Weather Environment Research Articles

Binding and transport of Cr(III) by clay minerals during the Great Oxidation Event

A spike in chromium (Cr) abundance above crustal background in iron formations post-dating the Great Oxidation Event (ca. 2.5-2.3 billion years ago) has been suggested to reflect the evolution of terrestrial aerobic pyrite and siderite oxidation and the initiation of widespread acid rock drainage that would enhance Cr transport from terrestrial weathering environments to the oceans. However, it remains unclear whether Cr was transported in a soluble form (as Cr(III) or Cr(VI)), or bound to particulate surfaces. Here, we experimentally investigate the binding mechanisms of Cr(III) – the typical oxidation state associated with primary igneous minerals such as chromite – to three common soil clay minerals (kaolinite, illite, and montmorillonite) using extended X-ray adsorption fine structure (EXAFS) spectroscopy. Our results demonstrate that Cr(III) precipitates on the clay surfaces over a pH range of 6 to 8 as guyanaite (β-CrOOH) due to the replacement of surface Al-octahedra by paired Cr(III) octahedra. Bidentate bonding with a Cr-Cr interatomic distance of 3.43-3.51 Å indicates the incorporation of Cr(III) into the clay structure. A comparison between Cr(III) adsorption/precipitation onto the three clays and the desorption of Cr(III) from Cr(III)-spiked clays shows that kaolinite has the highest retention capacity for Cr(III), a predictable result given that an entire Al-octahedral sheet is exposed for Cr(III) binding, while illite and montmorillonite only have Al-edge sites. Moreover, Cr(III) was essentially immobilized in our experiments except under very acidic conditions (pH<2). Extending our results to the interpretation of the Cr record in iron formations, we suggest that under intense chemical weathering conditions, not only did acidity promote the solubilization of Cr(III) from primary Cr-bearing minerals, but that parent rocks were more systematically weathered to an advanced state dominated by kaolinite – creating ideal conditions for Cr adsorption. Erosion of regolith that scavenged mobilized Cr(III) could then facilitate transport of Cr(III)-bearing kaolinite to coastal environments where it contributed to the super-crustal Cr abundances above detrital background preserved in ca. 2.5-2.0 Ga iron formations.

Benchmarking the Robustness of Object Detection Based on Near-Real Military Scenes

According to the technical requirements of intelligent development of auxiliary combat system, we construct a visual intelligent test platform. A near-real military scene dataset based on physical rendering is built, which contains 11,000 remote sensing images collected by an analog camera taking pictures in different illumination, weather environment, camera shooting angle, and scene scale condition. Besides, we add a natural style transfer module for a single unmodeled military scene image’s multienvironment generation. We conduct experiments to evaluate the stability of several UAV remote sensing image object detection algorithms. Based on the quality and speed value of the tested algorithms, the adaptability scores in different environments are calculated. Furthermore, we propose a comprehensive evaluation index system of military remote object detection based on a hierarchical model. We envision that our comprehensive benchmark will play a role in the evaluation of algorithm capability for military object detection tasks and the improvement of training algorithm capability.

Equatorward Medium to Large‐Scale Traveling Ionospheric Disturbances of High Latitude Origin During Quiet Conditions

AbstractThis article presents observations of medium to large‐scale traveling ionospheric disturbances (TIDs) originating from high latitudes, and propagating across the equator into the opposite hemisphere in the African‐European sector during geomagnetically quiet conditions between 2010 and 2018. During the study period, four geomagnetically quiet days were selected each month. The Global Navigation Satellite Systems (GNSS) total electron content (TEC) data were used to obtain the two dimensional (2‐D) TEC residuals. We have identified seven transhemispheric TIDs from the days that were analyzed with five originating in the southern and the rest in northern wintertime hemisphere. The TIDs meridional propagation velocities, periods and wavelengths are in the range of 270–322 m/s, 48–80 min and 802–1,296 km, respectively. The horizontal propagation velocities and horizontal wavelengths ranges are cH = 120–274 m/s and λH = 379–1,104 km, respectively. Analysis of the conditions of the space weather environment rules out space weather as a source of the TIDs. While observations of the 4.3 μm brightness temperature (BT) from the Atmospheric Infrared Sounder (AIRS) instrument on board the NASA Aqua satellite show the existence of atmospheric gravity waves (AGWs) in the troposphere‐stratosphere region, it is suggested that the tertiary GWs from orographic forcing may be the likely source of the TIDs observed in this study.

Compositional and Morphological Investigations of Roman Glass from Cremation Deposits at Birdoswald Fort on Hadrian’s Wall, UK

Several different types of burial were identified during the excavation of the Roman military cemetery associated with the fort at Birdoswald, on Hadrian’s Wall (UK). Fragments of glass vessels and glass beads were recovered from many of the cremation deposits, as they were commonly used during cremation rituals, and many of these had been affected by heat. X-ray fluorescence spectroscopy and scanning electron microscopy were used to investigate the raw materials, colorants and opacifiers employed to produce the glass assemblage. Most of the large fragments are transparent with a blue-green colour, with a composition typical of recycled glass. The smaller fragments are from beads and are coloured and sometimes opaque. Colourants and opacifiers characteristic of Roman glass were added in this glass formulation, including cobalt-based compounds (blue glass), copper alloys (green glass), white calcium antimonate, and yellow lead antimonate. The multianalytical approach of this research has allowed for the distinguishing of the extreme depletion of sodium on the surface of the melted glass fragments due to the exposure to high temperatures during the cremation process, followed by surface weathering in a burial environment. Based on the chemical composition of the bulk of the samples, a model of high temperature viscosity of glass was applied in order to assess the cremation temperature in the pyre, providing relevant information about funerary rituals and cremation technology in Roman Britain.

Vehicle Image Detection Method Using Deep Learning in UAV Video.

Traditional machine learning algorithms are susceptible to objective factors such as video quality and weather environment in the vehicle detection of Unmanned Aerial Vehicle (UAV) videos, resulting in poor detection results. A vehicle image detection method using deep learning in UAV video is proposed. The algorithm in this paper treats surveillance video as many frames of images for vehicle detection in the image. First, perform HSV (Hue-Saturation-Value) spatial brightness translation operation on the original sample to increase the adaptability to different light conditions and sample diversity. Then, the Single Shot MultiBox Detector (SSD) model framework is used as the basis for vehicle detection. In order to obtain a better feature extraction effect, focus loss is added to the basic SSD for optimization. Finally, the trained network model is used to analyze the UAV video, and the detection performance is analyzed experimentally. The results show that the vehicle detection rate of this algorithm has reached 96.49%. It can ensure that the vehicle is accurately detected from the drone video.

DBWM: A diluted bitumen weathering model

To help better assist the management of Diluted bitumen (DilBit) spills in marine environment, a model named as DilBit Weathering Model (DBWM) was developed in this study to simulate DilBits weathering in marine environment. The DBWM was developed based on specific algorithms for evaporation, dispersion, biodegradation, as well as density and viscosity changes for DilBit weathering and other widely used algorithms for conventional oil weathering in marine environment. To validate the model, a series of DilBit weathering simulation were conducted and compared with the experimental data. Furthermore, the performance of DBWM was compared with a widely used oil weathering model (Automated Data Inquiry for Oil Spills, ADIOS2). The results demonstrated the feasibility and advantages of the developed DBWM in simulating the weathering of marine DilBit spills. Thus, the proposed DBWM can provide effective decision support to marine DilBit spill management.

Fiber Bragg Grating Smart Material and Structural Health Monitoring System Based on Digital Twin Drive

The damage self‐diagnosis function puts forward higher requirements for the research and development of intelligent structural health monitoring, and in most cases, it is necessary to monitor the load first, especially the monitoring of the impact load. The development of smart materials and structures is based on advanced sensing systems. In order to achieve this purpose, a high‐speed demodulation system based on fiber grating with double long period grating is studied, and then, a damage self‐diagnosis system based on fiber grating is constructed. The system can realize the strain distribution and impact load monitoring of the structure. After quantitative analysis of the signal, an advanced information identification method is used to realize the impact load location. This paper focuses on the data preprocessing process of bridge health monitoring. In view of the characteristics of high data complexity, large amount of data, and many noise components in the process of structural monitoring, this paper adopts basic data cleaning for the original data set, including data dimensionless and missing value processing. Based on the digital twin technology, the composition of the digital twin KNN model of bridge swivel construction monitoring and management is analyzed, and the digital twin system architecture of bridge swivel construction monitoring and management is built. The function display of the monitoring platform, including setting a variety of permission login modes, displaying BIM model, geographic information, and weather environment; monitoring data entry and addition, deletion, and modification; data chart analysis and export; and email warning, to verify the feasibility of the application of digital twin technology in bridge monitoring, and the advantages of the intelligent monitoring system are obtained. The strain error is found to be less than 15.48 μℇ in the research, which is within the range of the fiber grating. This method can effectively monitor and forecast these distributed, nonlinear, strongly coupled, multivariable, and time‐varying complex structures. By monitoring bridges, the original monitoring data of bridges can be obtained, and scientific research data and analysis services can be provided. In particular, the damage caused by shock and vibration is monitored, so that the accumulation of damage can be detected before it threatens the safety of the structure, so that the damaged structure can be repaired in time to ensure the safe operation of the structure.

Weather Forecasting over Iraq Using Machine Learning

The weather generally comprises various factors, such as wind speed, precipitation, and rainfall. Environmental weather forecasting is a demanding task for researchers, and in recent years it has attracted much study attention. Our assessment considers a wide range of weather conditions across Iraq utilizing information gathered from NASA's estimate of the world's energy resources for the years 1981 to 2021. Therefore, the correct forecast of meteorological parameters is a difficult challenge due to their changing environmental conditions. Random forest, decision tree, and GBR algorithms are used for weather forecasting. A comparison among used methods is performed and the RF is achieved the best results with accuracy, MAE, MSE, R2 of 92%, 0.5, 2.45, and 0.92, respectively.

Review of Long-Term Trends in the Equatorial Ionosphere Due the Geomagnetic Field Secular Variations and Its Relevance to Space Weather

The Earth’s ionosphere presents long-term trends that have been of interest since a pioneering study in 1989 suggesting that greenhouse gases increasing due to anthropogenic activity will produce not only a troposphere global warming, but a cooling in the upper atmosphere as well. Since then, long-term changes in the upper atmosphere, and particularly in the ionosphere, have become a significant topic in global change studies with many results already published. There are also other ionospheric long-term change forcings of natural origin, such as the Earth’s magnetic field secular variation with very special characteristics at equatorial and low latitudes. The ionosphere, as a part of the space weather environment, plays a crucial role to the point that it could certainly be said that space weather cannot be understood without reference to it. In this work, theoretical and experimental results on equatorial and low-latitude ionospheric trends linked to the geomagnetic field secular variation are reviewed and analyzed. Controversies and gaps in existing knowledge are identified together with important areas for future study. These trends, although weak when compared to other ionospheric variations, are steady and may become significant in the future and important even now for long-term space weather forecasts.

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Relationship between external weather conditions and number of hibernating bats in two caves in the western Italian Alps

Weather conditions can influence the hibernation behaviour of temperate cave-dwelling bats that are tolerant to low temperatures, and their number can be correlated with weather variables. In this work a first assessment on the correlation between the number of individuals of three species of hibernating bats (Barbastella barbastellus, Myotis emarginatus and Rhinolophus hipposideros) and the environmental weather conditions before the survey was carried out is provided for two hibernacula of the Italian Western Alps.&#x0D; For the B. barbastellus, a significant inverse correlation was observed between the number of bats detected and the average daily temperature for thirty days preceding the count (p= 0.036) and ten days before counting (p= 0.036). A significant positive correlation was observed for M. emarginatus between the number of individuals and the average daily temperatures for thirty days preceding the count (p= 0.018). For R. hipposideros, a significant inverse correlation was observed with the average daily temperatures for the ten days before the count (p= 0.048) and the differences in the maximum and minimum temperature of the ten days preceding the count (p= 0.002).&#x0D; Results of this study show how the ambient temperatures before a count can influence the number of bats present in hibernacula. This confirms how the abundance of bats at underground hibernating sites can be used as an indicator of climate change, as temperature is an important factor controlling hibernation, although further studies are needed in order to better evaluate how the climatic variables interact with each other in regulating the number of bats in the hibernacula.

UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images

As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challenging to acquire and more expensive to label. Obtaining a large amount of high-quality aerial image data for object recognition and image understanding is an urgent problem. Existing studies show that synthetic data can effectively reduce the amount of training data required. Therefore, in this paper, we propose the first synthetic aerial image dataset for ship recognition, called UnityShip. This dataset contains over 100,000 synthetic images and 194,054 ship instances, including 79 different ship models in ten categories and six different large virtual scenes with different time periods, weather environments, and altitudes. The annotations include environmental information, instance-level horizontal bounding boxes, oriented bounding boxes, and the type and ID of each ship. This provides the basis for object detection, oriented object detection, fine-grained recognition, and scene recognition. To investigate the applications of UnityShip, the synthetic data were validated for model pre-training and data augmentation using three different object detection algorithms and six existing real-world ship detection datasets. Our experimental results show that for small-sized and medium-sized real-world datasets, the synthetic data achieve an improvement in model pre-training and data augmentation, showing the value and potential of synthetic data in aerial image recognition and understanding tasks.

The Relationship between Clouds Containing Multiple Layers 7.5–30 m Thick and Surface Weather Conditions

Previous studies have identified finely laminated, or layered, features within Arctic clouds. This study focuses on quasi-horizontal layers that are 7.5 to 30 m thick, within clouds from 0 to 5 km altitude. No pre-selection for any particular cloud types was made prior to the identification of laminations. We capitalize on the 4-year measurement record available from Eureka, Nunavut (79.6∘ N, 85.6∘ W), using the Canadian Network for the Detection of Atmospheric Composition Change (CANDAC) Rayleigh–Mie–Raman Lidar (CRL; 1 min, 7.5 m resolution). Laminated features are identified on 18% of all days, from 2016–2019. Their presence is conclusively excluded on 12% of days. March, April, and May have a higher measurement cadence and show laminations on 41% of days. Individual months show laminations on up to 50% of days. Our results suggest that laminations are not rare phenomena at Eureka. To determine laminations’ likely contribution to Arctic weather and climate, local weather reports were obtained from the nearby Environment and Climate Change Canada (ECCC) weather station. Days with laminated clouds are strongly correlated with precipitating snow (r = 0.63), while days with non-laminated clouds (r = −0.40) and clear sky days (r = −0.43) are moderately anti-correlated with snow precipitation.

Vehicle and pedestrian target detection in auto driving scene

To improve the target detection accuracy and speed of autonomous driving in various weather environments and small target traffic senarios,an improved YOLOV4 target detection model based on CSPDarknet45_G backbone network is proposed in this paper.By adding a new DBG module which consists of DArknetConv2D + BN + GELU activation function,this model is enhanced in generalization ability and accuracy. We also improved Res unit residual module to enhance shallow features fusing with deep feathers and reduced the number of neurons in the CSP module to simplify the module structure.The K-Means++ clustering algorithm is introduced to obtain the size of the prior box used for target detection to satisfy the data set in this paper. In the captured target vehicle image data set, the model detection result shows that the improved YOLOV4 model achieve an average detection accuracy of 90.45%, a recall of 94.37%, and an FPS of 50 frames per second when the IOU is taken as 0.5, which meet the real-time and accuracy of the detection task in this paper.

Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.

Data transmission bit error rate modeling and analysis of multi‐screen measurement system based on optical communication link

AbstractTo reduce the bit error rate of data transmission in the field of the long‐distance ballistic trajectory of weapon range, this article utilizes a MIMO‐OFDM atmospheric laser link communication model to transfer the obtained data using the four‐screen measurement system in the terminal ballistic. First, the atmospheric attenuation channel model caused by haze and fog in the atmospheric link communication is analyzed. Then the atmospheric attenuation factors of haze day are introduced and the received optical power of the system is calculated. Lastly, the atmospheric laser link communication model in different weather environment is established. The calculation functions of signal‐to‐noise ratio and bit error rate in the long‐distance link communication are derived. Through the calculation and analysis, the attenuation of laser's power caused by fog and haze with different visibilities is obtained. The results show that the lower is the visibility under fog or haze days, the faster is the attenuation of the laser's power and the higher is the bit error rate of the communication link. The atmospheric attenuation can be reduced by increasing the laser's power. The results verify the feasibility of the MIMO‐OFDM atmospheric laser link communication model. The proposed model has a certain guiding significance for the further study of reliable data transmission in the ultra‐long‐range ballistic.

The Effects of Climate Change on the Vertical Structure of Severe Weather Environments

The Effects of Climate Change on the Vertical Structure of Severe Weather Environments

An Open-Source, Low-Cost Measurement System for Collecting Hydrometeorological Data in the Open Field

To realize precision agriculture at multiple locations in the field, a low-cost measurement system should be developed for easy collection of hydrometeorological data, such as temperature, moisture, and light. In this study, a compact and low-cost hydrometeorological measurement system with a simplified wire code, which is customizable according to the purpose of observation, was built using a circuit board that connects Arduino to the sensors, which was then implemented and analyzed. The developed system measures air and soil temperatures, soil water content, and photosynthetic photon flux density using a sensor connected to Arduino Uno and saves the continuous, high-temporal-resolution output to an SD card. The results obtained from continuous measurement showed that the data collected using this system was significantly better than those collected using commercially available equipment. Anyone can easily measure the weather environments by using this fully open, highly versatile, portable, and user-friendly system. This system can contribute to the growth and expansion of precision agriculture, field management, development of crop models, and laborsaving. It can also provide a global solution to ongoing agricultural issues and improve the efficiency of farming operations, particularly in developing and low-income countries.

Reddening and darkening trends of on/off swirls and the relationship with magnetic field strength

AbstractLunar swirls are albedo features associated with magnetic anomalies, and their formation mechanism has long been debated. Because spectral properties of lunar swirls provide information on space weathering environments and surface compositions, differences in the spectra between swirls and background regions can give a clue to their formation. We analyze space weathering for swirls in the lunar highlands and maria using the VIS-NIR spectral slope, 1550 nm reflectance, and 950 nm band depth. We find that highland swirls show lower reddening-to-darkening ratios than off-swirl regions, implying more decreases of nanophase iron relative to microphase iron on swirls. We also use magnetic field data to investigate the influence of the crustal magnetic field on the space weathering of a few selected swirls. The Reiner Gamma swirl becomes more immature as the magnetic field strength increases, although the entire swirl has a high albedo. Especially, the Reiner Gamma tail shows that the stronger magnetic field makes the surface undergo less spectral reddening. Our results support the solar wind stand-off hypothesis, which is that the solar wind deflected by the crustal field determines the optical property of the surface.

Prediction of Target Detection Probability Based on Air-to-Air Long-Range Scenarios in Anomalous Atmospheric Environments

We investigate a target detection probability (TDP) using path loss of an airborne radar based on air-to-air scenarios in anomalous atmospheric and weather environments. In the process of calculating the TDP, it is necessary to obtain the overall path loss including the anomalous atmospheric environment, gas attenuation, rainfall attenuation, and beam scanning loss. The path loss including the quad-linear refractivity model and other radar input parameters is simulated using the Advanced Refractive Effects Prediction System (AREPS) software along the range and the altitude. For the gas and rainfall attenuations, ITU-R models are used to consider the weather environment. In addition, the radar beam scan loss and a radar cross section (RCS) of the target are considered to estimate the TDP of the airborne long-range radar. The TDP performance is examined by employing the threshold evaluations of the total path loss derived from the detectability factor and the free-space radar range equation. Finally, the TDPs are obtained by assuming various air-to-air scenarios for the airborne radar in anomalous atmospheric and weather environments.