Solar Radiation Flux Research Articles

Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

This study proposes an integrated energy system using hydrogen storage to realize the efficient utilization of renewable energy sources and reduce the fluctuation when renewable energy is connected to grid. The system utilizes solar and wind energy to realize hydrogen production, desalination, and CCHP. First, the energy, exergy, and economic evaluations for the proposed system are carried out, and then an in-depth analysis of the key operating parameters is performed. The system achieves energy efficiency, exergy efficiency, and cost rate of 48.49%, 19.98%, and 7.969$/h, respectively. And the exergy analysis shows that the main exergy destructions are caused by the parabolic trough solar collector and the transcritical CO2 power cycle. The parametric analysis demonstrates when solar radiation flux and wind speed increase, the exergy efficiency and hydrogen production are increased, but the cost rate is increased accordingly. Finally, two sets of multi-objective optimization schemes are performed combining the artificial neural network with the non-dominant genetic algorithm-Ⅱ(NSGA-II). For the optimized fresh water output, cost rate, and exergy efficiency, it is achieving improvements of 51.73%, 8.4%, and 3.6%, and for the optimized hydrogen production, cost rate, and exergy efficiency, it is increased by 12.53%, 0.564%, and 0.75%, respectively.

A dataset of solar radiation and soil heat fluxes for agro-ecosystems in the hilly area of Central Sichuan Province (2005–2021)

A dataset of solar radiation and soil heat fluxes for agro-ecosystems in the hilly area of Central Sichuan Province (2005–2021)

Comparative Study of Solar Rotation of Transition Region and Corona using Solar Irradiance and Radio Flux

Comparative Study of Solar Rotation of Transition Region and Corona using Solar Irradiance and Radio Flux

Oceanic response to tropical cyclone in the northern South China Sea observed by underwater gliders during 2018 and 2020

In this study, we mainly used in-situ observations from underwater gliders to analyze the ocean response in the northern South China Sea affected by Son-tinh (2018), Mandal et al. (2018) Mangkhut (2018)and Noul (2020). The results showed that these TCs caused 0.6 °C, 1.1 °C and 1.7 °C maximum sea surface temperature cooling respectively, which were weaker than general conditions because of long distance, weak intensity and fast movement speed. Net solar radiation, precipitation, 10-m wind and sea surface heat flux also made contribution in changes of SST. The mixed layer depth (MLD) became shallower after Son-Tinh and Noul passed through, while during Mangkhut it did not change significantly. After TCs passed through, the stratification around MLD became more obvious, with a banded distribution and stronger high-value areas of buoyancy frequency. Within 1 week after the shortest distance, the temperature and salinity responses in the upper ocean were stronger than those at the sea surface, and the gradients of temperature and salinity and their anomalies were more evident in the subsurface layer. The results of this study show that underwater glider observations are important for understanding oceanic responses to tropical cyclones and are useful for studying tropical cyclone activities.

Near-surface ocean temperature and air-sea heat flux observed by a buoy array during summer to autumn in year 2014 in the northern South China Sea

The observational data of the air-sea interface and upper ocean elements from a buoy array deployed in the northern South China Sea from June to November 2014 is analyzed. The COARE 3.0 algorithm was adopted to examine the variability of air-sea heat fluxes and their contributions to near-surface ocean temperature changes. During the observation period, air-sea heat exchange in the northern South China Sea is primarily determined by solar shortwave radiation and latent heat flux, with net heat flux mostly negative, indicating that the ocean predominantly loses heat. In autumn, net heat flux loss significantly increased by an average of 34.4 W/m2, primarily due to enhanced latent heat loss driven by stronger winter monsoon. During typhoon period, net air-sea heat flux was significantly suppressed, mainly due to reduced solar shortwave radiation, with minimal contribution from sensible heat flux. Near-surface ocean temperature exhibited a seasonal cooling trend, averaging 29.7°C in summer and 28.4°C in autumn. The maximum cooling during typhoon reached 3.1°C, with minimal contribution from air-sea heat flux. During the monsoon transition period, weaker winds led to a slight increase in near-surface ocean temperature, with air-sea heat flux contributing 70.2% and 56.3% to this process. During winter monsoon, more uniform water column mixing resulted in a gradual decrease in near-surface ocean temperature, with air-sea heat flux contributing over 60% to this process.

Antarctic Warm Extremes Across Seasons and Their Response to Advection

AbstractAntarctic warm extremes impact the cryosphere, with very warm extremes driving surface melt on ice shelves. Here, we analyze temperatures exceeding the 90th percentile and the associated circulation patterns and radiation anomalies. ERA5 reanalysis data show positive geopotential height anomalies related to the occurrence of warm extremes. The highest temperature during warm extremes appears on the western periphery of high‐pressure systems, consistent with anticyclonic advection. Temperature anomalies during warm extremes are strongest in winter due to the transport of warm and moist air and a strong meridional temperature gradient. In summer, the weak meridional gradients of top‐of‐atmosphere downward solar radiation flux and surface air temperature contribute to weak temperature anomalies. Warm extremes are associated with positive longwave radiation anomalies in all seasons, but with negative shortwave radiation anomalies at the surface except during polar night. These relationships are verified by station observations. Our results confirm that Antarctic warm extremes are mostly driven by meridional advection of warm air, and suggest that these warm air masses are predominantly moist and cloudy.

Predicting multi-annual green roof net ecosystem exchange using machine learning

Green roofs are an urban mitigation strategy to increase CO2 uptake by green infrastructure. Reliable quantification of multi-year net ecosystem exchange (NEE) is essential for evaluating carbon balance, but proves challenging due to lack of long-term data and transferability of models. Machine learning can effectively predict NEE and identify non-linear patterns for spatially and temporally upscaling. For the first time, we developed Random Forest (RF) models using long-term eddy covariance (EC) data from 2015 to 2020 from an extensive green roof at Berlin-Brandenburg Airport (BER) to understand the influence of meteorological predictors on NEE prediction and transferability. A simple (M1), extended (M2) and optimized (M3) model based on different combinations of predictors were built. M3 performed best, deviating by −13 % from the observed uptake of −132.4 gC m−2 a−1 (R2 of 0.74), with M2 (R2 = 0.73) showing similar results. Key predictors were volumetric water content (VWC) and solar radiation flux densities. The models showed robust performance under pronounced environmental conditions. Drought in 2018 introduced significant uncertainties, whereas higher VWC in 2019 and 2020 led to enhanced performance. All models tended to overestimate CO2 assimilation and underestimate respiration of the green roof. M1 deviated by −58 % from observed NEE over the entire period, indicating transferability of M1 to other locations is not feasible, whereas transferability of M2 and M3 showed better potential for broader application with minimal calibration. This study highlights the importance of water-related variables and available energy and demonstrates that RF, incorporating NEE process understanding, can effectively predict NEE.

Bifacial Photovоltaic Sensor for Insolation Energy Resource Monitoring

Accurate and reliable measurements of the total solar radiation flux make it possible to evaluate the efficiency of using stand-alone photovaltaic systems in various meteorological conditions. These measurements allow more accurately predict in time the energy production volume, accumulator and capacitive storage devices parameters and the payback period. Research purpose was to study the possibility of creating a photoelectric sensor and a method of uninterrupted measurement based on this sensor which allows to measure total solar radiation flux including its direct, diffused and reflected components simultaneously. The photovaltaic sensor with bifacial photosensitivity was manufactured and applied, which is low-inertia comparing to traditional thermoelectric pyranometers, and its spectral sensitivity is quite close to the same parameter of photovoltaic power supply system. It creates the possibility to estimate the insolation level capable to be completely converted into electrical energy without an ineffective heat-generating long-wavelength part of the solar spectrum. A laboratory measuring stand was made to test the sensor's operability. Modeling and experiments’ the sensor parameters were carried out and confirmed its operability. The bifacial photosensitivity sensor allows to control simultaneously direct, diffused and reflected from the earth's surface components of solar radiation, what gives more complete information about the energy potential of the photovoltaic power supply system location.

The influence of spatial variability of solar radiation on the mass balance of glaciers in the Grønfjorden Bay area (the Svalbard archipelago)

In this article, we investigate how the irregular insolation of two low-elevated Svalbard glaciers exerts effect on rates of their surface melting. We compare the spatial distribution of rates of the surface lowering of glaciers Vøringbreen (0.76 km2) and Aldegondabreen (5.5 km2), both are located near Barentsburg settlement in the western part of Nordenskiöld Land (the Spitsbergen Island). As an approximation of the solar radiation flux, we used the potential incoming solar radiation calculated by the ArcticDEM digital elevation model for the period July 15–September 15, which is a typical time of ice ablation in the region under consideration. Motions of both glaciers are extremely slow, which allows assuming that lowering of their surfaces are identical to the rates of surface melting. We have found that both glaciers are distinctly divided into two parts, more and less sunlit. The spatial pattern of insolation of the Vøringbreen glacier is controlled by the shading of the walls surrounding the cirque, while the Aldegondabreen one due to its concave shape has two different areas with a more southern and more northern exposure. The lowering of the surface shows that the more and less illuminated parts differ significantly in ice ablation. The maximum differences in melting caused by the irregular insolation are 2.1 m of ice depth over five years for the Aldegondabreen Glacier (2008–2013 and 2013–2018) and 2.2 m over six years for the Vøringbreen Glacier (2013–2019), that is 40, 30 and 25% of the total values of the surface depression for the corresponding periods. Within every 50-meter altitude interval, correlation coefficients between surface ablation and insolation vary from –0.33 to –0.62 for the Aldegondabreen and from –0.50 to –0.92 for the Vøringbreen glacier. When compared with the vertical gradient of the ice melting, the variability of ablation caused by the irregular insolation correspond to a difference in altitudes of 45–50 m in vertical for the Aldegondabreen and 60 m for Vøringbreen. These values are significant taking into account the small altitudinal range of the glaciers in that part of Spitsbergen.

SENSITIVITY OF A 1D LAKE MODEL TO THE INPUT METEOROLOGICAL DATA

The paper evaluates how the choice of a source of meteorological data used as boundary conditions and the changes in model calibration parameters affect the adequate performance of a model of lake thermo- and hydrodynamics. The quality of simulation of water temperature dynamics in a small lake by a onedimensional GLM model using data from an automatic meteorological station on the lake shore, a state weather station and two global reanalyses was quantitatively assessed. It is shown that the best modeling result (RMSE of water temperature calculation 0,8-2,0°C, Nash-Sutcliffe efficiency >0,7) can be achieved using data sets that explicitly provide incoming shortwave and longwave radiation fluxes. At the same time, good representation of the bottom water temperature requires correct setting of wind regime, which is only possible if data from instrumental ground-based observations at a stationary weather station are applied. Combining ground-based observations of wind speed, air temperature and humidity with radiation fluxes from global reanalyses makes it possible to achieve a calculation error ≤1°C at all depths.The analysis of model sensitivity to calibration parameters in case when the reanalysis data are applied showed that parameters related to wind mixing, i.e. the scaling factor for wind speed and constants related to the in-model description of wind impact energy, have the greatest influence on the final calculation error. If we change the intensity coefficients of other types of mixing during calibration it does not lead to an obvious trend in the quantitative estimates of modeling quality. It could also be reasonable to introduce correction multipliers to the values of air temperature and solar radiation fluxes, thus neutralizing the effect of unrepresentativeness of the meteorological data applied for a particular lake, as well as to correct the value of solar radiation extinction coefficient relative to the value obtained from the Secchi depth. The results of the study could be used to justify the choice of meteorological data source and to optimize the methods of calibration of lake thermal stratification models

Study of bifacial photovoltaics with fluorinated ethylene propylene as an anti-reflective layer

Bifacial photovoltaics is a type of solar photovoltaics technology that is fast growing in popularity owing to the several advantages it offers. In this study, the inclusion of fluorinated ethylene propylene polymer as one of the constituent materials in bifacial photovoltaic modules under various configurations and its effect on the module’s optical performance was investigated and compared with a commercial bifacial module. Monte Carlo ray tracing was used to conduct the study and the system was analyzed under both non tracking and uniaxial tracking conditions for varying surface albedo values corresponding to an ideal scattering surface, white concrete and sand. The analyses performed under normal incidence condition revelated that the net irradiance on the PV layers varied by as much as 96.0314 W m−2 between the best and worst performing bifacial configurations. Under uniaxial tracking, the top and rear surfaces of the PV modules could cumulatively be subjected to 21.799 kWh of solar irradiation flux per day over a generation window of eleven hours. The proposed changes could offer cost savings of USD 0.0118 per panel per day and up to an additional 5.802 kg of CO2 equivalent offsets per panel per year.

Concentrated solar flux modeling in solar power towers with a 3D objects-atmosphere hybrid system to consider atmospheric and environmental gains

This article presents a realistic and novel method to estimate the solar radiant flux collected by the receiver of a solar power tower (SPT) system, taking into account the detailed atmospheric radiative transfer. It describes how an atmospheric radiative transfer Monte Carlo code is modified to solve the radiative transfer both in the atmosphere and within the concentrating system consisting of the heliostat field and the receiver. To validate the geometric modeling of a complete SPT (624 heliostats with 24 facets) as well as the estimation of its optical efficiency (both independent of the atmosphere), a comparison with the reference ray-tracing code “Solstice” is presented for two times of the day, two solar disk half-angles, and two heliostat surface slope errors. This new model allows the estimation of not only the optical losses but also, as in Moulana (2019), the gains due to atmospheric and environmental contributions i.e., radiant flux from circumsolar, aerosol scattering, ground reflection, etc. Annual average results (with a numerical uncertainty less than 0.01%) under clear sky conditions (without clouds) show that the gains are not negligible and could reach up to 0.414 MW (1.08% of the radiant flux collected by the receiver) for a relatively small SPT located in a desert area.

Engineering optics and thermodynamics design of parabolic trough solar collector

Abstract The development and design of parabolic trough solar thermal collectors (PTC) need to comprehensively consider the influence of optic, thermodynamic, layout, and other factors. The optics and thermodynamic analysis of PTC with different configurations was carried out, and the influence of layout on heat collection was studied. In the case of determined PTC aperture size, the distribution trend of solar irradiation flux density of different HCE diameters is the same, and the peak value of solar irradiation flux density is higher when the concentration ratio is larger. For the PTC with the same concentration ratio, with the incidence angle increase, the intercept rate and optical efficiency of the PTC decrease, the thermal energy loss rate increases, and the thermal efficiency decreases. Under the same incidence angle, with the increase of concentration ratio, the PTC’s intercept rate and optical efficiency decrease gradually, and the average efficiency of the PTC increases first and then decreases. In the process of PTC design and selection, considering the concentration ratio, latitude, and cost factors, the aperture size of 8.2 m and the HCE of the inner diameter of 90 mm can be used as the recommended configuration scheme for subsequent HCE development.

Wildfire smoke reduces lake ecosystem metabolic rates unequally across a trophic gradient

Wildfire smoke covers entire continents, depositing aerosols and reducing solar radiation fluxes to millions of freshwater ecosystems, yet little is known about impacts on lakes. Here, we quantified trends in the spatial extent of smoke cover in California, USA, and assessed responses of gross primary production and ecosystem respiration to smoke in 10 lakes spanning a gradient in water clarity and nutrient concentrations. From 2006 − 2022, the maximum extent of medium or high-density smoke occurring between June-October increased by 300,000 km2. In the three smokiest years (2018, 2020, 2021), lakes experienced 23 − 45 medium or high-density smoke days, characterized by 20% lower shortwave radiation fluxes and five-fold higher atmospheric fine particulate matter concentrations. Ecosystem respiration generally declined during smoke cover, especially in low-nutrient, cold lakes, whereas responses of primary production were more variable. Lake attributes and seasonal timing of wildfires will mediate the effects of smoke on lakes.

Deep temporal convolutional networks for F10.7 radiation flux short-term forecasting

Abstract. F10.7, the solar flux at a wavelength of 10.7 cm (F10.7), is often used as an important parameter input in various space weather models and is also a key parameter for measuring the strength of solar activity levels. Therefore, it is valuable to study and forecast F10.7. In this paper, the temporal convolutional network (TCN) approach in deep learning is used to predict the daily value of F10.7. The F10.7 series from 1957 to 2019 are used. The data during 1957–1995 are adopted as the training dataset, the data during 1996–2008 (solar cycle 23) are adopted as the validation dataset, and the data during 2009–2019 (solar cycle 24) are adopted as the test dataset. The leave-one-out method is used to group the dataset for multiple validations. The prediction results for 1–3 d ahead during solar cycle 24 have a high correlation coefficient (R) of 0.98 and a root mean square error (RMSE) of only 5.04–5.18 sfu. The overall accuracy of the TCN forecasts is better than the autoregressive (AR) model (it only takes past values of the F10.7 index as inputs) and the results of the US Space Weather Prediction Center (SWPC) forecasts, especially for 2 and 3 d ahead. In addition, the TCN model is slightly better than other neural network models like the backpropagation (BP) neural network and long short-term memory (LSTM) network in terms of the solar radiation flux F10.7 forecast. The TCN model predicted F10.7 with a lower root mean square error, a higher correlation coefficient, and a better overall model prediction.

Climatology and trends of cloudiness in a productive rice and vegetable region of South‐Eastern South America

AbstractClouds are a key element of the climate system. They play a crucial role in modulating solar radiative flux reaching the Earth's surface and influencing environmental conditions. In particular, total cloud cover (TCC) has both direct and indirect influence on agricultural production. Through dialogues with vegetable and rice producers of Northeastern Argentina (NEA), an important productive region in South‐Eastern South America, the key role of cloud cover on crop yields was brought to the forefront. In this study, we present the climatology and observed long‐term changes in TCC in NEA. The analyses are performed based on two independent datasets: TCC ground‐based (GB) observations and satellite‐based estimates from the International Satellite Cloud Climatology Project (ISCCP). The datasets cover a common period, from December 1983 to November 2016 (Satellite Period), while GB TCC observations extend further, from March 1961 to February 2021 (GB Period). To facilitate a more comprehensive examination of GB TCC, we introduce a novel Cloud Index, expressed in a familiar unit (%), which allows the study of its temporal variations. TCC exhibits a distinct annual cycle and substantial spatial variability over NEA, consistently evident in both satellite and GB datasets. Over the Satellite Period (GB Period), we observed a decrease in NEA's TCC in both datasets (GB dataset), with seasonal variations and spatial heterogeneity. We ensured the reliability of our results by comparing the two datasets, which showed similar temporal variability, although the ISCCP Cloud Amount values were larger than the GB TCC. The described TCC climatology and observed changes have considerable implications for NEA's agricultural production and provide a robust foundation for future research. This article generates robust methodological basis for the analysis of this complex variable, a necessary step to explore the forcing of climate variability that modulates cloudiness in future research, and that could facilitate future studies in other regions of South America and the world.

ВЛИЯНИЕ ТРЕНДА СКОРОСТИ ВЕТРА НА ПРОДОЛЖИТЕЛЬНОСТЬ ПЕРИОДА ТЕРМИЧЕСКОГО КОМФОРТА НА ЕВРОПЕЙСКОЙ ЧАСТИ РОССИИ В ПОСЛЕДНИЕ ДЕСЯТИЛЕТИЯ

The combination of the parameters of temperature, humidity, and wind conditions of surface air, solar radiation fluxes, human metabolism under various modes of physical exertion and clothing properties characterize the thermal comfort. Under conditions of global warming, the climate characteristics that affect the quality of life, health, and well-being of people, in particular, the length of thermal comfort periods, are changing. Bioclimatic indices that determine the thermal comfort conditions depend not only on temperature changes, but also on humidity and wind speed trends. When comparing the results of the calculations of the thermal comfort period length with the use of air temperature observations and major bioclimatic indices, the authors have revealed a significant difference in the effect of changes in average wind speed on the length of the thermal comfort period in different regions of European Russia.

Enabling Self-Adaptive Water-Energy-Balance of Photothermal Water Diode Evaporator: Dynamically Maximizing Energy Utilization Under the Ever-Changing Sunlight.

Maintaining a match between input solar energy and required energy by water supply management is key to achieving efficient interfacial solar-driven evaporation (ISDE). In practice, the solar radiation flux is constantly changing throughout the day, so keeping a dynamic water-energy-balance of ISDE is a big challenge. Herein, a photothermal water diode (WD) evaporator concept is proposed by an integrated hydrophilic/hydrophobic Janus absorber to overcome the issue. Due to the unique unidirectional water transport properties induced by asymmetric wettability, a self-adaptive balance between photothermal energy input and water uptake is established, thus realizing the energy matching and utilization maximization. The experimental and simulation results exhibit that with the increase of sunlight intensity, the water supply speed is significantly accelerated due to the dynamic management and self-regulation on water replenishment. Therefore, an excellent evaporation rate of up to 2.14kgm-2h-1 with a high efficiency of 93.7% under 1sun illumination is achieved. This water diode engineering with Janus wettability provides a novel strategy and extends the path for designing solar evaporation systems with diverse water supply properties, which shows great potential in different environmental conditions.

The solar radiation evaluation by satellite images processing

. To evaluate the global solar irradiation flux received at the ground surface for Algeria, an analytical model is implemented by processing satellite images and solving the equation of radiative transfer. This model is derived from that initially worked out by C. Gautier et al. in 1980 using high-resolution Goes images. We get that it is well adapted to the processing of lesser resolution images such as those collected by Meteosat 2 following the B2 format. The data base under study mainly consists of clear-sky B2 Meteosat images recorded every three hours in the visible channel (i.e., [0.4 – 1.1 µm]) during the 1986/87 period and representing North Africa and Southern Europe. Hourly and daily global solar irradiation fluxes received at the ground on a horizontal surface have therefore been evaluated by applying the analytical model to the Meteosat images. The obtained results were compared to the hourly ground solar measurements recorded in the radiometric stations of Bouzareah (Algiers) and Oran during the 1986/87 period. Since the deviations between both types of solar data do not exceed 10%, the radiances estimated by modelling are found to be close to the related ground measurements.

F10.7 Daily Forecast Using LSTM Combined With VMD Method

AbstractThe F10.7 solar radiation flux is a well‐known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short‐term Memory (LSTM) network are combined to construct a VMD‐LSTM model for predicting F10.7 values. The F10.7 sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final F10.7 value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD‐LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (R) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD‐LSTM model exhibits strong predictive capability for the F10.7 index during solar cycle 24.