Search

Background: Numerous studies showed an effect of weather on physical activity (PA) levels in children. However, no study has yet examined the relevance of personal factors in this relationship. Therefore, this study analyzes (1) whether there are systematic interindividual differences in the extent to which weather affects the PA behavior and (2) whether physical literacy (PL) moderates the weather–PA association in children. Methods: A total of 951 children in 12 Danish schools (age 9.76 [1.59] y; 54.3% girls) completed objective PA assessments via accelerometry (moderate to vigorous PA, light PA, and sedentary behavior). Local weather data (precipitation, wind speed, temperature, and sunshine duration) were provided by the Danish Meteorological Institute. Participants' PL was measured employing the Danish version of the Canadian Assessment of Physical Literacy-2. The 4116 accelerometer days underwent longitudinal multilevel analyses while considering their nesting into pupils and school classes (n = 51). Results: Fluctuations in all PA indicators were significantly explained by variations in weather conditions, especially precipitation (P ≤.035). Significant interindividual differences were found for 9 of 12 analytical dimensions, suggesting that weather changes influence PA behavior differently across individuals (especially moderate to vigorous PA, χ2[4] ≥ 11.5, P ≤.021). However, PL moderated the relationship between weather and PA in only 2 of the 48 analytical constellations. Conclusions: Despite the varying impact of weather on PA across individuals, the present study favors a main effect model in which weather and PL exert independent effects on children's PA. The insufficient support for PL as a moderating factor calls for future studies to test alternative mechanisms in the weather–PA association. [ABSTRACT FROM AUTHOR]

Nurseries and greenhouses face challenges of limited water supply and increased demand for irrigation efficiency to minimize runoff and mitigate water loss to the environment. Overhead irrigation systems are among the most widely used methods for container plants, particularly in small container sizes. However, there is a lack of research examining the distribution uniformity (DU) of the most used sprinklers in nursery settings. Our study investigated the DU of different overhead sprinkler types and models commonly used in outdoor nurseries in the United States to guide greater adoption of higher irrigation efficiency technology. Catch-can experiments compared ten different sprinkler models in small (4.6 m × 4.6 m) and large (9.1 m × 9.1 m) square experimental plots in Irvine, California. We measured water volume, wind speed, and operating pressure, and calculated the application rate for 189 test runs conducted between Mar 2020 and May 2023. Our results show that of the models tested, the greatest DU was achieved by the Hunter MP2000 at 276 kPa (DU = 0.78 ± 0.05) in the small spacing, and the Senninger Xcel Wobbler with a 3.97 mm nozzle at 172 kPa (DU = 0.76 ± 0.06) in the large spacing. Wind speed and operating conditions affected the DU and spatial uniformity of irrigation among the ten models, highlighting the importance of maintaining operating pressures at the manufacturer's recommendations to ensure optimal application rates and DU. Together these results offer a quantitative comparison of sprinkler performance at different operating pressure and in a wide range of wind speeds, allowing users to select sprinkler models that best fit their operation and maximize water conservation.

A joint maintenance decision-making framework is proposed to optimise the long-term maintenance plan and lower the maintenance cost for offshore wind farms. The historical wind speed data are screened by using the method of k-means clustering, and Markov chains are established for the wind speed in different seasons. On this basis, the approach of Markov chain Monte Carlo is applied to simulate the distribution of repair vessel's waiting time for maintenance, where the impact of wind speed on maintenance availability is considered. Moreover, the components in wind turbines are divided into four states according to their effective ages, i.e. young, mature, old and failed, respectively. A maintenance decision model is established, with the objective to minimise maintenance cost. Besides, three types of opportunistic maintenance are considered, i.e. failure-based opportunistic maintenance (FBOM), event-based opportunistic maintenance (EBOM) and age-based opportunistic maintenance (ABOM), respectively. The enhanced elitist genetic algorithm (SEGA) is adopted to solve the optimisation problem. The results indicate that among the three types of opportunistic maintenance, ABOM can reduce maintenance cost more effectively, and it is more suitable for long-term maintenance plans of offshore wind farm. [ABSTRACT FROM AUTHOR]

The quantum well diode (QWD) performs a dual role, functioning both as an emitter and a detector due to its unique feature of spectral overlap between emission and detection spectra. This dual functionality positions QWDs as promising candidates in the realm of multifunctional sensors. Furthermore, the well-established maturity of optical fiber communication, grounded in its intrinsic property of total reflection, makes it an ideal transmission medium for QWD sensing signals. Leveraging the coexisting emission and detection capabilities of QWDs, we have constructed a sensing system in this article. This system utilizes a QWD, which is stimulated to emit light, with the emitted light traveling through a specified length of optical fiber. A specialized load-bearing film, featuring an aluminum membrane on its rear to act as a mirror and an object of known weight on its front, induces deformation in the film, thereby altering the characteristics of the reflected light. This modulated light is subsequently captured by the QWD via the optical fiber, enabling the computation of the weight of the object. In this article, the QWD's emission peak is around 522 nm, and its detection range extends from 370 to 530 nm. Furthermore, by employing the appropriate approach, integrating QWD with optical fibers can be extended to sensing and measuring various physical quantities such as temperature, solution concentration, wind speed, and more. The advantages of QWDs include cost-effectiveness, multifunctionality, portability, and environmental friendliness. This technology represents a promising avenue for sensor control in the era of the Internet of Things. [ABSTRACT FROM AUTHOR]

The nearshore wind stress, u∗2 ${u}_{\ast }^{2}$, is examined using machine‐learning models for air‐ocean data collected via new flux buoys deployed across four experiments. Consistent with prior nearshore studies, existing open‐ocean models predict nearshore u∗2 ${u}_{\ast }^{2}$ with a large error of 0.0152 m2/s2. Gaussian Process Regression (GPR) for nearshore u∗BM2 ${u}_{\ast \text{BM}}^{2}$ is developed, reducing errors to 0.0108 m2/s2. Nearshore air‐sea parameterizations are examined with wind speed (61%) and the wind‐wave frequency of encounters (16%) being the most important. A simpler nearshore, GPR‐derived, wind‐dependent‐only model (u∗NSU2 ${u}_{\ast \text{NSU}}^{2}$) is developed, with errors of 0.0135 m2/s2. GPRs, evaluated using identical variables, were applied to nearshore observations, and these observations modeled with open‐ocean formulations for an initial comparison of parameterizations between these two regimes. The parameterizations are similar, though with subtle nonlinear differences. The new nearshore data set and machine‐learning models enhance the accuracy of predictions and understanding of differences from the open‐ocean. Plain Language Summary: Understanding and accurately parameterizing wind stress on the ocean surface is crucial for modeling oceanic and atmospheric phenomena. Despite its significance, nearshore wind stress and its influencing factors remain inadequately understood due to limited in situ observations and the complexity of the problem. To address this gap, ten shallow‐water air‐sea buoys were developed and deployed alongside directional wave buoys in four field experiments, yielding a substantial data set of nearshore wind stress and associated variables. Discrepancies between nearshore and open‐ocean models were observed, with open‐ocean models exhibiting large errors. Machine‐learning models enhance the accuracy of predictions and understanding of the input variables. A new data‐informed machine‐learning model for nearshore wind stress significantly reduced the error by 30%. Each variable parameterization is described, and a simple model for wind stress based solely on wind speed is provided. Furthermore, an examination of wind stress parameterizations revealed similarities and differences between nearshore and open‐ocean, primarily attributed to wind speeds. These findings significantly enhance the accuracy of weather and ocean forecasts, particularly for nearshore regions. Key Points: Ten air‐sea buoys for winds and fluxes, deployed with wave buoys in four experiments, improve nearshore wind stress observation gapsSupervised machine learning enhances nearshore wind stress predictions, yielding parameterized variable responses without preconceptionsNearshore wind stress parameterizations relative to the open‐ocean have similar variable responses though with small nonlinear differences [ABSTRACT FROM AUTHOR]

Reliable and highly resolved information about onshore wind energy potential (WEP) is essential for expanding renewable energy to eventually achieve carbon neutrality. In this pilot study, simulated 60 m wind speeds (ws60m) from a km‐scale, convection‐permitting 3.3 km‐resolution ICON‐LAM simulation and often‐used 31 km‐resolution ERA5 reanalysis are evaluated at 18 weather masts. The estimated ICON‐LAM and ERA5 WEPs are then compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of −1.8 m s−1 (−27%). In contrast, ICON‐LAM shows a ME of −0.1 m s−1 (−1.8%), resulting in a much higher average WEP by 48% compared to ERA5. A combined Global Wind Atlas‐ERA5 product reduces the ws60m underestimation of ERA5 to −0.3 m s−1 (−4.7%), but shows a similar average WEP compared to ERA5 resulting from the WEP spatial heterogeneity. Plain Language Summary: Onshore wind energy is expected to play a major role in the global energy transition. However, reliable and highly resolved information on the onshore wind energy potential (WEP) crucial for expansion planning is missing over southern Africa. This study evaluated high resolution 3.3 km ICON‐LAM atmospheric simulations and 31 km ERA5 reanalysis against 60 m wind speed (ws60m) observations and compared the corresponding derived WEPs. The results show that ERA5 underestimates ws60m by 27%, resulting in a 48% lower WEP assessment than ICON‐LAM, whose ws60m simulation results show a very small bias. Underestimation of wind energy yields may hinder further expansion of wind energy, as less economic performance is expected, which underlines the importance of highly resolved weather data. Key Points: Simulated ERA5 and km‐scale ICON‐LAM wind speeds are evaluated and corresponding southern Africa wind energy potentials are calculatedERA5 underestimates 60 m wind speed, whereas ICON‐LAM produces lower biases in the wind speed simulationsHigher wind energy potentials are revealed from wind speeds simulated by ICON‐LAM compared to ERA5, which is often used for such assessments [ABSTRACT FROM AUTHOR]

This study aims to improve the understanding of geomagnetic storms by utilizing machine learning models and analyzing several heliophysical variables, such as the interplanetary magnetic field, proton density, solar wind speed, and proton temperature. Rather than relying on traditional correlation-based methods, we employ advanced machine learning techniques to examine the complex relationships between these factors and geomagnetic storms. Our analysis covers a large dataset spanning six solar cycles, including the current 25th cycle, to provide comprehensive insights into the dynamics of these storms. Our study highlights the significance of the interplanetary magnetic field as a key predictor of geomagnetic storms, challenging previous beliefs that primarily focused on sunspot activity. By using high-resolution data, we uncover new patterns and provide a more detailed analysis of the factors influencing geomagnetic storms. We emphasize the importance of considering a range of heliophysical variables, such as proton temperature and flow pressure, which offer new insights into the complex dynamics driving these storm events. The application of machine learning models, particularly Random Forest and Gradient Boosting, demonstrated superior predictive accuracy compared to traditional methods. Our results reveal that the Dst-index MIN, scalar B, and alpha/proton ratio are among the most influential factors, accounting for a significant portion of the prediction model's accuracy. These findings underscore the utility of machine learning in identifying critical drivers of geomagnetic activity and enhancing forecast precision. Additionally, our research underscores the need for comprehensive models that can accurately predict geomagnetic storms by integrating various data sources. This machine learning approach not only improves predictive accuracy but also enhances our understanding of the underlying mechanisms of space weather. The insights gained from this study have important implications for both scientific research and practical applications, such as improving early warning systems for geomagnetic storms and mitigating their potential impacts on Earth. [ABSTRACT FROM AUTHOR]

Data collection facilitated by remotely piloted aircraft (RPA) has proven to be revolutionary in many disciplines including for research in extreme environments. Here we assess current use and utility of small multirotor remotely piloted aircraft (RPAs) for the challenging role of facilitating ship-based cetacean research in Antarctica. While such aircraft are now used routinely in sheltered environments in and off Antarctica, a comprehensive literature review found that RPA-mediated cetacean research conducted from ships at sea and outside of the Antarctic Peninsula region was relatively uncommon. In order to determine the potential utility of ship-based multirotor RPA operations for cetacean research, we repeatedly deployed small RPAs during a multidisciplinary research voyage in maritime East Antarctica to collect scientific data contributing to an understanding of krill and krill predator interactions. RPA flight metrics (duration, height, length, speed, distance from ship, battery drainage, satellites acquired) were compared to ship underway environmental sampling data. At a mean duration of 12 minutes, these 139 RPA flights were relatively short yet adequate to achieve the science intended, namely a range of cetacean related data streams including photogrammetry, photo identification, behavioural observations and whale blow sampling in addition to water sampling and collection of general scenic imagery. RPA flight operations were constrained by wind speed but not by air temperature with flights undertaken throughout the full range of air temperatures experienced (down to -9.5°C) but not throughout the full range of wind speeds experienced. For a 12-minute flight duration, battery drainage was around 60% indicating that the RPAs were rarely pushed to their operational limit. There was little evidence that the cold impacted RPA lithium battery performance with estimated maximum flight time within approximately 10% of expected flight time for the RPA platforms most used. Whist small multirotor RPAs are rarely applied to cetacean related research in maritime East Antarctica, we demonstrate their value and potential to deliver data critical to address knowledge gaps that challenge the effective management of both krill and their predators. [ABSTRACT FROM AUTHOR]

Sea ports play a major role in the transport of goods worldwide, and knowledge of wind characteristics in these areas is vital to maintaining safety. However, coastal wind flow can be highly complex and turbulent, necessitating additional analysis. A Doppler lidar providing continuous wind profiles is deployed in the Port of Genoa, Italy, to characterize the mean wind velocity and turbulence properties within the coastal surface layer (40–250 m above ground level). Weather conditions, reanalysis data, and transient wind profiles are combined to analyze wind field characteristics on a day which experienced a thunderstorm. We also utilize a method to identify and categorize sources of turbulence through analysis of lidar derived quantities such as wind shear, turbulent kinetic energy, and vertical skewness. Seasonal variations in the wind properties are investigated by selecting data from June (summer) and December (winter). Differences are found in dominant wind direction and the associated frequency of convective mixing, with onshore winds most common in summer and offshore winds in winter. The measured lidar velocity is also compared against Monin–Obukhov similarity theory predictions, showing satisfactory agreement at low heights but struggling to reproduce observations of the stable atmospheric conditions present during winter. Plain Language Summary: Approximately 40% of the world's population lives in the coastal regions. Similarly, seaports host about 80% of the global maritime transport and therefore play a major role in the transportation of goods and services. However, coastal regions and ports are also windy places due in part to strong temperature differences between land and sea. This study uses a remote sensing instrument—Doppler lidar—to measure mean wind velocity and turbulence characteristics in the Port of Genoa, Italy, between 40 and 250 m above the ground. The study first provides a detailed investigation of a case (7 June 2020) that was characterized by variable weather that included rain, thunderstorms, and intermittent sunshine. Seasonal variations in the wind characteristics above the Port of Genoa are investigated by focusing on June (summer) and December (winter) months. Winds that blow from sea to land are most common in summer whereas the winds that blow from land to sea are dominant in winter. The wind measurements are compared against theoretical predictions that are often employed in numerical weather prediction models. A varying degree of similarity between the measured and predicted values is found. Key Points: Analyses of wind variability during a thunderstorm and seasonal wind patterns using Doppler lidar measurements in the Port of Genoa, ItalyClassification of turbulent sources using lidar‐derived quantities and comparison of wind profiles against theoretical predictionsSeasonal analysis of surface heating induced convective‐mixing sources and their frequency [ABSTRACT FROM AUTHOR]

Least squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional‐order Beetle swarm optimization algorithm is proposed to optimize the relevant parameters. To weaken the negative impact of wind speed volatility on the accuracy of the prediction model, a two‐stage modal decomposition strategy based on extreme‐point symmetric mode decomposition, FO‐BSO algorithm, and VMD is proposed. ESMD is used to decompose the original wind speed time series. The diversity entropy is introduced as the criterion, and the FO‐BSO‐VMD secondary decomposition is applied for the high‐entropy modal components to further weaken the volatility of wind speed. Simulation results show that compared with the original LSSVM model, the ESMD‐FO‐BSO‐VMD‐LSSVM model presented in this study exhibits an average 44.10% increase in the fitting degree of wind speed prediction over three steps, which verifies the superior performance of the model in short‐term multistep wind speed prediction. [ABSTRACT FROM AUTHOR]

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS's performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly. [ABSTRACT FROM AUTHOR]

Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings' outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters. [ABSTRACT FROM AUTHOR]

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The Wind–Vehicle–Bridge (WVB) coupling method is extensively applied in engineering practices for the dynamic analysis of railway bridges. However, research on the dynamic behaviors of the vehicle on curved bridges in wind conditions remains limited due to the complexities of interactions involved. In this study, the railway alignment and an actual curved bridge were chosen and modeled. The framework of the WVB system was detailed, and four excitation cases were investigated, considering mean wind, fluctuating wind and track irregularities. Three distinct scenarios, including vehicle on a curved bridge subjected to Inward Blowing Wind (IBW), Outward Blowing Wind (OBW) and vehicle on a straight bridge subjected to wind, were explored. The main conclusions are as follows: (1) wind direction significantly affects wheel unloading rate, lateral force, and derailment coefficient; (2) under mean wind condition, critical wind speed increases with the vehicle speed increasing under IBW, while it decreases with the vehicle speed increasing under OBW; (3) Combined wind and track irregularities reveal that wind direction significantly influences dynamic responses, and lower speeds are not necessarily equal to safer conditions for vehicles on curved bridges. [ABSTRACT FROM AUTHOR]

Moss spores are present in aerobiological samples, but their low representation, lack of known allergenic properties, and difficult identification have led to their being overlooked by aerobiologists so far. The data about their presence in the atmosphere and the factors that influence them are, however, important from the biodiversity conservation point of view, since they give us information about their ability to spread to new habitats. In this pilot study, we analysed their presence in the atmosphere of Bratislava city, Slovakia (2018–2023), using Burkard volumetric sampler, and determined the most significant factors influencing its temporal distribution. The size category of 13–18 µm was the most represented in the samples. Environmental factors influenced the daily spore concentrations and the characteristics of the whole spore season. The start of the Main Spore Season (MSS) depended mainly on the temperatures in November–January, initiating earlier growth of sporophytes in the following year, while the intensity of the MSS was influenced by high humidity in April, stimulating the formation of spores in sporangia. The daily concentration of airborne moss spores was mostly influenced by the actual temperature and wind speed, promoting the release and dispersal of spores, and precipitation lowering their levels due to the "wash-out" effect, although no intradiurnal pattern was observed. More data from other locations is needed to determine the role of atmospheric spore transport for the conservation of moss species facing anthropogenic climatic change. [ABSTRACT FROM AUTHOR]

We estimate the progenitor and explosion properties of the nearby Type II SN 2023ixf using a synthetic model grid of Type II supernova light curves. By comparing the light curves of SN 2023ixf with the pre-existing grid of Type II supernovae containing about 228000 models with different combinations of the progenitor and explosion properties, we obtain the |$\chi ^2$| value for every model and evaluate the properties of the models providing small values of |$\chi ^2$|⁠. We found that the light-curve models with a progenitor zero-age main-sequence mass of |$10\, {M}_\odot$|⁠ , explosion energy of |$(2\\!-\\!3) \times 10^{51}\:\mbox{erg}$|⁠ , |$^{56}\mbox{Ni}$| mass of 0.04– |$0.06\, {M}_\odot$|⁠ , mass-loss rate of |$10^{-3}$| – |$10^{-2}\, {M}_\odot \:\mbox{yr}^{-1}$| with wind velocity of |$10\:\mbox{km}\:\mbox{s}^{-1}$|⁠ , and dense, confined circumstellar matter radius of |$(6\\!-\\!10) \times 10^{14}\:\mbox{cm}$| match well to the observed light curves of SN 2023ixf. The photospheric velocity evolution of these models is also consistent with the observed velocity evolution. We note that the progenitor mass estimate could be affected by the adopted progenitor models. Although our parameter estimation is based on a pre-existing model grid and we do not perform any additional computations, the estimated parameters are consistent with those obtained by the detailed modeling of SN 2023ixf previously reported. This result shows that comparing the pre-existing model grid is a reasonable way to obtain a rough estimate for the properties of Type II supernovae. This simple way to estimate the properties of Type II supernovae will be essential in the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) era when thousands of Type II supernovae are expected to be discovered yearly. [ABSTRACT FROM AUTHOR]

Downbursts are severe wind systems originating from thunderstorm clouds, and their strong horizontal outflows can pose serious hazards to natural and built environments. In the context of the activities of the European project THUNDERR—Detection, simulation, modelling and loading of thunderstorm outflows to design wind‐safer and cost‐efficient structures—a comprehensive database of full‐scale downburst measurements was built. All records were acquired by bi‐ or tri‐axial ultrasonic anemometers installed in the main ports of the High Tyrrhenian Sea, namely Genova, Livorno and La Spezia, within the European projects 'Wind and Ports' and 'Wind, Ports and Sea'. The very limited space and time structure of downburst outflows makes the available records in nature inadequate for developing models that could be used in the atmospheric science and engineering communities. The database described herein represents a step forward in attempting to fill this gap. The downburst nature of all events contained in the dataset was verified through detailed meteorological analyses, including comparisons with radar and satellite images and lightning recordings. The wind speed records associated with the events detected by the anemometric network are made publicly available through the online repository Zenodo and can be reused for multiple purposes. The dataset is expected to convey an important impulse towards the physical characterization and modelling of downburst winds and their codification into design tools for the assessment of wind loading and its effects on structures and infrastructure. Furthermore, it could serve as a promising, essential tool for researchers and risk‐related insurance companies. [ABSTRACT FROM AUTHOR]

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions. [ABSTRACT FROM AUTHOR]

Supercooled liquid water (SLW) refers to droplets in clouds that remain unfrozen at temperatures below 0 °C. SLW is an important intermediate hydrometeor in the processes of snowfall and rainfall that can modulate the radiation budget. This study investigates the distribution of supercooled cloud water over mainland China using the East Asia–Pacific cloud macro- and microphysical properties dataset (2016), derived from Himawari-8 observations. The results show that the highest frequency of SLW in liquid-phase stratus clouds occur at the eastern slope of the Tibetan Plateau, the western side of the Sichuan Basin. Additional SLW is mostly found in liquid-phase clouds over the Sichuan Basin and its adjacent areas in southern China. In the region with the highest frequency of SLW, the mechanical forcing of the Tibetan Plateau causes the convergence of low-level airflow within the basin, which also carries moisture that is forced to ascend stably, creating a favorable condition for the formation of supercooled clouds. As the airflow continues to ascend, it encounters the mid-to-upper-level westerlies and temperature inversion. At the mid-to-upper level, the westerlies exhibit stronger wind speeds, directing flow towards the basin. Concurrently, the temperature inversion stabilizes the atmospheric stratification, limiting the further ascent of airflow. This inversion can also restrain convection and upward motion within the clouds, allowing for SLW to exist and persist for an extended period. [ABSTRACT FROM AUTHOR]

The Arabian Peninsula (AP) is notable for its unique meteorological and climatic patterns and plays a pivotal role in understanding regional climate dynamics and dust emissions. The scarcity of ground-based observations makes atmospheric data essential, rendering reanalysis and satellite products invaluable for understanding weather patterns and climate variability. However, the accuracy of these products in the AP's desert environment has not been extensively evaluated. This study undertakes the first comprehensive validation of reanalysis products—the European Centre for Medium-Range Weather Forecasts' European Reanalysis version 5 (ERA5) and ERA5 Land (ERA5L), along with Clouds and Earth's Radiant Energy System (CERES) radiation fluxes—against measurements from the Liwa desert in the UAE. The data, collected during the Wind-blown Sand Experiment (WISE)–UAE field experiment from July 2022 to December 2023, includes air temperature and relative humidity at 2 m, 10 m wind speed, surface pressure, skin temperature, and net radiation fluxes. Our analysis reveals a strong agreement between ERA5/ERA5L and the observed diurnal T2m cycle, despite a warm night bias and cold day bias with a magnitude within 2 K. The wind speed analysis uncovered a bimodal distribution attributed to sea-breeze circulation and the nocturnal low-level jet, with the reanalysis overestimating the nighttime wind speeds by 2 m s−1. This is linked to biases in nighttime temperatures arising from an inaccurate representation of nocturnal boundary layer processes. The daytime cold bias contrasts with the excessive net radiation flux at the surface by about 50–100 W m−2, underscoring the challenges in the physical representation of land–atmosphere interactions. [ABSTRACT FROM AUTHOR]

Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km2 or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR's bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km2 resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore (a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; (b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and (c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives. [ABSTRACT FROM AUTHOR]

To confront the growing difficulty along with the increasing complexity and comprehensiveness of airborne system, an airborne system integrating architecture for multiple types of models are proposed. A platform based on virtual data bus and adapters is developed. The platform’s accuracy for single model simulation is tested compared to the model’s original platform using the wind speed model of a real airplane. The platform’s integration ability, a display and control joint model containing PFD, MFD, ND, engine status collecting device, wind speed model and visualization software is established. The result shows this platform is able to efficiently integrate multiple types of models and is capable of supporting the development of complex airborne systems. [ABSTRACT FROM AUTHOR]

Parameterizing momentum flux under light wind conditions poses a challenge in the development of air-sea flux parameterizations. In this study, we evaluated some modifications to air–sea momentum flux parameterizations under light wind conditions, including surface layer effects (smooth flow and capillary waves), gustiness effects, and non-local effects. By coupling these modifications into the Community Atmosphere Model version 6 (CAM6), we conducted six sensitivity experiments to assess their direct impact on air–sea momentum flux under light wind conditions and their indirect impact on the simulation performance in low-latitude sea areas. During the iteration process in model, the complex negative feedback between wind speed and momentum flux led to differences in momentum flux patterns among experiments in the three key sea areas and inconsistency between offline and online test results. Based on the comprehensive evaluation metric Distance between Indices of Simulation and Observation (DISO), we assessed the simulation performance of experiments in the entire low-latitude sea area, the three key sea areas, and under light wind conditions. The discrepancy in the 10-m wind speeds deviation between light wind conditions and medium to strong wind conditions led to a reversal in the ranking of the simulation performance of the six experiments under light wind conditions compared to the entire low-latitude sea area. [ABSTRACT FROM AUTHOR]

The Korean infrasound catalogue (KIC) covers 1999–2022 and characterizes a rich variety of source types as well as document the effects of the time-varying atmosphere on event detection and location across the Korean Peninsula. The KIC is produced using data from six South Korean infrasound arrays that are cooperatively operated by Southern Methodist University and Korea Institute of Geoscience and Mineral Resources. Signal detection relies on an Adaptive F-Detector that estimates arrival time and backazimuth, which draws a distinction between detection and parameter estimation. Detections and associated parameters are input into a Bayesian Infrasonic Source Location procedure. The resulting KIC contains 38   455 infrasound events and documents repeated events from several locations. The catalogue includes many anthropogenic sources such as an industrial chemical explosion, explosions at limestone open-pit mines and quarries, North Korean underground nuclear explosions and other atmospheric or underwater events of unknown origin. Most events in the KIC occur during working hours and days, suggesting a dominance of human-related signals. The expansion of infrasound arrays over the years in South Korea and the inclusion of data from the International Monitoring System infrasound stations in Russia and Japan increase the number of infrasound events and improve location accuracy because of the increase in azimuthal station coverage. A review of selected events and associated signals at multiple arrays provides a location quality assessment. We quantify infrasound events that have accompanying seismic arrivals (seismoacoustic events) to support the source type assessment. Ray tracing using the Ground-to-Space (G2S) atmospheric model generally predicts observed arrivals when strong stratospheric winds exist, although the predicted arrival times have significant discrepancies. In some cases, local atmospheric data better captures small-scale variations in the wind velocity of the shallow atmosphere and can improve arrival time predictions that are not well matched by the G2S model. The analysis of selected events also illustrates the importance of topographic effects on tropospheric infrasound propagation at local distances. The KIC is the first infrasound catalogue compiled in this region, and it can serve as a valuable data set in developing more robust infrasound source localization and characterization methods. [ABSTRACT FROM AUTHOR]

Humid heat and associated heat stress have increased in frequency, intensity, and duration across the globe, particularly at lower latitudes. One of the more robust metrics for heat stress impacts on the human body is wet-bulb globe temperature (WBGT), because it incorporates temperature, humidity, wind speed, and solar radiation. WBGT can typically only be measured using nonstandard instrumentation (e.g., black globe thermometers). However, estimation formulas have been developed to calculate WBGT using standard surface meteorological variables. This study evaluates several WBGT estimation formulas for the southeastern United States using North Carolina Environment and Climate Observing Network (ECONet) and U.S. Military measurement campaign data as verification. The estimation algorithm with the smallest mean absolute error was subsequently chosen to evaluate summer WBGT trends and extremes at 39 ASOS stations with long continuous (1950–2023) data records. Trend results showed that summer WBGT has increased throughout much of the southeastern United States, with larger increases at night than during the day. Although there were some surprisingly large WBGT trends at higher elevation locations far from coastlines, the greatest increases were predominantly located in the Florida Peninsula and Louisiana. Increases in the intensity and frequency of extreme (90th percentile) WBGTs were particularly stark in large coastal urban centers (e.g., New Orleans, Tampa, and Miami). Some locations like New Orleans and Tampa have experienced more than two additional extreme heat stress days and nights per decade since 1950, with an exponential escalation in the number of extreme summer nights during the most recent decade. Significance Statement: Humid heat and associated heat stress pose threats to health in the moist subtropical climate of the southeastern United States. Wet-bulb globe temperature (WBGT) is a robust metric for heat stress but must be estimated using complex algorithms. We first evaluated the accuracy of three WBGT algorithms in the southeastern United States, using measured verification data. Subsequently, we used the most accurate algorithm to investigate WBGT trends and extremes since 1950 in 39 cities. Results showed that summer heat stress has increased throughout the region, especially at night. Increases in the intensity and frequency of extreme heat stress were most prevalent at urban coastal locations in Florida and Louisiana, emphasizing the impacts of increased urbanization and evaporation on heat stress. [ABSTRACT FROM AUTHOR]

The need to reduce global emissions leads us to look for various sources of clean energy. In recent decades, wind technology has advanced significantly, enabling large-scale power generation in both marine and terrestrial environments, as well as the development of mini-wind solutions. However, we often underestimate the capacity of certain human activities and production processes to generate clean energy, wasting their true potential. This work focuses on using artificially generated wind gusts to transform them into clean electricity through small wind turbines. The proposal is developed in four phases: (1) identify activities that generate wind, (2) collect data on wind speed and direction, (3) perform a descriptive statistical analysis of the wind resource, and (4) select the appropriate technology to calculate the electricity generation. The proposal is evaluated using the air flow produced by the air conditioning systems of a data center in Colombia. The results are analyzed from technical, economic, environmental, and political perspectives. Through small wind power, an annual production of approximately 468 MWh is estimated, avoiding the emission of 300 metric tons of CO 2 . [ABSTRACT FROM AUTHOR]

Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance. [ABSTRACT FROM AUTHOR]

In this study, the parameterization of the drag coefficient is improved using remotely sensed wind and wave products during tropical cyclones (TCs) and implemented for wave simulation using a third-generation numeric model, namely, WAVEWATCH-III (WW3). The significant wave height and peak wavelength from SWIM(Surface Waves Investigation and Monitoring) measured wave spectra are collocated with wind speeds derived from the Haiyang-2 (HY-2) constellation, Soil Moisture Active–Passive (SMAP) radiometer, and Advanced Microwave Scanning Radiometer-2 (AMSR2) in 2019 − 2023. Cyclonic winds at a spatial resolution of 0.25° and intervals of 6 h are obtained by fusion remotely sensed winds from the HY-2 scatterometers and radiometers from SMAP and AMSR2. The parameterization of the drag coefficient during TCs is improved using a matching dataset for 100 TCs consisting of fusion winds and SWIM-measured wave spectra. The significant wave height (SWH) is simulated by WW3 using the optimized parameterization in the several input/dissipation source terms, i.e., ST2, ST3, ST4 and ST6. It is found that the accuracy of simulated SWH using optimized parameterization in switch ST2, ST3 and ST4 is worse than that using optimized parameterization in switch ST6. Moreover, taking two parameterizations in switch ST6 of WW3, validation against the measurements from altimeters and the SWH calculated by SWIM-measured wave spectrum during other 20 TCs yields a root mean square error (RMSE) of 0.60 m, and a correlation coefficient (COR) of 0.85 by optimized parameterization, which are better than the 0.68 m RMSE, and 0.80 COR obtained using existing parameterization. Furthermore, the variation in the bias (altimeter minus WW3) shows that the larger bias (> 2 m) at wind speed > 20 m/s or SWH > 6 m is significantly improved using the optimized parameterization formula and fusion remotely sensed winds. [ABSTRACT FROM AUTHOR]

The rotating fan beam scanning observation mechanism of China-France oceanography satellite (CFOSAT) scatterometer provides more comprehensive ocean measurements. However, its data quality is often impacted by rain contamination. In this study, we analysed the effect of rain on CFOSAT SCATterometer (CSCAT) backscatter measurements by collocating CSCAT data, the Integrated Multi-satellitE Retrievals (IMERG) rain data, and the wind reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF). The research findings indicate that, rain exhibits an intensification effect on backscatter at low wind speeds, but this effect gradually diminishes with increasing wind speeds, eventually transitioning to an attenuation effect at a wind speed between 10–20 m·s−1. At extremely high wind speeds above 20 m·s−1, the rain initially increases and then decreases CSCAT normalized radar cross section (σ0) as the rain rate increases. The sensitivity of CSCAT σ0 to rain rate decreases as the incidence angle increases. Its sensitivity to wind speed decreases as the rain rate increases. The azimuthal modulation becomes more pronounced with higher wind speeds and smaller incidence angles. However, rain weakens the asymmetry and anisotropy. Furthermore, the HH polarization exhibits a more pronounced susceptibility to rain compared to the VV polarization. [ABSTRACT FROM AUTHOR]

A method based on attenuation horizontal component and azimuth scale expansion is proposed to extract wind speed from X-band marine radar images. The attenuation horizontal component of each azimuth is first retrieved by ideal attenuation model, and the wind direction is extracted by the azimuthal dependence of the attenuation horizontal component. To overcome the influence of the occlusion area, the azimuth scale expansion method is used to extract the wind direction. Then the attenuation model in the upwind direction is determined according to the attenuation horizontal component. Finally, an empirical function model relationship between the full-range integral area of the attenuation function in the upwind direction and the reference wind speed is established. The radar data used to test the algorithm were collected from a ship in the Yellow China Sea. The real-time reference wind parameters were obtained by an anemometer installed on the main mast of the ship. The experimental results show that the algorithm improves the accuracy of wind speed retrieval, effectively reduces the error caused by outlier interference in radar images, and overcomes the interference of occlusion area by combining the azimuth scale expansion method. [ABSTRACT FROM AUTHOR]

Studies on aerodynamic controls of central-slotted box decks primarily focused on mitigating vortex-induced vibrations (VIV), as this type of deck typically performs well against flutter instability. However, as the span length increases, the critical wind speed of aerodynamic static instability (U cr ) might be lower than flutter critical wind speed. Thus, U cr will determine the overall aerodynamic performance of such bridges. Investigating this instability through wind tunnel testing methods and numerical simulation can be expensive and time-consuming. In this paper, surrogate models using machine learning approaches, specifically artificial neural network (ANN) and extreme gradient boosting (XGBoost), were developed and optimized for fast and reliable prediction for U cr based on wind tunnel tests and simulation data. The results demonstrated that the built surrogate models can predict U cr accurately. The parametric study results showed that the height ratio of wind fairing apex (a/b), wind angle of attack (α), and length of the main span (L) have the most influence on the U cr compared with other parameters. Finally, based on the developed ANN surrogate model and the artificial bee colony (ABC) optimization algorithm, an optimized section was proposed to enhance the section's performance against aerodynamic static instability. [ABSTRACT FROM AUTHOR]

This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to examine the relationship between gully slope slumping erosion and topographic factors, and to provide a theoretical reference for the mechanism of gully slope slumping erosion and erosion disaster monitoring in bare sandstone area. Methods Based on in-situ monitoring and 3D laser scanning technology, the characteristics of slumping erosion and the variation of topographic factors were analyzed. [Results (1) The amount of slumping erosion was significantly correlated with temperature and wind speed, and the correlation coefficients were 0.867 and 0.917. (2) The topographic factor values showed an increasing trend in general, the surface roughness and surface undulations fluctuated more obviously on the slope, and the fluctuations changed slowly in the middle and lower slopes, and the gully density had no obvious regularity. (3) The amount of slumping erosion was significantly correlated with surface roughness and surface fluctuation, and the relationship was polynomial regression, there was no significant correlation with gully density. There was a certain correlation among the topographic factors, and the surface roughness and surface undulations showed a significant positive correlation, and the correlation coefficient was 0.89. [Conclusion] The erosion amount of gully slope in bare sandstone area can be characterized by the numerical fluctuation of surface roughness and surface fluctuation [ABSTRACT FROM AUTHOR]

This work aims to study the spatio-temporal variability of turbidity in Lake Chascomús using 34 years (1987–2020) of Landsat (TM, ETM + , and OLI) and Sentinel-2-MSI optical data and to understand this variability in terms of environmental variables. A semi-analytical algorithm, using reflectance in the red and near-infrared bands, was calibrated for Landsat and Sentinel-2 bands and tested using in situ turbidity measurements. The best performance was found using only the near-infrared band with 12.84% median accuracy and -12.84% bias when comparing in situ radiometric measurements and field data. When satellite-derived turbidity was compared to in situ values, the median accuracy was 31.8% and the bias 13.22%. Monthly climatological turbidity maps revealed spatial heterogeneity in Lake Chascomús, with differences observed between the north-west and south-east regions, particularly in summer and winter. Turbidity showed marked seasonal dynamics, with a minimum in autumn and a maximum in spring. Annual climatological turbidity maps showed significant inter-annual variability. Generalized linear models showed turbidity was positively associated with wind speed and photosynthetic active radiation (26.2% of the variability explained). Remote sensing was found to be a fundamental complement to traditional field-based methods for monitoring water quality parameters and allowing a better description of their spatio-temporal variability. [ABSTRACT FROM AUTHOR]

Stratospheric balloons are platforms with great relevance in space missions to reach scientific observations. The payload thermal analyses of such missions are usually focused on the float phase. However, during the ascent phase, the coolest temperatures of the entire mission may be reached, mainly due to the convective cooling in the tropopause. This can be explained by the combination of relative wind speed and the harsh thermal environment. The aim of this work is to evaluate the impact of the thermal environment on the relative wind speed to determine the worst-case thermal analysis. Therefore, in order to perform a robust payload thermal during the ascent phase, the uncertainty in the thermal environment and the relative velocity must be evaluated. The former are reduced by defining the thermal environment based on real-data. The latter are reduced by evaluating the parameters involved in the ascent rate. For this purpose, a dynamic model has been developed to characterise the ascent rate and the horizontal relative velocity of the balloon-borne system. This tool has been validated with flight data from the REXUS/BEXUS programme, with the BEXUS missions launched from Esrange, Kiruna, Sweden from 2014 to 2018. The thermal analysis performed shows a temperature difference greater than 10 °C depending on the thermal worst-case selection. The work here presented reduces the uncertainties of the stratospheric payloads ascent phase thermal analysis. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To investigate how the curve radius parameter affects the safety of high-speed train operation in a wind–sand environment, researchers used the wind–sand section of the Lanzhou–Urumqi high-speed railway as their research backdrop. The Euler‒Lagrange method was used to discretize the sand particles based on train aerodynamics theory, and the wind–sand flow model of the head–middle– tail three-member formation was established. The accuracy of the model was verified by comparing the data from published papers, and then the train's wind– sand load was calculated and extracted. Subsequently, a dynamic model of a CRH2 train was created, and the dynamic response of the train was estimated by choosing various curve radii and adding wind–sand loads. The results demonstrated that as wind speed increases, the forces acting on the train due to wind rise. Due to the different shapes and positions of the carriages, the wind– sand load on each car differs; the middle car is subjected to greater lift, lateral force, and overturning moment, as well as lower resistance, nodding moment, and head shaking moment, while the head and tail cars move in the opposite direction and, as a whole, are exposed more adverse forces. The study found that when the curve radius increased from 7000 to 9000 m, the derailment coefficient and wheel-rail lateral force are reduced by approximately 29%~32%, along with an approximately 20%~23% decrease in the wheel load reduction rate. When the radius reaches 10000 m, there is a decrease in the derailment coefficient and wheel-rail lateral force of approximately 5%~8%, as well as a reduction in the wheel weight load reduction rate of approximately 3%~6%.It has been concluded that increasing the curve radius can significantly reduce the safety indices in the wind-blown sand environment, thus improving operational safety. However, it should be noted that a radius of 8000~9000 m is appropriate, and the effect of further increase is not obvious. The results of this study can provide certain guidance for the selection of curve radius and the evaluation of the operational safety of high-speed trains in windy–sandy areas. [ABSTRACT FROM AUTHOR]

The loss of fertile soil due to wind erosion has major impacts on the landscape and the environment. Its intensity and extent depend on many factors, where vegetation cover, soil moisture and wind intensity play key roles. Climatic conditions are constantly developing towards a higher extremity. Episodes of drought associated with a higher risk of wind erosion are longer and more frequent. For farmers, this results in higher demands for management in order to increase resistance against the negative effects of wind erosion. Windbreaks effectively protect the soil by reducing wind speed. This paper describes the automation of the current procedure for the evaluation of the spatial structure of windbreaks by means of advanced image signal analysis (2D) and the subsequent use of machine learning for further classification of spatial structure parameters (optical porosity). Software called Windbreak which enables the evaluation of optical porosity, including the assessment of canopy height, has been developed. The program allows working with digital photographs at the original resolution. The output from the Windbreak software can be also used as input values for determining the effectiveness of windbreaks. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air‐assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision‐making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional‐integral‐differential (PID) air velocity control system was developed. RESULTS: The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air‐assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. CONCLUSION: These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Pine wilt disease has caused significant economic, ecological, and social losses in China, but there is a notable lack of research on the dynamic process of its propagation and diffusion over long timescales. This study revealed the spatial and temporal spread of the natural invasion of pine wilt disease through an analysis of long time series at macroscopic scales. We analysed and verified by simulations the driving mechanisms of host and wind fields in the natural spread of pine wilt disease. RESULTS: The research findings indicate that from 1982 to 2019, the number of counties affected by pine wilt disease in the Yangtze River Delta region of China exhibited a pattern of 'steady increase–fluctuation–outbreak'. The host forest played a decisive role in the natural spread of the disease, while the wind field played a supporting role. The study revealed specific contributions from various factors, where host forest landscape connectivity, host forest area share, mean wind speed, and wind frequency accounted for 31.8%, 28.7%, 22.6%, and 8.8%, respectively. The interaction of increased host forest area and increased wind speed can significantly increase the risk of pine wilt disease transmission. To validate these findings, vectorial metacellular automata simulations of pine nematode transmission in the Yangtze River Delta were conducted, yielding results with an accuracy of 0.803. CONCLUSION: By quantifying the contribution of host forest connectivity to the natural spread of pine wilt disease, this research offers a scientific foundation and innovative insights for preventing and controlling its dissemination. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Copyright of Packaging & Food Machinery is the property of Packaging & Food Machinery Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Downslope windstorms are common leeward of the Rocky Mountains across the High Plains of Colorado during October through March and can cause property damage and travel disruptions. This region is renowned for two primary types of strong downslope winds: (1) chinooks that feature warm, dry winds and are often associated with the development of mountain waves, and (2) boras that are colder and often associated with the passage of a mid- and upper-level trough. The National Blend of Models (NBM) is regularly utilized by forecasters at the National Weather Service Forecast Office (WFO) in Boulder and is one tool used by the WFO to forecast downslope windstorms. No study, however, has quantified the performance of the NBM during downslope windstorms along the Colorado Front Range. In this exploratory study, downslope windstorms from October 2020 through May 2021 were identified using ten observation sites along the Colorado Front Range. Windstorms were subsequently analyzed to quantify forecast statistics of wind speeds and gusts during windstorms to provide a better understanding of NBM v4.0 performance. On average, we found that the overall maximum magnitudes of wind speeds and gusts are on average 38% and 35% too low in the NBM output, respectively, when averaged across 24 to 72-h lead times. Weather and Research Forecasting (WRF) model simulations of varying vertical resolution were also completed to determine if WRF, a constituent model of the NBM, could provide a robust representation of select downslope windstorms across the Colorado Front Range. [ABSTRACT FROM AUTHOR]

The relationship between urban heat island (UHI) and land use/land cover (LULC), and local climate zone (LCZ) is apparent and takes rising attention in the current literature. This study aimed to investigate the relationship between meteorological data collected from 30 stations between 2016 and 2022 and Istanbul's LULCs and LCZs. Several notable findings were uncovered, providing light on the UHI phenomenon and its consequences for the city's characteristics. The stations in urban areas (typology A) had higher temperatures than stations in rural/suburban (typology B) and forested landscape (typology C). Those yearly values were ∼1°C for monthly mean temperatures and ∼1.5°C for monthly minimum temperatures. Moreover, urban areas possessed +4 and +2 hot days (35°C and above) for typologies B and C, respectively. Another remarkable result was that stations situated close to water surfaces exhibited a lower tendency to exceed temperatures of 35°C. Furthermore, built-type LCZs wind velocity achieved a lower value than land cover type, and humidity in typology A was 5% and 10% less than the typologies B and C, respectively. Consequently, the southern part of Istanbul emerged as the most vulnerable location to the UHI phenomena, suffering greater temperatures. [ABSTRACT FROM AUTHOR]

Large amounts of dust are generated during digging operations in underground mines, and the transportation of dust is affected by the shape of the roadway section. In order to know the dust transportation law of different cross-section shapes when digging. Fluent software was used to simulate the distribution of wind flow field, dust transportation trajectory, and dust quality concentration in roadways with semicircular arch, three-centered arch, rectangle and trapezium, and then analyze and compare the influence of four cross-section shapes on dust transportation, and use gray correlation analysis to investigate the correlation between the time needed to reduce the dust quality concentration of the roadway to the safe value and the ventilation air volume and air supply distance. The results of the study show that the wind speed in the roadway is in the following order from high to low: rectangle > trapezium > three-centered arch > semicircular arch. Dust particles of each size rubbed each other after blasting, and there were fewer dust particles with larger sizes in the three-centered arch roadway. Both the air supply distance and ventilation air volume impact the dust removal effect; when the air supply distances are 5 m and 10 m, the three-centered arch has a better dust removal effect; when the air supply distances are 15mand 20m, the effect is better in the rectangular roadway and trapezoidal roadway; and when the ventilation air volume is increased, it is the three-centered arch. Through the gray correlation analysis, it can be seen that in order to improve the construction environment in the roadway after blasting, the rectangular section can be chosen when considering the air supply distance, and the three-centered arch can be chosen when considering the ventilation air volume. [ABSTRACT FROM AUTHOR]

In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions. [ABSTRACT FROM AUTHOR]