Search Results (2,688)

The metric for probing the variation in atmospheric refractive indices is radio refractivity (RR), which is a key factor in determining the losses associated with a radio signal as it traverses from one atmospheric layer to another. Ten years (2015–2024) of surface hourly data of temperature (K), pressure (P), and relative humidity (RH) obtained from ERA-5 reanalysis were used for RR computations based on ITU-R models. Twelve major cities of South Africa were benchmarked for the study. Time series plots of the overall ten-year RR hourly mean were generated for the cities. The correlation coefficient (R) between RR and RH was investigated. The results indicate the highest and lowest RR of 360.94 and 301.09 (N-Units) in Pietermaritzburg and Kimberly, respectively, with a range of 59.85 over the country. In the southern coast, Pietermaritzburg recorded the highest and lowest values of 360.14 and 325.52 (N-Units) at 21:00 and 11:00 hrs., followed by Durban with 348.55 and 339.44 at 17:00 and 10:00 hrs., Bhisho with 346.88 and 320.622 at 00:00 and 11:00 hrs., and Cape Town with 328.54 and 322.47 (N-Units) at 00:00 and 10:00 hrs., respectively. In the central region, Bloemfontein recorded values of 344.97 and 305.58 at 04:00 and 13:00 hrs., respectively, while Kimberly recorded 338.06 and 301.09 at 04:00 and 13:00 hrs., respectively. In the northern region, Johannesburg recorded the highest and lowest values of 358.79 and 318.56 (N-Units) at 03:00 and 13:00 hrs., respectively; Pretoria recorded values of 352.25 and 316.76 at 04:00 and 13:00 hrs., respectively; Emalahleni recorded values of 358.79 and 318.95 at 03:00 and 13:00 hrs., respectively; and Polokwane recorded values of 357.59 and 320.82 at 03:00 and 13:00 hrs., respectively. Mahikeng recorded values of 346.70 and 311.37 at 04:00 and 13:00 h, while Mbombela recorded values of 360.11 and 329.17 (N-Units) at 00:00 and 12:00 h, respectively. The implications of these results are a higher refractive attenuation effect of terrestrial transmitted radio signals in cities with higher RR and during the early morning, evening, and night hours of the day. A high positive (R) of 0.84 to 0.99 was observed between RR and RH across the country. A geo-spatial RR contour map was generated for the study stations for practical applications and could be helpful in cities where the contour passes within South Africa. These findings should be taken into consideration in the design and reappraisal of terrestrial radio-link and power budgets to ensure quality of service. The overall findings provide practical applications for mitigating RR-prone attenuation on terrestrial radio channels, such as Radio and Television broadcasting, GSM, and microwave link systems, among others, across South Africa and other countries with similar geography and climate. Full article

With the rapid advancement of clean energy, wind farm planning and construction are expanding worldwide, increasing the demand for accurate resource assessments. This study investigates the influence of vertical wind characteristics on wind farm siting and energy production, using measured meteorological data from the Hangjinqi wind farm. Results show that both mean wind speed increase substantially with altitude, indicating that upper layers provide richer and more stable wind resources. The estimated annual energy production of the site reaches 23,500 MWh, with capacity factors ranging from 35% to 42%, well above the national average. Seasonal and diurnal variations are evident: wind speeds strengthen during winter and spring, particularly at night, while turbulence intensity peaks in the daytime and decreases with height. Carbon dioxide (CO₂) reduction also increases with hub height, with the most pronounced seasonal reductions in spring (3367.6–5041.1 tCO₂, +49.7%) and winter (3215.7–5380.0 tCO₂, +67.4%), equivalent to several thousand tons of standard coal per turbine annually. Optimal performance is observed at 100–140 m, demonstrating efficient utilization of mid- to high-altitude resources. Nevertheless, discrepancies in turbine performance at different hub heights suggest untapped potential at higher elevations. These findings highlight the importance of incorporating vertical wind characteristics into wind farm siting decisions, and support the deployment of turbines with tower heights ≥140 m alongside intelligent scheduling and forecasting strategies to maximize energy yield and economic benefits. Full article

Following the implementation of the Shanghai Clean Air Act, this study investigates the evolution of air pollution in central Shanghai (Putuo District) by analyzing continuous monitoring data (2016–2020) and chemical speciation of particulate matter (2017–2018). The results confirm a transition toward a “low exceedance rate and low background concentration” regime. However, short-term exceedance episodes persist, generally occurring in winter and spring, with significantly amplified diurnal variations on exceedance days. Distinct patterns emerged between PM fractions: PM₁₀ exceedances were characterized by a single morning peak linked to traffic-induced coarse particles, while PM₂.₅ exceedances showed synchronized diurnal peaks with NO₂, suggesting a stronger contribution from vehicle exhaust. Source apportionment revealed that mineral components (21.61%) and organic matter (OM, 21.02%) dominated in PM₁₀, implicating construction and road dust. In contrast, PM_2.5 was primarily composed of OM (26.73%) and secondary inorganic ions (dominated by nitrate), highlighting the greater importance of secondary formation. The findings underscore that sustained PM_2.5 mitigation requires targeted control of gasoline vehicle emissions and gaseous precursors. Full article

Fuel moisture content (FMC) estimation is essential for wildfire danger assessment and fire behavior modeling. This study quantifies the value of integrating satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP and the Advanced Baseline Imager (ABI) aboard GOES-16 with High-Resolution Rapid Refresh (HRRR) numerical weather prediction data for hourly 10 h dead FMC estimation across the continental United States. We leverage the complementary characteristics of each system: VIIRS provides enhanced spatial resolution (375–750 m), while ABI contributes high temporal frequency observations (hourly). Using XGBoost machine learning models trained on 2020–2021 data, we systematically evaluate performance improvements stemming from incorporating satellite retrievals individually and in combination with HRRR meteorological variables through eight experimental configurations. Results demonstrate that while both satellite systems individually enhance prediction accuracy beyond HRRR-only models, their combination provides substantially greater improvements: 27% RMSE and MAE reduction and 46.7% increase in explained variance (R${}^2$) relative to HRRR baseline performance. Comprehensive seasonal analysis reveals consistent satellite data contributions across all seasons, with stable median performance throughout the year. Diurnal analysis across the complete 24 h cycle shows sustained improvements during all hours, not only during satellite overpass times, indicating effective integration of temporal information. Spatial analysis reveals improvements in western fire-prone regions where afternoon overpass timing aligns with peak fire danger conditions. Feature importance analysis using multiple explainable AI methods demonstrates that HRRR meteorological variables provide the fundamental physical framework for prediction, while satellite observations contribute fine-scale refinements that improve moisture estimates. The VIIRS lag-hour predictor successfully maintains observational value up to 72 h after acquisition, enabling flexible operational implementation. This research demonstrates the first systematic comparison of VIIRS versus ABI contributions to dead FMC estimation and establishes a framework for hourly, satellite-enhanced FMC products that support more accurate fire danger assessment and enhanced situational awareness for wildfire management operations. Full article

Accurate lightning data are critical for disaster warning and climate research. This study systematically compares the Fengyun-4A Lightning Mapping Imager (FY-4A LMI) satellite and the Advanced Time-of-arrival and Direction (ADTD) lightning location network in the Beijing-Tianjin-Hebei (BTH) region (April–August, 2020–2023) using coefficient of variation (CV) analysis, Welch’s independent samples t-test, Pearson correlation analysis, and inverse distance weighting (IDW) interpolation. Key results: (1) A significant systematic discrepancy exists between the two datasets, with an annual mean ratio of 0.0636 (t = −5.1758, p < 0.01); FY-4A LMI shows higher observational stability (CV = 5.46%), while ADTD excels in capturing intense lightning events (CV = 28.01%). (2) Both datasets exhibit a consistent unimodal monthly pattern peaking in July (moderately strong positive correlation, r = 0.7354, p < 0.01) but differ distinctly in diurnal distribution. (3) High-density lightning areas of both datasets concentrate south of the Yanshan Mountains and east of the Taihang Mountains, shaped by topography and water vapor transport. This study reveals the three-factor (climatic background, topographic forcing, technical characteristics) coupled regulatory mechanism of data discrepancies and highlights the complementarity of the two datasets, providing a solid scientific basis for satellite-ground data fusion and regional lightning disaster defense. Full article

Accurate short-term forecasting of solar photovoltaic (PV) power output is essential for efficient grid integration and energy management, especially given the widespread global adoption of PV systems. To address this research gap, the present study introduces a scalable, interpretable ensemble learning model of PV power prediction with respect to a large PVOD v1.0 dataset, which encompasses more than 270,000 points representing ten PV stations. The proposed methodology involves data preprocessing, feature engineering, and a hybrid ensemble model consisting of Random Forest, XGBoost, and CatBoost. Temporal features, which included hour, day, and month, were created to reflect the diurnal and seasonal characteristics, whereas feature importance analysis identified global irradiance, temperature, and temporal indices as key indicators. The hybrid ensemble model presented has a high predictive power, with an R² = 0.993, a Mean Absolute Error (MAE) = 0.227 kW, and a Root Mean Squared Error (RMSE) = 0.628 kW when applied to the PVOD v1.0 dataset to predict short-term PV power. These findings were achieved on standardized, multi-station, open access data and thus are not in an entirely rigorous sense comparable to previous studies that may have used other datasets, forecasting horizons, or feature sets. Rather than asserting numerical dominance over other approaches, this paper focuses on the real utility of integrating well-known tree-based ensemble techniques with time-related feature engineering to derive real, interpretable, and computationally efficient PV power prediction models that can be used in smart grid applications. This paper shows that a mixture of conventional ensemble methods and extensive temporal feature engineering is effective in producing consistent accuracy in PV forecasting. The framework can be reproduced and run efficiently, which makes it applicable in the integration of smart grid applications. Full article

Cities in India experience distinct seasons, including summer, winter and monsoons. the understanding of thermal comfort within modern houses throughout the different seasons is pivotal for determining a passive design strategy for residences, towards carbon neutrality. Long-term investigations were conducted within five typical houses in the warm–humid climate of Kharagpur, India, spanning three seasons from July 2023 to July 2024. These included air temperature (AT), relative humidity (RH), indoor wind speed and globe temperature for calculating standard effective temperature (SET*). The SET* was used in thermal comfort evaluation, focusing on the cooling effects of elevated wind speeds. The results showed that indoor ATs were well stabilized among the houses, ranging from 27 to 32 °C in monsoon, 20 to 23 °C in winter and 30 to 32 °C in summer on average, due to the effects of high thermal mass structure with relatively small openings. Overall, both the house-wise differences (1–2 °C) and diurnal differences (0.5–3 °C) were much smaller than the seasonal differences. It was found that the resultant indoor operative temperatures (OTs) did not fall within the required comfort levels during the summer and monsoons, whereas those of the winter months met the required standard. The current modern Indian houses of high thermal mass structure prevented flexible adaptations to the dynamic seasonal changes as well as changes within a day. The occupants tended to reduce the SET* by increasing the wind speeds with the assistance of mechanical air circulation, thus reducing the perceived AT by 5 °C in summers. Separate design strategies should be adopted seasonally and in different parts of the day, to maintain a thermally comfortable environment for the occupants. Full article

Agrivoltaics—the co-location of photovoltaic energy production with agriculture—offers a promising pathway to address growing pressures on land, food, and clean energy resources. This study evaluates the first agrivoltaic pilot installation in Jordan, located in Amman (935 m above sea level; hot-summer Mediterranean climate), during its first operational year. Two 11.1 kWp bifacial photovoltaic (PV) systems were compared: (i) a south-facing array tilted at 10°, and (ii) a vertical east–west “fence” configuration. The tilted system achieved an annual specific yield of 1962 kWh/kWp, approximately 35% higher than the 1288 kWh/kWp obtained from the vertical array. Seasonal variation was observed, with the performance gap widening to ~45% during winter and narrowing to ~22% in June. As expected, the vertical system exhibited more uniform diurnal output, enhanced early-morning and late-afternoon generation, and lower soiling losses. The light profiles measured for the year indicate that vertical systems barely impede the light requirements of crops, while the tilted system splits into distinct profiles for the intra-row area (akin to the vertical system) and sub-panel area, which is likely to support only low-light requirement crops. This configuration increases the levelized cost of electricity (LCOE) by roughly 88% compared to a conventional ground-mounted system due to elevated structural costs. In contrast, the vertical east–west system provides an energy yield equivalent to about 33% of the land area at the tested configuration but achieves this without increasing the LCOE. These results highlight a fundamental trade-off: elevated tilted systems offer greater land-use efficiency but at higher cost, whereas vertical systems preserve cost parity at the expense of lower energy density. Full article

Cool roof and green roof have been acknowledged as effective heat mitigation strategies for fighting against the urban heat island (UHI). However, empirical data in hot–humid climate are still insufficient. Experimental conventional, cool and green roofs (three types) were established to comprehensively investigate the thermal performances in Hong Kong under typical summer conditions, as retrofitting strategies for an office building. The holistic vertical thermal behavior was investigated. The comparative cooling potentials were assessed. The results reveal a “vertical thermal sequence” in peak temperatures of each substrate layer for the conventional, cool and green roofs on a sunny day. However, local reversion in the thermal sequence may occur on a rainy day. Green roof-plot C (GR_C) demonstrates the highest thermal damping effect, followed by plot B (GR_B), A (GR_A) and the cool roof (CR) in summer. On a sunny day, the thermal dampening effectiveness of the substrates in the three green roofs is consistent: drainage > soil > water reservoir > root barrier. The holistic vertical thermal profiling was constructed in a high-rise office context in Hong Kong. The diurnal temperature profiles indicate all roof systems could effectively attenuate the temperature fluctuations. The daily maximum surface temperature reduction (SDMR) was introduced for cooling potential characterization of the cool roof and green roofs with multiple vegetation types. On a sunny day, the cool roof and green roofs all showed significant cooling potential. SDMR on the concrete tile of the best performing system was GR_C (26 °C), followed by GR_B (22.4 °C), GR_A (20.7 °C) and CR (13.3 °C), respectively. The SDMR on the ceiling ranked as GR_C, GR_B, GR_A and CR, with 2.9 °C, 2.4 °C, 2.1 °C and 2.1 °C, separately. On a rainy day, the cooling effect was still present but greatly diminished. A critical insight of a “warming effect at the ceiling” of the green roof was revealed. This research offers critical insights for unlocking rooftop cooling potential, endorsing cool roof and green roof as pivotal solutions for sustainable urban environments. Full article

The relevance of this study is driven by the increasing requirements for the energy efficiency and indoor comfort of residential and public buildings, particularly in regions with extreme climatic conditions characterized by substantial daily and seasonal temperature fluctuations. Effective management of heat transfer through building envelopes has become a key factor in reducing energy consumption and improving indoor comfort. This paper presents the results of an experimental–numerical investigation of the thermal behavior of an adaptive exterior wall system with a controllable air cavity. Steady-state and transient simulations were performed for three envelope configurations: a baseline design, a design with vertical air channels, and an adaptive configuration equipped with adjustable openings. Quantitative analysis showed that during the winter period, the adaptive configuration increases the interior surface temperature by 1.5–2.3 °C compared to the baseline design, resulting in a 12–18% reduction in the specific heat flux through the wall. In the summer period, the temperature of the exterior cladding decreases by 3–5 °C relative to the baseline, which reduces heat gains by 8–14% and lowers the cooling load. Additional analysis of temperature fields demonstrated that the presence of vertical air channels has a limited effect during winter: temperature differences at the surfaces do not exceed 1 °C. A similar pattern is observed in warm periods; however, due to controlled air circulation, the adaptive configuration provides an improved thermal regime. The results confirm the effectiveness of the adaptive wall system under the climatic conditions of southern Kazakhstan, characterized by high solar radiation and large diurnal temperature variations. The practical significance of the study lies in the potential application of adaptive façades to enhance the energy efficiency of buildings during both winter and summer seasons. Full article

Monitoring stem sap flow is essential for understanding plant water-use strategies and eco-physiological processes in the ecologically fragile karst region. In the study, we continuously monitored four co-occurring species—Cryptomeria japonica var. sinensis (LS), Liquidambar formosana (FX), Camptotheca acuminata (XS), and Melia azedarach (KL)—using the thermal dissipation probe method in a karst farmland-to-forest restoration area. We analyzed diurnal and nocturnal sap flow variations across different growth periods and their responses to environmental factors at an hourly scale. The results showed (1) A “high daytime, low nighttime” sap flow pattern during the growing season for all species. (2) The proportion of nocturnal sap flow was significantly lower in the growing than in the non-growing season. (3) Daytime sap flow was primarily driven by photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) during the growing season. In the non-growing season, daytime drivers were species-specific: relative humidity (RH, 39.39%) for LS; air temperature (Ta, 23.14%) for FX; PAR (33.03%) for XS; and soil moisture at a 10 cm depth (SM1, 25.2%) for KL. Nocturnal flow was governed by VPD and RH during the growing season versus soil moisture (SM1 and SM2) and RH in the non-growing season. These findings reveal interspecific differences in water-use strategies and provide a scientific basis for species selection and afforestation management in the karst ecological restoration of this research area. Full article

Southern Patagonia in Argentina possesses a world-class wind resource; however, its remote location challenges long-term monitoring. This study presents the first long-term Doppler LiDAR-based wind characterization in the region, analyzing six months of high-resolution data at a 100 m hub height. Power for the LiDAR is provided by a hybrid system combining photovoltaic (PV) and grid sources, with remote monitoring. The results reveal two distinct seasonal regimes identified through a multi-model statistical framework (Weibull, Lognormal, and non-parametric Kernel Density Estimation: a high-energy summer with concentrated westerly flows and pronounced diurnal cycles (Weibull scale parameter A ≈ 11.9 m/s), and a more stable autumn with a broad wind direction spectrum (shape parameter k ≈ 2.86). Energy output, simulated using Windographer v5.3.12 (Academic License) for a Vestas V117-3.3 MW turbine, shows close alignment (~15% difference) with the operational Bicentenario I & II wind farm (Jaramillo, AR), validating the site’s wind energy potential. This study confirms the viability of utility-scale wind power generation in Southern Patagonia and establishes Doppler LiDAR as a reliable tool for high-resolution wind resource assessment in remote, high-wind environments. Full article

Accurate day-ahead photovoltaic (PV) power forecasting is essential for secure operation and scheduling in power systems with high PV penetration, yet its performance is often constrained by the coarse spatial resolution of operational numerical weather prediction (NWP) products at the plant scale. To address this issue, this paper proposes an attention-enhanced CNN–LSTM forecasting framework integrated with a spatial downscaling strategy. First, seasonal and diurnal characteristics of PV generation are analyzed based on theoretical irradiance and historical power measurements. A CNN–LSTM network with a channel-wise attention mechanism is then employed to capture temporal dependencies, while a composite loss function is adopted to improve robustness. We fuse multi-source meteorological variables from NWP outputs with an attention-based module. We also introduce a multi-site XGBoost downscaling model. This model refines plant-level meteorological inputs. We evaluate the framework on multi-site PV data from representative seasons. The results show lower RMSE and higher correlation than the benchmark models. The gains are larger in medium power ranges. These findings suggest that spatially refined NWP inputs improve day-ahead PV forecasting. They also show that attention-enhanced deep learning makes the forecasts more reliable. Quantitatively, the downscaled meteorological variables consistently achieve lower normalized MAE and normalized RMSE than the raw NWP fields, with irradiance-related errors reduced by about 40% to 55%. For day-ahead PV forecasting, using downscaled NWP inputs reduces RMSE from 0.0328 to 0.0184 and MAE from 0.0194 to 0.0112, while increasing the Pearson correlation to 0.995 and the CR to 98.1%. Full article

Land surface temperature (LST) is fundamental for monitoring surface energy balance and environmental dynamics, with remote sensing providing the primary means of acquisition. However, directional anisotropy (DA) introduces systematic bias in satellite-derived LST products, particularly over complex landscapes. This study examines the impact of angular effects on LST retrievals from three leading East Asian geostationary satellites (FengYun 4A, FengYun 4B, and Himawari 9) across Hunan Province, China, using integrated comparison with in situ measurements and reanalysis data. Results show that all products exhibit a systematic cold bias, with FY4B achieving the highest accuracy. Diurnal retrieval precision increases with higher solar zenith angles (SZA), while no consistent relationship is observed between viewing zenith angle (VZA) and retrieval accuracy. Notably, the retrieval bias of the FY4 series increases significantly when the sun and sensor are aligned in azimuth, particularly when the relative azimuth angle (RAA) is less than or equal to 30°. Parametric modeling reveals that emissivity kernel-induced anisotropy is the principal driver of significant LST deviations in central Hunan, while solar kernel effects result in LST overestimation in mountainous regions and underestimation in plains. Increases in elevation or vegetation density reduce emissivity-induced errors but amplify errors caused by shadowing and sunlit effects. Emissivity anisotropy is thus identified as the primary source of LST DA. These findings deepen the understanding of LST DA in remote sensing and provide essential guidance for refining retrieval algorithms and improving the applicability of LST products in complex terrains. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article