Search Results (427)

Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days after establishment. The conservation and integration of trees in silvopastoral systems reflected a clear anthropogenic influence, evidenced by the preference for species of the Fabaceae family, likely due to their multipurpose nature. Although the altitudinal gradient did not show direct effects on soil properties, intermediate altitudes revealed a significant role of CaCO₃ in enhancing soil fertility. These edaphic conditions at mid-altitudes favored the leaf area development of Brizantha, particularly during the early growth stages, as indicated by significantly larger values (p < 0.05). However, at the harvest stage, no significant differences were observed in physiological or productive traits, nor in foliar chemical components, underscoring the species’ high hardiness and broad adaptation to both soil and altitude conditions. In Brizantha, a significant reduction (p < 0.05) in stomatal size and density was observed under shade in silvopastoral areas, where solar radiation and air temperature decreased, while relative humidity increased. Nonetheless, these microclimatic variations did not lead to significant changes in foliar chemistry, growth variables, or biomass production, suggesting a high degree of adaptive plasticity to microclimatic fluctuations. Foliar ash content exhibited an increasing trend with altitude, indicating greater efficiency of Brizantha in absorbing calcium, phosphorus, and potassium at higher altitudes, possibly linked to more favorable edaphoclimatic conditions for nutrient uptake. Finally, forage quality declined with plant age, as evidenced by reductions in protein, ash, and In Vitro Dry Matter Digestibility (IVDMD), alongside increases in fiber, Neutral Detergent Fiber (NDF), and Acid Detergent Fiber (ADF). These findings support the recommendation of cutting intervals between 30 and 45 days, during which Brizantha displays a more favorable nutritional profile, higher digestibility, and consequently, greater value for animal feeding. Full article

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure—combining a multimode fiber and an apodizator—achieves 85.8% far-field uniformity over a 200 mm aperture. A power–spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50× dynamic range and maintains power control accuracy within ±0.9%. To suppress internal background interference, a BRDF-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 × 10⁻¹² W, ensuring stable detection of a 10⁻¹⁰ W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a ±3° field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression. The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals. Full article

The Earth Radiation Budget (ERB), a measure of the difference between incoming solar irradiance and outgoing reflected and emitted radiant energy, is a fundamental property of Earth’s climate system. The Libera satellite mission will measure the ERB’s outgoing components to continue the long-term climate data record established by NASA’s Clouds and the Earth’s Radiant Energy System (CERES) mission. In addition to ensuring data continuity, Libera will introduce a novel split-shortwave spectral channel to quantify the partitioning of the outgoing reflected solar component into visible and near-infrared sub-components. However, converting these split-shortwave radiances into the ERB-relevant irradiances requires the development of split-shortwave Angular Distribution Models (ADMs), which demand extensive angular sampling. Here, we show how Rotating Azimuthal Plane Scan (RAPS) parameters—specifically operational cadence and azimuthal scan rate—affect the observational coverage of a defined scene and angular space. Our results show that for a fixed number of azimuthal rotations, a relatively slow azimuthal scan rate of 0.5° per second, combined with more time spent in the RAPS observational mode, provides a more comprehensive sampling of the desired scene and angular space. We also show that operating the Libera instrument in RAPS mode at a cadence between every fifth day and every other day for the first year of space-based operations will provide sufficient scene and angular sampling for the observations to achieve radiance convergence for the scenes that comprise more than half of the expected Libera observations. Obtaining radiance convergence is necessary for accurate ADMs. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Meiyu is a critical component of the summer rainy season over the Yangtze–Huaihe River Basin (YHRB) in China, and the Meiyu onset date (MOD), serving as a key indicator of Meiyu, has garnered substantial attention. This article demonstrates an in-phase relationship between MOD and the preceding spring Barents–Kara Seas ice concentration (BKSIC) during 1979–2023. Specifically, the loss of spring BKSIC promotes an earlier MOD. Further analysis indicates that decreased spring BKSIC reduces the reflection of shortwave radiation, thereby enhancing oceanic solar radiation absorption and warming sea surface temperature (SST) in spring. The warming SST persists into summer and induces significant deep warming in the BKS through enhanced upward longwave radiation. The BKS deep warming triggers a wave train propagating southeastward to the East Asia–Northwest Pacific region, leading to a strengthened East Asian Subtropical Jet and an intensified Western North Pacific Subtropical High in summer. Under these conditions, the transport of warm and humid airflows into the YHRB is enhanced, promoting convective instability through increased low-level warming and humidity, combined with enhanced wind shear, which jointly contribute to an earlier MOD. These results may advance the understanding of MOD variability and provide valuable information for disaster prevention and mitigation. Full article

This study examines the roles of air mass advection and solar radiation in shaping daily air temperature anomalies in Warsaw, Poland, from 2008 to 2023. It integrates solar radiation data, HYSPLIT back-trajectories, air temperature measurements, and machine learning methods, which are key atmospheric factors contributing to temperature anomalies in different seasons. Radiation dominates during warm seasons, while advection-related geographic factors are more influential during winter. Increased solar radiation is observed across all seasons during high-positive temperature anomalies (exceeding two standard deviations). In contrast, cold anomalies in summer are accompanied by strong negative solar radiation anomalies (−136.3 W/m²), while winter cold events may still coincide with positive radiation anomalies (25.7 W/m²). Very slow circulation over Central Europe, which occurs twice as often in summer as in winter, leads to positive temperature (1.3 °C) and negative radiation (−2.1 W/m²) anomalies in summer and to negative temperature (−1.9 °C) anomalies and slightly positive radiation (0.3 W/m²) anomalies in winter. The seasonal variability in the spatial origin of air masses reflects shifts in synoptic-scale circulation patterns. These findings highlight the importance of considering the combined influence of radiative and advective processes in driving temperature extremes and their seasonal dynamics in mid-latitude climates. Full article

This study provides a comprehensive spatiotemporal analysis of sand and dust storms (SDSs) in Uzbekistan using ground-based meteorological data from 2010 to 2023. The results reveal significant spatial heterogeneity in the SDS activity, with the highest frequency of SDS days observed in the southern and western regions, including Surkhandarya, Kashkadarya, Bukhara, Khorezm, and Republic of Karakalpakstan. In the most vulnerable areas, such as Karakalpakstan, Surkhandarya, and Kashkadarya, the annual number of SDS days can exceed 80 in certain years, reflecting a high recurrence of extreme dust events in certain climatic zones. About 53% of the SDS events were regional, affecting several stations, while 47% were localized, indicating a combination of large-scale dust transport and localized emissions. Seasonal patterns showed a peak SDS activity between March and August, coinciding with the dry season characterized by elevated temperatures, reduced soil moisture, and intense agricultural activity, all of which contribute to the surface exposure and increased vulnerability. This study found a significant variation in the event duration across regions, with Karakalpakstan and Surkhandarya experiencing the highest proportion of prolonged events due to its orography and persistent southerly wind patterns. Using ERA5 data and a decision tree regressor, the analysis identified the wind direction and mean wind speed as the most influential meteorological factors, followed by the maximum wind speed and soil temperature, with other variables such as solar radiation and soil moisture playing moderate roles. This study highlights the importance of regional wind patterns and geomorphology in SDS formation, with prevailing wind directions from the northwest, west, and south. The integration of the ERA5 reanalysis and machine learning techniques offers significant potential for improving SDS monitoring and studies. Full article

The S-band (i.e., 2–4 GHz) is essential in multiple fields of radio astronomy, ranging from pulsar and solar studies to investigations of the early universe. The Italian 64 m fully steerable Sardinia Radio Telescope (SRT) is a system designed to operate in a wide frequency band ranging from 300 MHz to 116 GHz. Recently, the Astronomical Observatory of Cagliari (OAC) has been developing a new cryogenic seven-beam S-band radio receiver. This paper describes the design, fabrication and electromagnetic characterization of the feed horn for this new receiver. It has been designed to observe the sky in the 3–4.5 GHz frequency range and it will be composed of seven feed horns arranged in a regular hexagonal layout with a central element. The feed horns are optimized for placement in the primary focus and consequently illuminate the 64 m primary mirror of the SRT. The electromagnetic characterization of the single feed horn is crucial to verify the receiver’s performance; for this reason, a single feed horn has been manufactured to compare the measured reflection coefficient and the radiated far-field diagram with the results of the electromagnetic simulations, performed using the CST^® Suite Studio 2024 and Ansys HFSS^® Electromagnetics Suite 2021 R1 (To make the S-parameters and the radiation diagram measurement procedure feasible, the single feed horn has been connected to two adapters: a circular-to-rectangular waveguide adapter and a coax-to-rectangular waveguide adapter. The results of the measurements performed in the anechoic chamber are in very good agreement with the simulated results. Additionally, the feed horn phase center position is evaluated, merging the measurements and simulations results for an optimal installation on the primary focus of the SRT. Full article

Urban forests are remnants of forest habitats within urban areas. Their structural alterations create stressful microclimatic conditions that can influence the morphology of sensitive plants, such as ferns. This study analyzed variations in the morphological traits of ferns in four urban forest sites in central Veracruz, Mexico, considering the microclimatic differences arising from vegetation structure. Temperature, humidity, canopy openness, and radiation were measured, along with eight foliar traits, while assessing the impact of site and habit (terrestrial or epiphytic) on the response. Sites with greater alterations in vegetation structure exhibited increased canopy openness, solar radiation, temperature, and a higher number of days with lower relative humidity. In these sites, leaves showed an increase in dry matter content and vein density, indicating a greater investment in resource storage and structural resistance. In the less-disturbed sites, terrestrial ferns demonstrated larger leaf area and specific leaf area, suggesting greater growth potential. Conversely, epiphytes generally had smaller leaves, which could represent an adaptive advantage for these species. The results also suggest a process of biotic homogenization within this plant group, reflecting a similar morphological response, except for indicator species restricted to less disturbed sites. Thus, this study reveals that microclimatic variations induced by urbanization significantly affect plant morphology and, ultimately, species diversity. Full article

The objective of this article is to conduct a review to analyze global trends in the use of air pollution models under the influence of climate variability (CV) over urban areas. Five scientific databases were used (2013–2024): Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar. The frequency of citations of the variables of interest in the selected scientific databases was analyzed by means of an index using quartiles (Q). The results showed a hierarchy in the use of models: regional climate models/RCMs (Q3) > statistical models/SMs (Q3) > chemical transport models/CTMs (Q4) > machine learning models/MLMs (Q4) > atmospheric dispersion models/ADMs (Q4). RCMs, such as WRF, were essential for generating high-resolution projections of air pollution, crucial for local impact assessments. SMs, such as GAM, excelled in modeling nonlinear relationships between air pollutants and climate variables. CTMs, such as WRF-Chem, simulated detailed atmospheric chemical processes vital for understanding pollutant formation and transport. MLMs, such as ANNs, improved the accuracy of predictions and uncovered complex patterns. ADMs, such as HYSPLIT, evaluated air pollutant dispersion, informing regulatory strategies. The most studied pollutants globally were O₃ (Q3) > PM (Q3) > VOCs (Q4) > NOx (Q4) > SO₂ (Q4), with models adapting to their specific characteristics. Temperature emerged as the dominant climate variable, followed by wind, precipitation, humidity, and solar radiation. There was a clear differentiation in the selection of models and variables between high- and low-income countries. CTMs predominated in high-income countries, driven by their ability to simulate complex physicochemical processes, while SMs were preferred in low-income countries, due to their simplicity and lower resource requirements. Temperature was the main climate variable, and precipitation stood out in low-income countries for its impact on PM removal. VOCs were the most studied pollutant in high-income countries, and NOx in low-income countries, reflecting priorities and technical capabilities. The coupling between regional atmospheric models and city-scale air quality models was vital; future efforts should emphasize intra-urban models for finer urban pollution resolution. This study highlights how national resources and priorities influence air pollution research over cities under the influence of CV. Full article

Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consumer electricity demand to uncertain variables such as temperature and solar radiation, in addition to complex factors such as planned generator outages and operational constraints. Consequently, the prediction of imbalance signals using linear models is inherently challenging and requires the adaptation of more advanced methods in practice. This study comprehensively analyzes imbalance signal dynamics and develops practical forecasting tools using Machine Learning (ML) techniques. By incorporating a diverse range of features—including lagged imbalance data, weather forecast errors specific to Japan, and temporal patterns—we demonstrate that the prediction accuracy of imbalance signals is significantly improved compared to a baseline reflecting random forecasts based on class distribution observed during the initial training period. Furthermore, the proposed approach identifies the key drivers of hourly imbalance signals, while leveraging out-of-sample forecasting models. Based on these findings, we conclude that the use of multiple predictive models enhances the robustness and reliability of our forecasts, offering actionable tools for improving forecasting accuracy in real-world operations and contributing to a more stable and efficient electricity market. Full article

Multi-angle remote sensing observations play an important role in the remote sensing of solar radiation absorbed by land surfaces. Currently, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) teams have successively applied the Ross–Li kernel-driven bidirectional reflectance distribution function (BRDF) model to integrate multi-angle observations to produce long time series BRDF model parameter products (MCD43 and VNP43), which can be used for the inversion of various surface parameters and the angle correction of remote sensing data. Even though the MODIS and VIIRS BRDF products originate from sensors and algorithms with similar designs, the consistency between BRDF parameters for different sensors is still unknown, and this likely affects the consistency and accuracy of various downstream parameter inversions. In this study, we applied BRDF model parameter time-series data from the overlapping period of the MODIS and VIIRS services to systematically analyze the temporal and spatial differences between the BRDF parameters and derived indices of the two sensors from the site scale to the region scale in the red band and NIR band, respectively. Then, we analyzed the sensitivity of the BRDF parameters to variations in Normalized Difference Hotspot–Darkspot (NDHD) and examined the spatiotemporal distribution of zero-valued pixels in the BRDF parameter products generated by the constraint method in the Ross–Li model from both sensors, assessing their potential impact on NDHD derivation. The results confirm that among the three BRDF parameters, the isotropic scattering parameters of MODIS and VIIRS are more consistent, whereas the volumetric and geometric-optical scattering parameters are more sensitive and variable; this performance is more pronounced in the red band. The indices derived from the MODIS and VIIRS BRDF parameters were compared, revealing increasing discrepancies between the albedo and typical directional reflectance and the NDHD. The isotropic scattering parameter and the volumetric scattering parameter show responses that are very sensitive to increases in the equal interval of the NDHD, indicating that the differences between the MODIS and VIIRS products may strongly influence the consistency of NDHD estimation. In addition, both MODIS and VIIRS have a large proportion of zero-valued pixels (volumetric and geometric-optical parameter layers), whereas the spatiotemporal distribution of zero-valued pixels in VIIRS is more widespread. While the zero-valued pixels have a minor influence on reflectance and albedo estimation, such pixels should be considered with attention to the estimation accuracy of the vegetation angular index, which relies heavily on anisotropic characteristics, e.g., the NDHD. This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. The method used in this study also helps improve the theoretical framework for cross-sensor product consistency assessment and clarify the uncertainty in high-precision ecological monitoring and various remote sensing applications. Full article

The formation of avalanche hazards in mountainous regions is largely influenced by slope insolation and albedo. This paper presents a quantitative analysis of how solar radiation, surface reflectivity (albedo), temperature, and snow cover affect avalanche formation depending on slope aspect (north-, south-, east-, and west-facing). This study is based on remote sensing data from MODIS, ERA5-Land, CHIRPS, and a digital terrain model for the winter periods from 2000 to 2024. The results show that north-facing slopes have higher albedo values (up to 0.95) and greater snow cover stability (30–50%), which contributes to increased avalanche risk, especially at temperatures above −5 °C. South-facing slopes are characterized by lower albedo values (around 0.20–0.40) and more intense snowmelt, which reduces the likelihood of avalanches. Regression analysis revealed a strong positive correlation between snow depth and avalanche risk (r = 0.87), as well as a moderate negative correlation between temperature and snow cover stability (r = −0.25). The influence of albedo on avalanche risk was found to be indirect, acting through its impact on the surface energy balance. The resulting avalanche risk map demonstrated high accuracy (overall agreement: 86%; Kappa coefficient: 0.72), highlighting the effectiveness of an integrated approach based on geophysical and climatic parameters. The data obtained can be used to support avalanche safety management and slope assessment in the context of climate change. Full article