Search Results (8,859)

Evapotranspiration (ET) refers to the total water vapor flux transported by vegetation and surface soil to the atmosphere. It is an important component of water and heat regulation, and has an impact on plant productivity and water resource management. As a water-shortage region, the Mongolian Plateau is characterized by drought and an uneven distribution of rainwater resources. Understanding the spatiotemporal distribution characteristics of ET on the Mongolian Plateau is important for water resource regulation for climate change adaption and regional sustainable development. This study calculated the spatiotemporal distribution characteristics of the actual ET in the Mongolian Plateau based on the SFE-NP model and generated a surface ET dataset with a spatial resolution of 1 km and monthly temporal resolution from 2001 to 2020. Theil-Sen median and Mann–Kendall trend models were used to analyze the temporal and spatial distribution characteristics of the actual ET over the Mongolian Plateau. This dataset has been validated for accuracy against the commonly used authoritative ET datasets ERA5_Land and MOD16A2, demonstrating high precision and accuracy. This dataset can provide data support for research and applications such as surface water resource allocation and drought detection in the Mongolian Plateau. Full article

Our Sun is the closest X-ray astrophysical source to Earth. As such, it makes for a strong case study to better understand astrophysical processes. Solar flares are particularly interesting as they are linked to coronal mass ejections as well as magnetic field reconnection sites in the solar atmosphere. Flares can therefore provide insightful information on the physical processes at play on their production sites but also on the emission and acceleration of energetic charged particles towards our planet, making it an excellent forecasting tool for space weather. While solar flares are critical to understanding magnetic reconnection and particle acceleration, their hard X-ray polarization—key to distinguishing between competing theoretical models—remains poorly constrained by existing observations. To address this, we present the CUbesat Solar Polarimeter (CUSP), a mission under development to perform solar flare polarimetry in the 25–100 keV energy range. CUSP consists of a 6U-XL platform hosting a dual-phase Compton polarimeter. The polarimeter is made of a central assembly of four 4 × 4 arrays of plastic scintillators, each coupled to multi-anode photomultiplier tubes, surrounded by four strips of eight elongated GAGG scintillator bars coupled to avalanche photodiodes. Both types of sensors from Hamamatsu are, respectively, read out by the MAROC-3A and SKIROC-2A ASICs from Weeroc. In this manuscript, we present the preliminary spectral performances of single plastic and GAGG channels measured in a laboratory using development boards of the ASICs foreseen for the flight model. Full article

Urbanization and industrialization have led to the coexistence of winter haze and summer heat island in some cities in northern China, but the mitigation effect of ventilation corridors is lack of quantitative evaluation. This paper introduces circuit theory into urban climate research. Taking Shenyang as a case study, it comprehensively employs three-dimensional urban landscape pattern indices (including SVF, FAD, and Z0) to guide ventilation corridor construction, establishes an analytical framework for PM_2.5 and LST, and quantifies the environmental benefits of ventilation corridors. The results show that the corridor generated by circuit theory can make 65.14% of path PM lower than the average level of the city; Among the 7 exit paths of wind corridors, the surface temperature of 4 channels is lower than the average level of the city. FAD is positively correlated with Z0 (R² = 0.7) and negatively correlated with SVF (R² = 0.61). Meanwhile, the circuit theory model identifies eight pinch points along ventilation paths. CFD software is employed to simulate atmospheric environments for six typical building layouts to guide subsequent urban planning. Therefore, the reasonable layout of urban morphology indicators and the construction of reasonable ventilation corridors can effectively control the atmospheric particulate pollution and the heat island effect in summer. Full article

Effective communication is essential for professional practice, yet medical curricula rarely incorporate systematic, performance-based training. The Sell Yourself!—Presentation Techniques course was developed to address this gap through a two-day, practice-oriented program integrating rhetorical training, evolutionary psychology, and structured peer feedback. We examined anonymized institutional evaluations from 450 medical students using descriptive statistics and combined inductive–deductive thematic and content coding to gauge the perceived educational utility of the course. The course received a mean satisfaction rating of 9.6/10, with approximately 74% of students assigning the maximum score. Inductive analysis identified interactivity (143 mentions), practical usefulness (76), feedback and improvement (75), positive atmosphere (51), instructor quality (47), and multimedia examples (37) as key strengths, while critiques primarily concerned breaks and scheduling (62), course length and intensity (59), and smaller concerns regarding feedback processes, content structure, and technical issues. Deductive coding indicated perceived improvements across five predefined dimensions: increased confidence, rhetorical fluency, feedback quality, peer recognition, and cultural inclusivity. Structured rhetorical training appears to be well received by learners and may provide a feasible model for embedding communication competence in medical education. These findings also offer a transferable template for integrating performance-based communication training into other programs. However, conclusions are limited by reliance on self-reported perceptions and the absence of a control group or direct assessment of applied communication outcomes. Full article

The reactions of hydrogen transfer by hydroxyl radicals involving polycyclic aromatic hydrocarbons (PAH) are important, because these compounds contribute to environmental pollution and negatively affect human health. Hydroxyl radicals play a key role in atmospheric processes. This study analyzed the influence of the substituent and the number of aromatic rings in the compound on the kinetics of the hydrogen-transfer reaction. This work proposes for the first time a quantitative structure–activity relationship-based statistical framework combining design of experiments and tunneling corrections to predict PAH + ·OH kinetics. The main objective of this research was to identify which molecular features and substituent effects most strongly govern tunneling and reactivity, thereby enabling the rational prediction of PAH behavior in atmospheric and combustion environments. For this purpose, a quantitative structure–activity relationship model was developed using 22 descriptors, and their relationship with the kinetic parameters of the reaction was determined using statistical tools such as design of experiments and partial least squares. Full article

Land surface temperature (LST) is essential for studying land–atmosphere energy exchange, the impact of climate change, and its influence on crop yields and hydrology. Although satellite remote sensing provides large-scale LST data, existing spatiotemporal fusion methods face challenges. Traditional algorithms have difficulty with heterogeneous surfaces, and deep-learning models often produce blurred details and inaccurate temperatures, which limits their use in high-precision applications. This study addresses these issues by developing a Deep-Learning Spatial and Temporal Fusion Model (DLSTFM) for Landsat-8 and MODIS LST imagery in Griffith, Australia. DLSTFM employs a dual-branch structure: one branch is dedicated to dual-temporal fusion, and the other branch is dedicated to multi-source feature fusion. Key innovations include the Spatial Adaptive Feature Modulation (SAFM) module, which performs adaptive multi-scale feature fusion, and the Temperature Adaptive Correction Module (TCM), which makes pixel-wise adjustments using reference data. Experiments demonstrate that DLSTFM significantly outperforms traditional methods and existing deep-learning fusion methods. DLSTFM achieves clearer surface features and a mean absolute temperature error of approximately 2.1 K. The model also demonstrated excellent generalization performance in another test area (Ardiethan) without retraining, showcasing its substantial practical value for high-accuracy LST fusion. Full article

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting framework based on a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN) and an adaptive calibration mechanism to enhance uncertainty quantification and ensure statistically reliable prediction intervals. The framework employs a dual-pathway architecture: a temporal pathway combining Temporal Convolutional Networks (TCN) and multi-head self-attention to capture hierarchical temporal dependencies, and a spatial pathway based on Graph Attention Networks (GAT) to model nonlinear meteorological correlations. A learnable gated fusion mechanism adaptively integrates temporal–spatial representations, and weather-adaptive modules enhance robustness under diverse atmospheric conditions. Multi-quantile prediction intervals are calibrated using conformalized quantile regression to ensure reliable uncertainty coverage. Experiments on a real-world PV dataset (15 min resolution) demonstrate that the proposed method offers more accurate and sharper uncertainty estimates than competitive benchmarks, supporting risk-aware operational decision-making in power systems. Quantitative evaluation on a real-world 40 MW photovoltaic plant demonstrates that the proposed MTSA-QRN achieves a CRPS of 0.0400 before calibration, representing an improvement of over 55% compared with representative deep learning baselines such as Quantile-GRU, Quantile-LSTM, and Quantile-Transformer. After adaptive calibration, the proposed method attains a reliable empirical coverage close to the nominal level (PICP₉₀ = 0.9053), indicating effective uncertainty calibration. Although the calibrated prediction intervals become wider, the model maintains a competitive CRPS value (0.0453), striking a favorable balance between reliability and probabilistic accuracy. These results demonstrate the effectiveness of the proposed framework for reliable probabilistic photovoltaic power forecasting. Full article

Low-level wind shear (LLWS) is a critical aviation hazard that can cause flight disruptions and pose significant safety risks. Despite its operational importance, forecasting LLWS remains a challenging task. To improve LLWS prediction, probabilistic forecasting approaches based on ensemble prediction systems are increasingly used. In this study, LLWS forecasts were generated using a high-resolution, limited-area ensemble model, which allows for the representation of forecast uncertainty and variability in atmospheric conditions. Forecasts for Incheon International Airport were generated twice daily over the period from December 2018 to February 2020. To enhance forecast skill, statistical post-processing techniques, specifically Ensemble Model Output Statistics (EMOS), were applied and calibrated using Aircraft Meteorological Data Relay (AMDAR) observations. Prior to calibration, rank histograms were examined to assess the reliability and distributional consistency of the ensemble forecasts. Forecast performance was evaluated using commonly applied probabilistic verification metrics, including the mean absolute error (MAE), the continuous ranked probability score (CRPS), and probability integral transform (PIT). The results indicate that ensemble forecasts adjusted through statistical post-processing generally provide more reliable and accurate predictions than the unprocessed raw ensemble outputs. Full article

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

This paper presents the concepts of a vehicle pressure regulation ventilation system based on the results of absolute pressure measurements in land transport vehicles: passenger cars, buses and trains. Despite the fact that absolute pressure affects human well-being and health, this parameter is often overlooked in studies assessing thermal comfort. Absolute pressure measurements were taken during normal passenger transport operation. The studies were conducted for various terrain types: lowlands, highlands, and mountains. Absolute pressure fluctuations in land transport depended primarily on altitude, with the largest atmospheric pressure differences recorded in mountains and the smallest in lowlands. A pressure change of 8 hPa within a 24 h period constitutes an unfavorable mechanical stimulus for the human body and causes changes in the excitability of the nervous system. In all measurement series, absolute pressure fluctuations exceeded 8 hPa. Based on the results of absolute pressure measurements and altitude, a simplified model for predicting absolute pressure in transport vehicles was developed. To reduce absolute pressure fluctuations inside passenger land vehicle cabins, a ventilation scheme regulating pressure inside land vehicle cabins was proposed. Full article

Wildfire spread prediction models, including even the most sophisticated coupled atmosphere–wildfire models, diverge from observed wildfire progression during multi-day simulations, motivating the need for measurement-based assessments of wildfire state and improved data assimilation techniques. Data assimilation in the context of coupled atmosphere–wildfire models entails estimating wildfire progression history from observations and using this to obtain initial conditions for subsequent simulations through a spin-up process. In this study, an approach is developed for estimating fire progression history from VIIRS active fire measurements, GOES-derived ignition times, and terrain height data. The approach utilizes a conditional Wasserstein Generative Adversarial Network trained on simulations of historic wildfires from the coupled atmosphere–wildfire model WRF-SFIRE, with corresponding measurements for training obtained through the application of an approximate observation operator. Once trained, the cWGAN leverages measurements of real fires and corresponding terrain data to probabilistically generate fire progression estimates that are consistent with the WRF-SFIRE solutions used for training. The approach is validated on five Pacific US wildfires, and results are compared against high-resolution perimeters measured via aircraft, finding an average Sørensen–Dice coefficient of 0.81. The influence of terrain data on fire progression estimates is also assessed, finding an increased contribution when measurements are uninformative. Full article

A kinetic and thermodynamic multi-phase model has been developed to describe the adsorption of gases on ice surfaces and their subsequent diffusional loss into the bulk ice phase. This model comprises a gas phase, a solid surface, a sub-surface layer, and the ice bulk. The processes represented include gas adsorption on the surface, solvation into the sub-surface layer, and diffusion in the ice bulk. It is assumed that the gases dissolve according to Henry’s law, while the surface concentration follows the Langmuir adsorption equilibrium. The flux of molecules from the sub-surface layer into the ice bulk is treated according to Fick’s second law. Kinetic and thermodynamic quantities as applicable to the uptake of small carbonyl compounds on ice surfaces at temperatures around 200 K have been used to perform model calculations and corresponding sensitivity tests. The primary application in this study is acetic acid. The model simulations are applied by fitting the experimental data obtained from coated-wall flow-systems (CWFT) measurements, with the best curve-fit solutions providing reliable estimations of kinetic parameters. Over the temperature range from 190 to 220 K, the estimated desorption coefficient, k_des, varies from 0.02 to 1.35, while adsorption rate coefficient, k_ads, ranges from 3.92 and 4.17, and the estimated diffusion coefficient, D, changes by more than two orders of magnitude, increasing from 0.03 to 13.0. Sensitivity analyses confirm that this parameter estimation approach is robust and consistent with underlying physicochemical processes. It is shown that for shorter exposure times the loss of molecules from the gas phase is caused exclusively by adsorption onto the surface and solvation into the sub-surface layer. Diffusional loss into the bulk, on the other hand, is only important at longer exposure times. The model is a useful tool for elucidating surface and bulk process kinetic parameters, such as adsorption and desorption rate constants, solution and segregation rates, and diffusion coefficients, as well as the estimation of thermodynamic quantities, such as Langmuir and Henry constants and the ice film thickness. Full article

Lighter-than-air parakites deployed at sea in the close proximity of wind turbines may offer the possibility of mitigating wake losses encountered in large offshore wind farms. Such devices, having an order of magnitude similar to wind turbine rotors, can divert the stronger winds available at high altitudes to the lower level within the atmospheric boundary layer to enhance the wind flow between turbines. Mooring the parakites directly to the offshore wind turbine support structures would avoid the need for additional offshore structures. This paper investigates a novel and simple approach for mooring a parakite to an offshore wind turbine. The proposed approach exploits the lift forces of the inflatable parakite to reduce the tower bending moment at the base of the turbine induced by the rotor thrust. An iterative numerical model coupling the parakite loads to a catenary cable piecewise model is developed in Python 3.12.7 to quantify the bending moment reduction and shear load variations at the wind turbine tower base induced by the different kite geometries, windspeeds, and mooring cable lengths. The numerical model revealed that the proposed approach for mooring parakites can substantially reduce the tower bending loads experienced during rotor operation without considerably increasing the shearing forces. It was estimated that the tower bending moment decreased by 7.7% at the rated wind speed, where the rotor thrust is at its maximum, while the corresponding shear force increased by 0.6%. At higher wind speeds, where the magnitude of the rotor thrust decreases, the percentage reduction in bending moment gradually increases to 51.7% at a wind speed of 24 m/s, with the corresponding shear force increasing by only around 4.6%. Furthermore, while upscaling the parakite augments the tower bending moment reduction, changes in cable length had little effect on bending moment reduction and shear increase. Full article

Accurate precipitation forecasting is paramount for water security and disaster mitigation, yet it remains formidable due to atmospheric stochasticity and the inherent class imbalance in rainfall datasets. This study proposes an integrated “architecture-feature-augmentation” framework to circumvent these limitations. Through a systematic evaluation of CNN-LSTM and Transformer architectures, we delineate distinct performance profiles: The Transformer model, when coupled with feature engineering and physics-informed augmentation, yields a peak F1-score of 0.1429, marking the optimal configuration for harmonizing precision and recall. Conversely, CNN-LSTM demonstrates superior robustness in extreme event detection, consistently maintaining high recall rates (up to 0.90) across diverse scenarios. We identify feature engineering as a critical performance modulator, substantially bolstering CNN-LSTM’s baseline metrics while enabling the Transformer to realize its maximum predictive capacity. Although synthetic oversampling techniques—such as SMOTE and GAN—effectively extend the detection range for heavy precipitation, physics-informed augmentation provides the most consistent performance gains, particularly in multi-class contexts. We conclude that the Transformer, augmented by physical constraints, is the optimal candidate for high-precision requirements, whereas CNN-LSTM, integrated with synthetic augmentation, offers a more sensitive alternative for early warning systems prioritizing recall. These findings provide empirical guidance for advancing extreme weather preparedness and strategic water resource management. Full article

An experimental investigation was conducted to evaluate the statistical properties and scintillation mitigation performance of multi-aperture free-space optical transmission under real-measured atmospheric turbulence. Continuous monitoring of turbulence parameters over a 24 h period showed that the atmospheric coherence length ranged from 3 to 29 cm, indicating that the experimental link operated predominantly under weak-to-moderate turbulence conditions, while a limited number of measurement intervals exhibited relatively strong scintillation and were included for statistical modelling analysis. An 865 m four-channel receiving link was constructed under the measured turbulence conditions to acquire irradiance data for analysis. The results show that the multi-aperture reception significantly suppresses scintillation, reducing the scintillation index from 0.36 to 0.04 under moderate turbulence. The irradiance probability density functions were fitted using lognormal, Gamma–Gamma, exponentiated Weibull, and Málaga (M) distributions. The M distribution exhibited superior adaptability, with fitting accuracy improved by 18.75% under weak turbulence and 13.16% under moderate turbulence. Further analysis shows that the shape parameters of the M distribution vary systematically with turbulence strength, effectively capturing the turbulence-induced evolution of irradiance statistics and providing experimental support for turbulence channel modelling and the optimisation of FSO diversity reception architectures. Full article