sun-tracking photovoltaic systems are necessary to maintain the least amount of power yield loss, especially for CPV facilities. This research will also serve as a valuable guide for the future sun-tracking CPV system.

Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks

Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm−2, and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm−2 for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.

Solar Irradiance Nowcasting System Trial and Evaluation for Islanded Microgrid Control Purposes

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system’s accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system’s accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich.