搜索结果：Weather-monitoring

Global Navigation Satellite Systems (GNSS) are highly affected in equatorial regions, especially due to the formation of Equatorial Plasma Bubbles (EPBs), which cause disturbances in the ionosphere resulting in different forms of signal degradation. Despite Colombia's privileged geographic position, its limited monitoring infrastructure hinders the detection and mitigation of these effects. This study proposes the development of a Low-Cost Scintillation Laboratory (LCSL) using a cognitive radio-based approach for real-time scintillation monitoring, aimed at improving GNSS reliability. The system was designed following a Systems Engineering methodology, defining functional architectures and constraints. A communication system model was developed to account for EPBs' effects on GNSS signals, while cognitive radio algorithms within a Software-Defined Radio (SDR) framework enabled real-time detection, monitoring, and alert generation. To implement this approach, monitoring stations were deployed in Bogotá, Cartagena, and Santa Marta utilized low-cost GNSS receivers integrated with Machine Learning (ML) algorithms for the automatic classification of scintillation events. Additionally, the system's accuracy was validated by comparing experimental data with historical records from the Geophysical Institute of Peru (IGP). The results demonstrated that the integration of cognitive radio and ML-based detection enhanced precision and adaptability compared to traditional methods. The network of monitoring stations effectively validated the system's performance, providing valuable insights into equatorial ionospheric dynamics. This study contributes to the advancement of monitoring methodologies and highlights the importance of accessible infrastructure for mitigating EPB effects on GNSS, ultimately fostering more resilient navigation and communication systems.

Weather radar reflectivity images play a critical role in reliable weather monitoring and forecasting. However, inherent factors such as ground clutter, sea clutter, and electromagnetic interference frequently introduce nonprecipitation echoes (NPEs) into these data, posing significant challenges for accurate precipitation detection. A promising solution is leveraging deep learning networks to identify and remove NPEs using satellite observations. To enhance the practicality of these models, recent advancements in reparameterization technology have shown potential for reducing computational complexity. However, existing methods focus on parallel multibranch reparameterization; the fusion of multiple convolutions into single equivalent convolutions, referred to as multiconvolution reparameterization, is still not explored. In this study, we propose a novel reparameterized NPE removal network (RepNPE-Net), designed to recognize and remove NPEs of radar reflectivity data using multichannel brightness temperature (BT) observations from a geostationary meteorological satellite. Reparameterized NPE removal network (RepNPE)-Net incorporates two innovative dual-stream convolution structure-based modules, including the reparameterized dual-stream convolutional module (RepDCM) and the reparameterized attention dual-stream convolutional module (RepADCM), which synergize standard and depthwise separable (DS) residual convolutional blocks to improve feature extraction and representation capabilities. Within the RepADCM module, a positional efficient local attention (PELA) block is designed to enable the network to focus on spatially significant positional features and enhance model's accuracy. Furthermore, to strengthen the practical application ability of the proposed RepNPE-Net, we introduce hybrid convolution reparameterization (HCR) technology, which consolidates multibranch and multiconvolution operations (e.g., depthwise (DW) and pointwise (PW) convolutions) into single equivalent convolutions during the inference stage, significantly reducing computational complexity without compromising network performance. Experimental results demonstrate that RepNPE-Net outperforms existing methods in both NPE removal accuracy and computational efficiency, highlighting its potential for improving radar data quality and advancing meteorological research and applications.

Globally, feedlots rely on weather data to provide foundational information describing and predicting cattle exposure to heat load throughout the summer months. Anecdotally, in Australia feedlots are installing weather stations in close proximity to office buildings, for ease of maintenance. However, there is limited information available describing the variability of microclimatic conditions within feedlots, nor do recommendations exist regarding the ideal placement of weather stations to provide an accurate representation of the thermal environment experienced by feedlot cattle. The aim of this study was to evaluate the variability between in pen microclimate obtained from within pen data loggers, and weather stations situated at feedlot pens and in close proximity to the feedlot office. A total of six (n = 6) Australian feedlots covering a broad geographical range were enrolled into this study. Each feedlot had an automated weather station located in close proximity to i) office buildings (50-75 m from office) and second station located at ii) feedlot pens (2-10 m from pens). In addition (n = 16) ambient temperature (TA, °C) and relative humidity (RH, %) data loggers were placed within a number of pens at each feedlot. From these data comparisons between within pen, at pen and office climatic conditions were evaluated. The relationships between the climatic variables on the two weather stations located at each feedlot were determined using regression analysis, where linear and quadratic relationships were evaluated. Results from this study provides evidence highlighting the variability of within feedlot microclimate conditions, emphasising the importance of onsite weather monitoring. Weather station placement at feedlots is important to ensure that the data collected provides an accurate representation of the feedlot climate conditions. This study highlights the importance of identifying the ideal location for correct weather stations placement for feedlots. Failing to position onsite weather stations in an appropriate location may result in an under- or over-estimation of heat load that cattle are experiencing, which likely reduces the effectiveness of heat load mitigation strategies utilised.

Sclerotinia sclerotiorum causes Sclerotinia stem rot, or white mold, on multiple economically important crops in Michigan. Soybean farmers and crop consultants in the midwestern United States currently use S. sclerotiorum apothecia prediction models to inform fungicide application timing to optimize disease control and economic return. However, current models have not been validated for use in dry bean or potato and do not account for the effects of irrigation on apothecia development. To improve S. sclerotiorum apothecia prediction, on-site weather data were collected and used to generate new binomial logistic regression (LR) and supervised machine learning (ML) models for irrigated soybean, dry bean, and potato fields. The ML algorithms investigated included decision trees, random forests, and support vectors machines. Decision tree classification models outperformed LR and other ML models, achieving 77% accuracy on testing data. Accuracy increased to 89% when on-site weather data were included, indicating that on-site weather monitoring may be required to reliably predict apothecia presence in irrigated environments. Feature importance analysis identified row shading (the distance the plant canopy extends into the row) as critical for prediction accuracy. The minimum row shading required to trigger apothecia development varied slightly between crop types and row spacings, from 0.15 to 0.21 m. Apothecia density peaked when the soil temperature was 21.51°C and volumetric water content was 11.43 or 19.58%. Additionally, a rapid increase in apothecia presence was observed after canopy closure reached 87%. Future model testing and validation will be required prior to deployment as a decision aid for farmers and crop consultants.

Surface precipitation phase transition is conducive to devastating snowstorms and avalanches yet remains a global challenge due to the scarcity of surface observations. Here, we present the Real-time Precipitation Phase-Intensity Collaborative Retrieval Network (RePPIC-Net), a hybrid AI framework that quantifies surface precipitation phase from satellite observations. By integrating real-time 3D atmospheric physics fields from the AI-driven FuXi model with operational geostationary satellite observations through a hierarchical architecture, our system enables real-time monitoring of surface precipitation phase, as opposed to at least 4-hour latency of current operational systems. Validated against ground stations in China, RePPIC-Net achieves a Critical Success Index for Phase and Detection of 0.1574 (snowfall) and 0.3147 (rainfall) for 0.1-5 mm/h precipitation, outperforming 4-hour latency operational products' respective scores of 0.1001 and 0.3064. The real-time precipitation phase discrimination capability of RePPIC-Net allows the development of a satellite-based surface precipitation phase nowcasting system, meeting the need for 1-3 hour global surface precipitation phase transition warnings. RePPIC-Net provides a replicable blueprint for AI-powered real-time weather monitoring, filling a gap in wintertime weather disaster warnings.

The environmental footprint of dairy production is one of the most pressing challenges faced by the industry globally. Our study aimed to develop and validate a cost-effective sensing solution for real-time monitoring of dairy farms' GHG emissions and microclimatic conditions. Each of the integrated sensing nodes was equipped with carbon dioxide (CO2), methane (CH4), and ammonia (NH3) gas sensors, along with an all-in-one weather sensor. Sensing nodes were validated against gold-standard measurements using open-circuit respiration chambers with individual cows under controlled conditions. The CH4 emissions (133.0 ± 22.5 ppm, mean ± SD) showed an overall correlation (r = 0.46) with the gold-standard respiration chamber (166.0 ± 32.8 ppm) across all 3 d. However, the correlation changed over time, with a strong correlation on d 1 (r = 0.62), a moderate correlation on d 2 (r = 0.35), and a weak correlation on d 3 (r = 0.11). In contrast, sensor node quantified CO2 emissions (905 ± 779 ppm) showed a weaker correlation (r = 0.019, 2,461 ± 346 ppm), indicating the need for further improvements to the sensing node. A wireless network of calibrated sensing nodes was deployed in 3 different locations within a dairy farm: dry cow pen (DCP), feed bunk (FB), and freestall beds (FSB) at a research dairy farm. The CH4 emissions were greater in the DCP (12.5 ± 6.65 ppm) compared with FB (2.80 ± 0.61 ppm) and FSB (2.34 ± 0.62 ppm). The CO2 emissions at the FB were greater (1,498 ± 1,020 ppm) compared with the DCP (534 ± 222 ppm) and FSB (724 ± 517 ppm). The NH3 emissions were highest in the FSB (4.24 ± 0.91 ppm) compared with DCP (2.93 ± 1.35 ppm) and FB (1.10 ± 0.44 ppm). The differences in GHG emissions across the different areas of the dairy farm may be influenced by ambient temperature, humidity, housing conditions, and manure management practices. Our sensing nodes may provide a low-cost, scalable sensing network that can offer a practical solution for continuous GHG monitoring.

Marine oil spills represent a significant environmental pollution incident that severely disrupts the balance and stability of marine ecosystems. SAR (Synthetic Aperture Radar) demonstrates significant potential in oil spill detection due to its all-day and all-weather monitoring capabilities. However, existing deep learning models often overlook subtle textural features during detection, leading to frequent omissions of small-scale oil spill areas in the results. To address the existing challenges, this paper proposes LTF-MSPCNet (Local Texture Features-Multiscale Parallel Convolution). The model is built upon the TransUNet framework, integrating the LBP (Local Binary Pattern) texture feature extraction method into a multi-scale large-kernel convolution module. It further incorporates learnable feature extraction and feature reconstruction mechanisms, guiding the learned texture feature distribution to better approximate the texture characteristics of real SAR images. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is integrated after the decoder's upsampling stage to suppress background noise, thereby enhancing both the robustness and segmentation performance of the model. Experiments were conducted using carefully selected and preprocessed Sentinel-1 SAR images, including 1655 training samples and 370 testing samples. The proposed model achieves 86.46% Mean Dice and 92.13% Mean IoU, surpassing the baseline by 2.35% and 1.41%, respectively. Moreover, it also demonstrates consistent performance in practical oil spill detection, with IoU and Dice scores reaching 96.83% and 98.02%. In conclusion, the proposed model achieves accurate segmentation of oil spills and offers a novel technical pathway, providing a fresh research perspective for advancements in the field of oil spill monitoring.

Multimodal person reidentification (ReID), which aims to learn modality-complementary information by utilizing multimodal images simultaneously for person retrieval, is crucial for achieving all-time and all-weather monitoring. Existing methods try to address this issue through modality fusion to absorb complementary information. However, most of these methods are limited to the spatial domain only and usually overlook the intra-/intermodal interactions during feature fusion, resulting in insufficient learning of modality-specific and complementary information. To address these issues, we propose a tri-interaction enhancement network (TIENet), which contains three modules: spatial-frequency interaction (SFI), intermodal mask interaction (IMMI), and intramodal feature fusion (IMFF). Specifically, the SFI boosts the modality-specific representation by integrating the amplitude-guided attention mechanism into the phase space, combined with spatial-domain convolution to achieve fine-grained information learning. Meanwhile, the IMMI enhances the richness of the feature descriptors by embedding the intermodal relationships to preserve complementary information. Finally, the IMFF module considers the structure of the human body and integrates intramodal contextual information. Extensive experimental results demonstrate the effectiveness of our method, achieving superior performances on RGBNT201 and MARKET1501_RGBNT datasets.

As robots undertake increasingly complex tasks, such as real-time visible image sensing, environmental analysis, and weather monitoring under harsh conditions, design of an appropriate robot shell has become crucial to ensure the reliability of internal electronic components. Several key factors, such as the cooling efficiency, visible transparency, mechanical performance, and weathering resistance of the shell material, are proposed in this research to ensure future robot functionality. In this study, a polymeric double-layered shell for fabrication by stereolithography 3D printing was designed, featuring a porous outer layer and a spherical inner shell. The inner spherical shell provides approximately 90% transmission in the visible to near-infrared wavelength range (450-1050 nm) and ensures the proper functioning of the optical devices, such as cameras, lidar, and solar cells, inside the robot. In addition, the inner shell material displays high emittance in the mid-infrared range (5-20 μm) to facilitate effective radiative cooling and protect the robot control system from thermal damage. The 3D-printed inner shell is exposed to a real environment for three months, and its stable optical and mechanical performance confirms its weather resistance ability. Moreover, the 3D-printed outer robot shell promotes mechanical strength while the robot is moving. The optimal 50% porous outer shell is designed to protect the inner shell from continuous moving impact. Finite element simulations are also used to show that the 50% porosity of the outer shell significantly reduces the strain energy upon impact. Compared with a conventional single-layer design with a strain energy of 130 mJ, the double-layered shell with 50% porosity exhibits a reduced strain energy of 22.09 mJ. This double-layered design, which offers excellent weather resistance, high visible transparency, and effective radiative cooling, is promising for future applications in both land and water robot shells.

Investigating wind energy resources is the main goal of the current study. Three tiny Wind Turbine Generators (WTGs) are incorporated into four different system models using Homer Pro Tool to simulate and optimise a 4 × 3 system configuration, producing twelve operational scenarios. A review of real-time load data and the use of precise wind resource data from an operating Weather Monitoring Station (WMS) situated in the study area marked the beginning of the procedure. Based on the capacity factor (Cf) of six small wind turbines, three small wind turbines-Excel-10, Montana-3310, and SD6-701 are chosen. The three tiny turbines are incorporated into four hybrid optimum system models that combine battery, grid, and generators to reduce wind intermittency and power outages. The winning system architecture's results are evaluated. Results show that the combination of hybrid wind turbines accounts for approximately 88.4% of utility grid electricity curtailment. To meet the local net demand of 165.44 kWh/day in isolated and grid-connected modes, the cost factors, represented by NPC and LCOE, range from ₹2.1 to ₹3.5 Crore and ₹3.14 to ₹5.78/kWh, respectively, in all four simulated models. Additionally, a thorough sensitivity analysis of every feasible configuration is provided by varying hub height of wind turbine generators to anticipate power output generation and cost parameters.

With the increasing frequency and intensity of extreme weather events, there is a growing need to develop innovative and accessible methods for environmental monitoring. This work presents a solution based on natural trees equipped with a low-cost embedded system. The innovative idea is that measuring only internal temperature of a tree around its trunk, it is possible to evaluate some weather parameters, such as wind speed and direction. To evaluate this relationship, a multiscale decomposition technique (Discrete Wavelet Transform) and machine learning models (Random Forest, Gradient Boosting, SVM, and Linear Regression) were applied. Among the models tested, Random Forest achieved the best results, demonstrating high accuracy with an error of 6.60% over a continuous monitoring period of 47 days. As an important outcome, the result shows that the proposed tree-based solution is viable for weather monitoring in hard-to-reach places, particularly in the context of forest fire prevention.

The network can continuously track space weather from the Sun's surface to interplanetary space and Earth's atmosphere.

Cardiovascular disease (CVD) is the leading cause of death globally, with a growing impact worldwide, yet the role of environmental exposures such as geomagnetic activity (GMA) is unclear. In recent years, environmental factors such as air pollution, extreme temperatures, and natural disasters have been recognized as triggers for cardiovascular events, prompting interest in other environmental influences. Geomagnetic activity (GMA), defined as fluctuations in Earth's magnetic field driven by solar energy and charged particles, remains understudied due to challenges in its integration into epidemiologic research. This scoping review aimed to map the existing evidence on reported associations between geomagnetic activity and cardiovascular outcomes. A systematic literature search of PubMed, Web of Science, Excerpta Medica Database (EMBASE), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) identified 1,718 articles published between 1964 and 2023. After removal of 147 duplicates and screening against predefined eligibility criteria, 36 studies were included in the final review. These studies examined adult populations, measured geomagnetic activity or related space-weather exposures (geomagnetic storms, solar proton events, high-speed solar wind, cosmic ray intensity, and Schumann resonances), and reported cardiovascular outcomes such as myocardial infarction, acute coronary syndrome, stroke, or mortality. The majority of studies (n = 28) reported significant correlations, while eight found no effect. The incidence of myocardial infarction and acute coronary syndrome increased during geomagnetic storms, solar proton events, and high-speed solar wind, with greater susceptibility observed in individuals with diabetes, metabolic syndrome, or prior cardiovascular disease. The risk of stroke increased with storm intensity, up to 52% during severe events, particularly among young adults. Low geomagnetic activity combined with high cosmic ray activity was consistently associated with increased myocardial infarction incidence and mortality, while more active solar conditions appeared protective. Overall, evidence suggests that geomagnetic and cosmic variability may coincide with cardiovascular risk; however, findings remain inconsistent, and many studies rely on ecological designs with uncontrolled factors that limit interpretation. Given that evidence is still emerging, these observations remain preliminary. Standardized prospective studies are necessary to determine underlying mechanisms and assess whether space weather monitoring could benefit cardiovascular risk prediction and public health preparedness.

The magnetometers onboard the Geostationary Operational Environmental Satellites (GOES) provide crucial measurements for space weather monitoring and scientific research. However, periodic arcjet thruster firings introduce contamination in the measured magnetic field, affecting data accuracy. The currently used correction matrix approach mitigates these effects but struggles with transient variations and residual errors. In this study, we present an alternative correction method using XGBoost, a machine learning algorithm, to correct arcjet-induced contamination in the GOES-17 magnetometer data using GOES-18 as ground truth. Using cross-satellite comparisons and supervised learning techniques, our model is effective in reducing artificial disturbances, especially non-linear variations. We found that the XGBoost method works better than the existing correction matrix approach for E and P components, while the correction matrix performs better for the N component. Although some limitations remain due to training data constraints, our results highlight the importance of machine learning to improve magnetometer data quality by recognizing and correcting complex satellite-driven artifacts. The collocation of GOES-17 and GOES-18 provided a unique opportunity for cross-satellite calibration and validation, and with a longer collocation period, the XGBoost method shows significant promise for better correction of operational data, emphasizing the need for such configurations in future satellite missions.

This study aimed to assess the current water quality, identify the sources of fluorescent dissolved organic matter (fDOM), and quantify the CO2 flux from Kaptai Lake surface water. A water quality multiparameter analyzer, a membrane-enclosed pCO2 sensor, and a weather monitoring device were deployed to continuously record data over 48 hours to observe daily and spatial shifts. All measured water quality parameters remained within the acceptable limits set by the Department of Environment (DoE). The three-dimensional excitation-emission matrix (3D-EEM) analysis identified distinct fluorophores at peak A (Ex/Em = 245/404 nm), peak M (Ex/Em = 310/404 nm), peak T (Ex/Em = 280/338-346 nm), and peak Tuv (Ex/Em = 230/338-350 nm). Parallel factor analysis (PARAFAC) modelling further resolved these into protein-like components and fulvic-like substances, specifically C-like and M-like fluorophores, indicating the presence of both microbial and terrestrial sources. Spatial distribution patterns of fDOM intensity suggest variability driven by localized pollution sources across the lake. Optical indices further indicated that the fDOM components were predominantly biologically derived, characterized by low aromaticity, lower molecular weight and size, and were largely influenced by microbial degradation processes. Diurnal monitoring of partial pressure of CO2 (pCO2) in the lake water revealed values ranging from 577 to 1045 µatm. Correspondingly, the CO2 flux (FCO2) varied between 45 and 56 mmol CO2 m ⁻ 2 d ⁻ 1. The positive average FCO₂ indicates that the lake acts as a net source of CO2 to the regional atmosphere. Higher pCO2 levels are linked to lower dissolved oxygen and increased protein-like DOM that fuels microbial respiration, while humic-like DOM helps stabilize carbon by limiting CO2 release.

Synthetic Aperture Radar (SAR), renowned for its all-weather monitoring capability and high-resolution imaging characteristics, plays a pivotal role in ocean resource exploration, environmental surveillance, and maritime security. It has become a fundamental technological support in marine science research and maritime management. However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. To address these challenges, this paper proposes AC-YOLO, a novel lightweight SAR ship detection model based on YOLO11. Specifically, we design a lightweight cross-scale feature fusion module that adaptively fuses multi-scale feature information, enhancing small target detection while reducing model complexity. Additionally, we construct a hybrid attention enhancement module, integrating convolutional operations with a self-attention mechanism to improve feature discrimination without compromising computational efficiency. Furthermore, we propose an optimized bounding box regression loss function, the Minimum Point Distance Intersection over the Union (MPDIoU), which establishes multi-dimensional geometric metrics to accurately characterize discrepancies in overlap area, center distance, and scale variation between predicted and ground truth boxes. Experimental results demonstrate that, compared with the baseline YOLO11 model, AC-YOLO reduces parameter count by 30.0% and computational load by 15.6% on the SSDD dataset, with an average precision (AP) improvement of 1.2%; on the HRSID dataset, the AP increases by 1.5%. This model effectively reconciles the trade-off between complexity and detection accuracy, providing a feasible solution for deployment on edge computing platforms. The source code for the AC-YOLO model is available at: https://github.com/He-ship-sar/ACYOLO.

Urban Air Mobility (UAM) is particularly vulnerable to wind hazards, and conventional weather monitoring tools often do not offer the detailed, real-time information needed for safe operations. This research, which examines the visual perception of the on-board cycloidal scanning LiDAR system in improving UAM safety, is of significant importance. The cycloidal scanning LiDAR system, designed explicitly for on-board integration, delivers high-resolution visual mapping, real-time data processing, and comprehensive environmental scanning with 360° rotational capabilities. Its lightweight design and low power consumption make it well-suited for UAM applications, providing continual visual updates on wind conditions along the flight path. This study underscores the system's effectiveness in providing advanced visual perception for UAM operations. By emphasizing the crucial role of visual perception in identifying and responding to wind hazards, the research highlights the significance of this technology in guaranteeing safe and efficient UAM operations. Striking a balance between real-time visual capabilities and practical considerations such as power, size, and weight is vital to optimizing UAM safety and efficiency.

Unmanned Aerial Vehicles (UAVs) hold immense potential across various fields, including precision agriculture, rescue missions, delivery services, weather monitoring, and many more. Despite this promise, the limited flight duration of the current UAVs stands as a significant obstacle to their broadscale deployment. Attempting to extend flight time by solar panel charging during midflight is not viable due to battery limitations and the eventual need for replacement. This paper details our investigation of a battery-free fixed-wing UAV, built from cost-effective off-the-shelf components, that takes off, remains airborne, and lands safely using only solar energy. In particular, we perform a comprehensive analysis and design space exploration in the contemporary solar harvesting context and provide a detailed accounting of the prototype's mechanical and electrical capabilities. We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. Next, we evaluate the developed prototype in a bench-top setting using artificial light to demonstrate the feasibility of batteryless flight, followed by testing in an outdoor setting using natural light. Finally, we analyze the potential for scaling up the evaluation of batteryless UAVs across multiple locations and report our findings.

This paper presents an analysis of the debris flow phenomena in the Rio Inferno watershed (Municipality of Cesana Torinese, Western Alps, Italy). The annual frequency and magnitude of these events have caused significant damage to the viability of the historic Chaberton Military Road, which is now closed to transit. This study delved into the processes governing debris flows in the Rio Inferno watershed through detailed geomorphological analysis, an unmanned aerial vehicle (UAV) photogrammetric survey, and the elaboration of rainfall data from the nearby weather monitoring stations. The Hydrologic Engineering Center's River Analysis System (HEC-RAS) code was used to simulate debris flow events considering critical precipitations associated with return periods of 20, 50, 100, and 200 years, based on the highly detailed topographical model obtained by means of photogrammetry. The paper highlights the importance of studying debris flow phenomena to implement effective risk mitigation and management strategies, especially in the context of climate change and the increased vulnerability of mountain territories.