Solar Time Research Articles

Unusually long path length for a nearly scatter-free solar particle event observed by Solar Orbiter at 0.43 au

Context. After their acceleration and release at the Sun, solar energetic particles (SEPs) are injected into the interplanetary medium and are bound to the interplanetary magnetic field (IMF) by the Lorentz force. The expansion of the IMF close to the Sun focuses the particle pitch-angle distribution, and scattering counteracts this focusing. Solar Orbiter observed an unusual solar particle event on 9 April 2022 when it was at 0.43 astronomical units (au) from the Sun. Aims. We show that the inferred IMF along which the SEPs traveled was about three times longer than the nominal length of the Parker spiral and provide an explanation for this apparently long path. Methods. We used velocity dispersion analysis (VDA) information to infer the spiral length along which the electrons and ions traveled and infer their solar release times and arrival direction. Results. The path length inferred from VDA is approximately three times longer than the nominal Parker spiral. Nevertheless, the pitch-angle distribution of the particles of this event is highly anisotropic, and the electrons and ions appear to be streaming along the same IMF structures. The angular width of the streaming population is estimated to be approximately 30 degrees. The highly anisotropic ion beam was observed for more than 12 h. This may be due to the low level of fluctuations in the IMF, which in turn is very probably due to this event being inside an interplanetary coronal mass ejection The slow and small rotation in the IMF suggests a flux-rope structure. Small flux dropouts are associated with very small changes in pitch angle, which may be explained by different flux tubes connecting to different locations in the flare region. Conclusions. The unusually long path length along which the electrons and ions have propagated virtually scatter-free together with the short-term flux dropouts offer excellent opportunities to study the transport of SEPs within interplanetary structures. The 9 April 2022 solar particle event offers an especially rich number of unique observations that can be used to limit SEP transport models.

Resolving the tidal weather of the thermosphere using GDC

NASA’s Geospace Dynamics Constellation (GDC) mission is a six satellite constellation to make in situ measurements of important ionospheric and thermospheric variables to better understand the processes that govern Earth’s near space environment. Scheduled for a 2029 launch into high inclination orbits ∼82° at ∼380 km, the satellite orbit planes will separate over time to provide almost continuous local solar time coverage every day towards the end of the 3 year baseline GDC mission. As such, the neutral temperature and neutral wind measurements of GDC will likely allow the heliophysics community to make significant progress towards resolving the tidal weather of the thermosphere, that is, day-to-day tidal variability, and how it is driven by meteorological processes near the surface and in situ forcing in the ionosphere-thermosphere system. To assess the GDC ability to accurately resolve the tides each day and when in the mission this can be achieved, we conduct an Observational Simulation System Experiment (OSSE) using SD-WACCM-X and the predicted GDC orbits. Our results show that GDC can provide closure on the tidal variability (mean, diurnal and semidiurnal, migrating and nonmigrating) at orbit height in mission phase 4 and throughout most parts of mission phase 3. We also perform Hough Mode Extension fitting of relevant tidal components to study possible connections between the GDC observations and the tides at 200 km, to assess synergies between GDC and the forthcoming DYNAMIC mission (scheduled to be co-launched with GDC) that will measure altitude-resolved winds and temperatures in the ∼100–200 km height range.

Release Episodes of Electrons and Protons in Solar Energetic Particle Events

We analyzed a sample of 21 solar energetic particle (SEP) events with clear signatures in both near-relativistic electrons and high-energy protons spanning over ∼2.5 solar cycles from 1997 to 2016. We employed velocity dispersion analysis (VDA) for protons and fractional VDA (FVDA) for electrons, as well as time shifting analysis (TSA) in order to identify the solar release times (SRTs) of the electrons. We found that, for the majority of the events (62%), a simultaneous release was observed, while, for 14% of the events, electrons were released later than protons (i.e., delayed electrons); for 24% of the events, the opposite result was found (i.e., delayed protons). We found that the path length (L) traveled by the protons and electrons was not related to the aforementioned categorization. Moreover, we show that, in the case of simultaneous SEP events, protons and electrons are being released in close connection to type III and type II bursts, while the opposite is the case for delayed events. In addition, we demonstrate that, for the simultaneous events, both the proton and the electron release are established in heights < 5RS and that, especially for the well-connected simultaneous events, there is a co-occurrence of the type II burst with the release time of the particles.

H2 Production from Real Wastes of Polyethylene Terephthalate and Polylactic Acid using CNx/Ni2P Nanocatalyst

In this study, H2(g) production from real wastes of polyethylene terephthalate and polylactic acid using CNx/Ni2P nanocatalyst was investigated with photoreforming process. Optimum experimental conditions were found at ultrasonicated 4.8 mg/ml CNx/Ni2P nanocatalyst, at pre-treated 20 mg/ml polyethylene terephthalate, at pre-treated 20 mg/ml polylactic acid, at 4 ml aqueous 1 M KOH, at 10 ml internal volume of sealed photoreactor under anaerobic conditions, at 1200 W Xe solar lamp, at 60 h photoreforming solar irradiation time, at AM 1.0G, at 150 mW/cm2, at 25°C, respectively. XRD, FESEM, EDX, FTIR, TEM, DRS and XPS analyzes were performed for characterization studies of microplastics. Polyethylene terephthalate and polylactic acid measurements were measured in inductively coupled plasma mass spectrometry (ICP-MS). H2(g) measurements were made in gas chromatography–mass spectrometry (GC-MS). The maximum 41.40 ± 5.10 and 48.60 ± 3.12 µmol H2 / gsub yields were measured for CNx=20 mg/ml and for Ni2P=20 mg/ml, respectively. The maximum 18.26 ± 1.18 and 52.41 ± 7.29 µmol H2 / gsub H2(g) production yields were found for non-sonicated CNx/Ni2P and ultra-sonicated CNx/Ni2P nanocatalyst, respectively, after 24 h photoreforming solar irradition time. The maximum 123.75 ± 11.92 and 267.41 ± 24.65 µmol H2 / gsub H2(g) production yield was measured for polyethylene terephthalate and polylactic acid, respectively, after 60 h photoreforming solar irradiation time. The maximum 6.57 ± 0.87Percentage and 2.43 ± 0.38Percentage stoichiometric H2 conversion yields were observed for polyethylene terephthalate and polylactic acid, respectively, after 60 h photoreforming solar irradition time. The maximum 96 and 57 μmol H2 / gsub H2(g) yields for polyethylene terephthalate were obtained over CNx/Ni2P and H2NCNx/Ni2P, respectively, after 60 h photoreforming solar irradiation time. The maximum 182 and 173 μmol H2 / gsub H2(g) yields for polylactic acid were observed over CNx/Ni2P and H2NCNx/Ni2P, respectively, after 60 h photoreforming solar irradiation time. The maximum 4.85 ± 0.62, 88.37 ± 10.74, 26.55 ± 1.95, 21.94 ± 1.86, 75.30 ± 9.34, 60.07 ± 5.11 and 14.61 ± 2.14 μmol H2 / gsub H2(g) production yields were obtained for Acetate, Ethylene glycol, Formate, Glycolate, Glyoxal, Lactate and Terephthalate oxidation intermediates, respectively, after 24 h photoreforming solar irradiation times. 126 nmol Acetate, 131 nmol Formate, 5 nmol Glycolate and 6200 nmol Glyoxal organic oxidation intermediates for polyethylene terephthalate with CNx/Ni2P nanocatalyst were found after 7 days photoreforming solar irradiation time. 67 nmol Acetate and 63 nmol Formate organic oxidation intermediates for polylactic acid with CNx/Ni2P nanocatalyst were obtained after 7 days photoreforming solar irradiation time. Photoreforming process is a very effective, easy to apply, economical and environmentally friendly method for the removal of plastic and microplastic wastes.

Sequential stochastic and bootstrap methods to generate synthetic solar irradiance time series of high temporal resolution based on historical observations

Generating synthetic solar irradiance data through computational methods has become an attractive approach, especially when obtaining accurate measurements from historical observations or satellite-derived estimates is not feasible. Since solar photovoltaic energy production is influenced by variable weather conditions, having reliable solar irradiance data is crucial for evaluating the performance of photovoltaic systems accurately. In this paper, we propose two methods, namely stochastic method, and bootstrap method, to generate one-day synthetic solar irradiance data at a minimum 60-minute time resolution. Both techniques consider dynamic meteorological behavior and maintain the physical significance of the observed data, effectively capturing the intermittent nature of solar irradiance. Validations were performed for five sites with different climate conditions and significant geographical separation, and different time resolutions, through various metrics, including variability, statistical distribution, and energy production, which demonstrated mean average percentage errors as low as 2.1% for intraday irradiance fluctuations, statistical distribution goodness-of-fit up to 80%, and discrepancy for energy production close to 2.4%. results verify the applicability of the proposed methods regardless the time resolution, the location of the measured data and the procedure to record it (i.e., from ground weather stations or satellite-derived estimates). We also evaluated the computational performance of the proposed methods, synthetic, where the elapsed time to generate the synthetic data takes less than a minute to generate a hundred one-day solar irradiance time series. The novelty of the proposed methods is that it is only needed the information of a single month to generate representative synthetic solar irradiance one-day sequences generated for five climate conditions without the need of a training process (i.e., iterative adjustments of the parameters), being useful for pre-feasibility studies of the photovoltaic project and to train machine learning algorithms that deals with time series. To encourage the reproducibility of the research presented in this paper, the proposed methods to generate synthetic solar irradiance data are freely available in a well-documented GitHub repository from https://github.com/salazarna/synthetic-irradiance-sequence.

Analysis of seasonal variability and complementarity of wind and solar resources in Mexico

Solar and wind time variability may be mitigated by complementing each other, providing firm power to the national grid at local and regional scales. The field commonly utilizes reanalysis data; however, it is crucial to conduct bias analysis and validation to enhance reliability. Mexico’s vast geography and resource potential demand a comprehensive study to validate the accuracy of modeling critical meteorological variables in power estimations. This study explored wind and solar resources’ local and regional complementarity using experimental and ERA5 data. A bias-correction method was used to identify bias effects on power estimations and the reliability of long-term capacity factor analysis. Spearman’s coefficient was used as the complementarity index. The results show that the capacity factor of wind turbines is affected by bias-correction methods compared to that of photovoltaics. Spearman’s coefficient, in general, is robust over bias-correction. The Yucatan Peninsula displays significant potential throughout the year, Spearman −0.6, for combining offshore wind farms with onshore photovoltaics. During summer, the north-central, northeast regions and the Baja California Peninsula offer significant potential for combining wind and solar energy, −0.6 and −0.5 values, respectively. Incorporating renewable energy requires further analysis of local integration and technical limitations to create sustainable long-term solutions.

PRIME: a probabilistic neural network approach to solar wind propagation from L1

Introduction: For the last several decades, continuous monitoring of the solar wind has been carried out by spacecraft at the first Earth-Sun Lagrange point (L1). Due to computational expense or model limitations, those data often must be propagated to some point closer to the Earth in order to be usable by those studying the interaction between Earth’s magnetosphere and the solar wind. The current most widely used tool to propagate measurements from L1 (roughly 235 RE upstream) to Earth is the planar propagation method, which includes a number of known limitations. Motivated by these limitations, this study introduces a new algorithm called the Probabilistic Regressor for Input to the Magnetosphere Estimation (PRIME).Methods: PRIME is based on a novel probabilistic recurrent neural network architecture, and is capable of incorporating solar wind time history from L1 monitors to generate predictions of near-Earth solar wind as well as estimate uncertainties for those predictions.Results: A statistical validation shows PRIME’s predictions better match MMS magnetic field and plasma measurements just upstream of the bow shock than measurements from Wind propagated to MMS with a minimum variance analysis-based planar propagation technique. PRIME’s continuous rank probability score (CRPS) is 0.214σ on average across all parameters, compared to the minimum variance algorithm’s CRPS of 0.350σ. PRIME’s performance improvement over minimum variance is dramatic in plasma parameters, with an improvement in CRPS from 2.155 cm−3 to 0.850 cm−3 in number density and 16.15 km/s to 9.226 km/s in flow velocity VX GSE.Discussion: Case studies of particularly difficult to predict or extreme conditions are presented to illustrate the benefits and limitations of PRIME. PRIME’s uncertainties are shown to provide reasonably reliable predictions of the probability of particular solar wind conditions occurring.Conclusion: PRIME offers a simple solution to common limitations of solar wind propagation algorithms by generating accurate predictions of the solar wind at Earth with physically meaningful uncertainties attached.

Circannual seizure provocation as the day lengthens in the northern and southern hemispheres.

Circannual status epilepticus (SE) patterns in communities near Earth's poles best test the hypothesis that SE susceptibility varies with light exposure because these communities are routinely subject to large changes in annual light exposure, which may result in changes to daily sleep time. We compared northern hemispheric circannual SE occurrence in Kivalliq, Canada (latitude-62.8° N) to southern hemispheric Auckland, New Zealand (latitude-36.9° S). Instead of peaking at a similar calendar time, SE peaked at a similar solar time during the increasing daylight phase after each region's respective winter solstice. This demonstrates that cumulative effects of increasing light exposure can mediate SE susceptibility.

Storage requirements to mitigate intermittent renewable energy sources: analysis for the US Northeast

Moving away from fossil fuels is essential for a sustainable future. Carrying out this transition without reversing the improvements in the quality of life is the ultimate challenge. While minimizing the anticipated impacts of climate change is the primary driver of decarbonization, the inevitable exhaustion of fossil energy sources should provide just as strong or perhaps even stronger incentives. The vast majority of publications outlining the pathways to “net-zero carbon emission” fall short from leading to a truly “fossil fuel-free” future without falling back to some level of dependence on fossil fuels with carbon capture and sequestration. While carbon capture and sequestration might be a necessary step toward decarbonization, such intermediate goals might turn into a dead end without defining the end point. The main obstacle to wider adoption of renewable energy resources is their inherent intermittency. Solar and wind are, by far, the most abundant renewable energy sources that are expected to take the lion share in transitioning to a sustainable future. Intermittency arises at multiple levels. The most recognized are the short-term (minute-by-minute, hourly, or diurnal) variations that should be the easiest to address. Less frequently realized are the seasonal and inter-annual variabilities. Seasonality poses far greater challenges than minute-by-minute or hourly variations because they lead to the absence of energy resources for prolonged periods of time. Our interest is the feasibility of a future where all energy (100%) comes from renewable sources leaving no room for fossil fuels. We carry out rudimentary statistical analyses of solar radiation and wind speed time series records to quantify the degree of their intermittencies seasonally and inter-annually. We employ a simple but robust accounting of the shortfalls when the supplies do not meet demand via a modified cumulative supply/deficit analysis that incorporates energy losses arising from transporting excess energy to storage and retrieving it as needed. The presented analysis provides guidance for choosing between the installation of excess capacity or the deployment of energy storage to guarantee reliable energy services under the assumption that the energy system is powered exclusively by renewable energy sources. This paper examines the seasonal and inter-annual variability of hydropower and biofuel resources to estimate their potential to mitigate the intermittencies of solar and wind resources. The presented analyses are meant to provide crude, bulk part estimates and are not intended for planning or operational purposes of the actual energy infrastructures. The primary focus of this paper is the Northeast region of the United States using the conterminous United States as a reference to assess the viability of reducing the energy storage need in the study region via improved connectivity to the national grid. This paper builds on the modeling exercises carried out as part of the climate-induced extremes on food, energy, water systems studies.

A critical evaluation of decadal solar cycle imprints in the MiKlip historical ensemble simulations

Abstract. Studies concerning solar–terrestrial connections over the last decades claim to have found evidence that the quasi-decadal solar cycle can have an influence on the dynamics in the middle atmosphere in the Northern Hemisphere (NH) during the winter season. It has been argued that feedbacks between the intensity of the UV part of the solar spectrum and low-latitude stratospheric ozone may produce anomalies in meridional temperature gradients which have the potential to alter the zonal-mean flow in middle to high latitudes. Interactions between the zonal wind and planetary waves can lead to a downward propagation of the anomalies, produced in the middle atmosphere, down to the troposphere. More recently, it has been proposed that top-down-initiated decadal solar signals might modulate surface climate and synchronize the North Atlantic Oscillation. A realistic representation of the solar cycle in climate models was suggested to significantly enhance decadal prediction skill. These conclusions have been debated controversial since then due to the lack of realistic decadal prediction model setups and more extensive analysis. In this paper we aim for an objective and improved evaluation of possible solar imprints from the middle atmosphere to the surface and with that from head to toe. Thus, we analyze model output from historical ensemble simulations conducted with the state-of-the-art Max Planck Institute for Meteorology Earth System Model in high-resolution configuration (MPI-ESM-HR). The target of these simulations was to isolate the most crucial model physics to foster basic research on decadal climate prediction and to develop an operational ensemble decadal prediction system within the “Mittelfristige Klimaprognose” (MiKlip) framework. Based on correlations and multiple linear regression analysis we show that the MPI-ESM-HR simulates a realistic, statistically significant and robust shortwave heating rate and temperature response at the tropical stratopause, in good agreement with existing studies. However, the dynamical response to this initial radiative signal in the NH during the boreal winter season is weak. We find a slight strengthening of the polar vortex in midwinter during solar maximum conditions in the ensemble mean, which is consistent with the so-called “top-down” mechanism. The individual ensemble members, however, show a large spread in the dynamical response with opposite signs in response to the solar cycle, which might be a result of the large overall internal variability compensating for rather small solar imprints. We also analyze the possible surface responses to the 11-year solar cycle and review the proposed synchronization between the solar forcing and the North Atlantic Oscillation. We find that the simulated westerly wind anomalies in the lower troposphere, as well as the anomalies in the mean sea level pressure, are most likely independent from the timing of the solar signal in the middle atmosphere and the alleged top-down influences. The pattern rather reflects the decadal internal variability in the troposphere, mimicking positive and negative phases of the Arctic and North Atlantic oscillations throughout the year sporadically, which is then assigned to the solar predictor time series without any plausible physical connection and sound solar contribution. Finally, by applying lead–lag correlations, we find that the proposed synchronization between the solar cycle and the decadal component of the North Atlantic Oscillation might rather be a statistical artifact, affected for example by the internal decadal variability in the ocean, than a plausible physical connection between the UV solar forcing and quasi-decadal variations in the troposphere.

Comparison of a Bottom-Up GNSS Radio Occultation Method to Measure D- and E-Region Electron Densities with Ionosondes and FIRI

High-frequency skywave propagation can be heavily impacted by D- and E-region dynamics requiring accurate global measurements to optimize performance. A standard measurement technique is to use ionosondes, but they are unable to measure below 1 MHz and are only available at a limited number of land-locked sites around the globe. In contrast, the Global Navigation Satellite System radio occultation (GNSS-RO) bottom-up method is a new approach specifically designed to generate electron density profiles in the D- and E- region ionosphere. It takes advantage of satellite constellations that currently provide over 20,000 daily measurements and global coverage. In this paper, GNSS-RO profiles were compared against ionosonde profiles at four sites covering a wide latitudinal range, and FIRI modeled profiles corresponding to the same latitude and local solar time. This comparison was completed using daytime profiles when sporadic-E (Es) was not present. The average GNSS-RO profile is found to be a few kilometers higher in altitude than the ionosonde profiles at the minimum frequency, fmin. When the ionosonde profiles are shifted so that the altitudes match at fmin, they are in good agreement up to the E-region peak altitude, hmE. Below fmin, the GNSS-RO profile is in good agreement with the FIRI profile, indicating that the profiles can measure the D- to E- transition region. The frequency of the E-region peak, foE, showed general agreement between the GNSS-RO and ionosonde measurements; however, the hmE agreement was weaker and the GNSS-RO profiles tend to have an hmE in a narrow altitude range for all profiles. Virtual heights were simulated for the GNSS-RO profiles using a numerical ray tracer for direct comparison with ionosonde observations, which showed agreement for many of the virtual heights near fmin, but also indicated a positive bias in the GNSS-RO virtual heights that may be due to low foE or elevated hmE estimates. For a quiet ionosphere, the shifted GNSS-RO electron density profiles were a good match for both measured ionosonde profiles and modeled FIRI profiles and the method is capable of providing global coverage of the D- and E-regions. Future work will require more data for seasonal and morning–afternoon comparisons as well as comparisons for the disturbed ionosphere when the sporadic-E layer is present.

Small-scale concentrated solar power system with thermal energy storage: System-level modelling and techno-economic optimisation

A dynamic, techno-economic model of a small-scale, 31.5 kWe concentrated solar power (CSP) plant with a dish collector, two-tank molten salt storage, and a sCO2 power block is analysed in this study. Plant solar multiple and storage hours are optimised using a multi-objective genetic algorithm to minimise the levelised cost of electricity (LCOE) and maximise the capacity factor (CF). The optimal LCOE is found to be ranging from 122.7 USD/MWhe to 217.8 USD/MWhe when using optimistic and pessimistic power block cost assumptions. In contrast, the literature reports an LCOE of 193.9 USD/MWhe for a similar 31.5 kWe CSP system without storage. The addition of thermal energy storage in the system configuration dramatically improves the CF from 30.4%—reported in the literature—to 87.2% obtained from the present model. The sensitivity analysis suggests that upscaling the system to 400 kWe reduces the LCOE to 121.9–172.2 USD/MWhe as a result of lower unit power block costs at larger scales. The comparison of CSP and PV-battery systems in three distinct locations—characterised by varying direct and global solar resources—indicates that the CSP system is promising in locations with abundant direct normal irradiance (DNI). For locations with medium DNI resource, the LCOE of PV-battery system is within the margin of uncertainty of CSP system. In low-DNI locations, PV-battery systems proved to be preferable.

Responses of Total Electron Content to Solar Flares over Low and Mid-Latitude Regions during Sun Halo Day

The responses of total electron content (TEC) to solar flares over low- and mid-latitude regions during the sun halo days were investigated. The research is based on GPS data obtained from Bangalore (13.02117°N, 77.57038°E) on May 24, 2021; Cape Town (-33.918861°N, 18.423300°E) on October 26, 2020; and North Dakota (46.55756°N, -96.472300°E) on December 27, 2021, during the solar halo days. The results of this study demonstrate that the values of TEC increased at Bangalore and Cape Town stations during solar halo days as compared to other days. However, over the North Dakota station, TEC during the sun halo day was greater than that on the days before and after the halo day from around 19:00 UT to 23:59 UT hours. During the sun halo over Bangalore and Cape Town stations, positive relative changes in TEC prevail, suggesting that the action of the interplanetary electric field, the prompt penetration electric field, and the disturbance dynamo electric field lead to higher TEC values. However, during the sun halo over North Dakota station, negative relative changes in TEC prevail, which might be related to the consequence that low solar activity with no earth’s field disturbances (a positive Dst) leads to lower ionospheric TEC values. Stations that have greater relative changes in TEC have shown greater power spectrum and global wave spectrum energy. Cape Town has a greater relative change in TEC, a greater power spectrum, and a larger global wave spectrum than Bangalore and North Dakota stations. The values of TEC over three stations are different due to the latitudinal and longitudinal differences in addition to the universal time effects. Finally, solar flares have a major influence on ionosphere electrodynamics, and the upward drift velocity of ionospheres at the low latitude station is more strongly influenced by solar flares due to the effects of the ExB drifts, which induce TEC disturbances during the halo days over the suggested stations.

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.

Tick‐tock… says the moon and the sun: Daily activity patterns of mid‐large‐sized mammals in grassland‐dominated landscapes afforested withEucalyptus

AbstractSpecies can change their activity patterns in response to biotic and abiotic factors, such as sunlight and moonlight. The influence of these variables is a key question for ecological research and biodiversity conservation. The conversion of natural grasslands into tree plantations has reached high levels in temperate South America. These novel ecosystems elicit behavioural changes that can be detrimental or beneficial to wildlife. In this research, we describe the activity patterns of mammals (mid and large‐sized) during sun and lunar phases using camera trap data (2015–2021; 22 926 camera/nights) to assess the effects of the season (warm and cold), habitat type (native forests, grasslands,Eucalyptusplantations) and landscape afforestation degree (10%–90%) in eight landscape samples (5 km radius) at Uruguayan grasslands. This represents the first study of temporal patterns in grassland afforestation worldwide, and concerning solar time and lunar patterns in Uruguay. Across 257 camera trap stations, 5297 independent detections of 13 species were recorded. While there were no significant differences among seasons nor through a landscape gradient of afforestation cover, the local habitat type affected most species temporal niches, finding significant differences between native ecosystems andEucalyptusplantations, despite their close spatial proximity. Cathemeral and nocturnal activity patterns were seen for 12 species, and nine showed lunarphobic or lunarphilic trends according to habitat cover. In tree plantations, five of seven species narrowed their temporal activity compared to native habitats, and four changed their nocturnal activity. These alterations showed that several species adjust their daily activity patterns according to sun and moon phases underEucalyptusplantations, probably reacting to changes in predation risk, hunting pressure, or foraging opportunities. Given that afforestation is projected to grow in southern South America, we must improve our understanding of how species adjust their activities in such novel ecosystems to identify measures needed to increase their conservation opportunities.

Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism

The utilization of sustainable and renewable energy for building heating is pivotal to achieving sustainable development goals. However, the volatility of solar energy poses serious stochastic perturbation problems for heating systems. High-accuracy solar radiation prediction contributes to building energy systems’ economic and reliable operation and provides a basis for the decision-making of investors. To enhance the solar radiation prediction performance, a novel EMD-GRU-Attention (EGA) model through the hybridization of the empirical mode decomposition (EMD), gated recurrent unit (GRU), and attention mechanism is proposed. First, the Kalman filter (KF) is used to denoise the original solar radiation time series, and the EMD method is employed to decompose the denoised sequence into multiple subsequences to minimize the complexity and unpredictability of the solar radiation data. Afterward, the subsequences are modeled by the GRU model optimized via an attention mechanism (GRU-A) to predict solar radiation. To rigorously verify the multi-step prediction performance of the developed hybrid model, extensive comparative experiments are conducted by building several deep learning models, including RNN, GRU, EMD-GRU, and GRU-A. Afterward, a seasonal analysis is also conducted. The results show that the proposed EGA model has satisfactory performance in four seasonal datasets, especially in the autumn dataset. In terms of the one to three-step solar radiation forecasting task, the R2 of the proposed model is 0.983, 0.972, and 0.960, respectively. Thus, the feasibility and superiority of the proposed model in the solar radiation prediction task are confirmed. This study is anticipated to contribute to a reliable reference for the practical regulation strategies of solar-based building energy systems.

Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level

Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to their dependence on climate and geography. While numerous studies have focused on solar energy time series, few have specifically addressed the critical task of forecasting solar energy production at the national level. Accurate national-level forecasting is crucial for optimizing energy management, informing policy development, and promoting environmental sustainability. This study aims to address the challenges associated with the significant variability in renewable energy production and its impact on grid stability by improving the accuracy of existing forecasting approaches. To achieve this goal, we evaluate the effectiveness of univariate and multivariate approaches for time series forecasting of national solar energy production data from ESIOS (the Spanish System Operator). Our primary focus is on leveraging external solar variables, such as solar irradiance data. To this end, we propose a methodology to integrate solar irradiance forecasts with historical data from solar power plants in Spain to improve the performance of multivariate models. Subsequently, we compare the performance of classical regression techniques and state-of-the-art deep learning algorithms, presenting univariate and multivariate models for three forecast horizons (1 h, 24 h, and 48 h). Finally, we assess the performance of our best univariate and multivariate models by comparing them with the official forecast of ESIOS. Our findings indicate that the best-performing models are deep-learning multivariate approaches, which benefit from incorporating solar irradiance forecasts, particularly for longer forecast horizons (24 h and 48 h), and avoid the detrimental effects of the Hughes Phenomenon, which seems to hamper non-deep-learning forecasters. The top-performing models, based on Convolutional Networks and Convolutional + Recurrent Neural Networks, outperform ESIOS by reducing mean absolute error by 41% and 47.58%, respectively.

Diurnal variations of tropical cyclone outer region size growth

AbstractVarious aspects of tropical cyclones (TCs) fluctuate with the diurnal cycle. TC size is critical to the extent of its damage, but diurnal variations in the growth of the outer region size have largely remained unexplored. This paper examines the diurnal variations in the growth of the radius of gale‐force winds (34 kt; R34) by analyzing best‐track data from 2001 to 2019 for both North Atlantic TCs and global TCs outside of the North Atlantic region. Statistically significant diurnal variations was found for R34 growth, with the maximum 6‐h growth rate occurring at 2100–0300 local solar time (LST) for North Atlantic TCs, and at 0300–0900 LST for global TCs excluding the North Atlantic which experienced rapid intensification (≥30 knots within 24 h). The higher R34 6‐h growth rates during the night were linked to a larger extent of very deep convective clouds with infrared brightness temperatures <208 K during this time.

Expanded glass sphere/lauryl alcohol composite in gypsum: Physico-mechanical properties, energy storage performance and environmental impact assessment

In recent years, thermal energy storage systems, which are a promising solution to improve the thermal comfort of buildings and reduce energy demand, are a critical issue. This study aims to develop a new shape-stable gypsum composite using expanded glass sphere (EGS) and lauryl alcohol (LA) and analyses its thermal performances. For this purpose, EGS was impregnated with LA in different mass fractions (25 and 50%) by direct impregnation technique. DSC results exposed that melting temperature and melting enthalpy of shape-stable EGS/LA composite were 20.97 °C and 83.1 J/g, respectively. FTIR investigations confirmed the existence of well chemical compatibility between EGS and LA. TGA analysis revealed that the operating temperature of EGS/LA was considerably lower than its thermal degradation temperature. While incorporation of EGS and EGS/LA composite increases water absorption and porosity; caused a decrease in compressive strength, dry unit weight and ultrasound pulse velocity properties. EGS/Gypsum composite with LA (50%) provided a warmer room temperature for more than 12 h between midnight and solar noon hours. It achieved about 2 ℃ warmer room during these hours. After solar noon hours, proposed composite attained about 3.6 ℃ cooler room center temperatures. It provided cooler indoor temperatures for more than 5 h. A warmer indoor temperature was observed for EGS/Gypsum composite with LA (50%) after 18:00 until midnight to be 2 ℃. It can provide a carbon emission saving of around 3–23.2 kg-CO2/kWh, depending on fuel type for a wall cover made of EGS/Gypsum with LA.

A novel adaptive approach for improvement in the estimation of hourly diffuse solar radiation: A case study of China

Liu & Jordan–type models are still widely used to estimate diffuse radiation in various regions because of their simplicity. This paper proposes an adaptive approach of combining the grouping of kt–k data points with Liu & Jordan–type models to estimate the hourly diffuse radiation more accurately. This study was conducted using hourly radiation data measured in three regions in China (Shanghai, Xi'an, and Lhasa). The main novelties reported herein are the introduction of α as a constraint indicator and the proposal of a criterion for grouping kt–k data points. Grouping models, which adopted the functional form of cubic polynomials, were established in three regions based on the grouped data. Compared with the all-data model results, the percentage of mean absolute bias error (MABE%) and relative root mean square error (RMSE%) for the grouping model decreased by approximately 4% in Shanghai and Xi'an and 6% in Lhasa. Furthermore, weather condition analysis showed that the grouping models had the best adaptability on overcast days, followed by cloudy days and sunny days. A similar analysis was performed for solar time, with the best adaptability occurring at sunrise and sunset and the worst occurring at 9 am. The results also suggest that similar working procedures could contribute to hourly diffuse radiation estimation in other fields.