Weather Environment Research Articles

IDOD-YOLOV7: Image-Dehazing YOLOV7 for Object Detection in Low-Light Foggy Traffic Environments.

Convolutional neural network (CNN)-based autonomous driving object detection algorithms have excellent detection results on conventional datasets, but the detector performance can be severely degraded in low-light foggy weather environments. Existing methods have difficulty in achieving a balance between low-light image enhancement and object detection. To alleviate this problem, this paper proposes a foggy traffic environment object detection framework, IDOD-YOLOV7. This network is based on joint optimal learning of image defogging module IDOD (AOD + SAIP) and YOLOV7 detection modules. Specifically, for low-light foggy images, we propose to improve the image quality by joint optimization of image defogging (AOD) and image enhancement (SAIP), where the parameters of the SAIP module are predicted by a miniature CNN network and the AOD module performs image defogging by optimizing the atmospheric scattering model. The experimental results show that the IDOD module not only improves the image defogging quality for low-light fog images but also achieves better results in objective evaluation indexes such as PSNR and SSIM. The IDOD and YOLOV7 learn jointly in an end-to-end manner so that object detection can be performed while image enhancement is executed in a weakly supervised manner. Finally, a low-light fogged traffic image dataset (FTOD) was built by physical fogging in order to solve the domain transfer problem. The training of IDOD-YOLOV7 network by a real dataset (FTOD) improves the robustness of the model. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others. The IDOD-YOLOV7 algorithm not only suppresses the artifacts of low-light fog images and improves the visual effect of images but also improves the perception of autonomous driving in low-light foggy environments.

Influence of ZnO (Nano)Rods on the Dielectric Properties of DGEBF Epoxy Exposed to Progressive Artificial Weathering for VHF/Early UHF Applications

An as-produced epoxy material reinforced with ZnO (nano)rods for outdoor use was produced for the purpose of enclosing VHF/early UHF transceiving elements. Three different ZnO (nano)rod percentages were dispersed (0.1 wt%, 0.5 wt% and 2 wt%) in epoxy samples and were consequently exposed to an artificial weathering environment for a total period of 2880 h. SEM and XRD characterizations were followed by gravimetric, colorimetric and dielectric measurements that were conducted every 576-h AWE treatment intervals and were complemented by FTIR, UV-Vis, contact angle and gloss measurements. The composite exhibited favourable dielectric behaviour: ZnO presence inflicted minimal impact to the composites’ loss tangent, while dielectric permittivity was reduced for low ZnO content (0.1 wt%/0.5 wt%), especially until the 1152-h interval, while it suffered an increase for 2 wt% samples that was maintained from the beginning until the end of the experiment. Hence, the low-k (4–5.2) constructed material, compared to bare epoxy resin, maintained moderate loss tangent levels (0.55–0.7) in the VHF/early UHF spectrum, throughout the full AWE treatment, and also attained reduced dielectric permittivity, thus exhibiting moderate radio frequency transparency, together with enhanced endurance against environmental fatigue.

Euler–Lagrange stochastic modeling of droplet breakup and impact in supersonic flight

Blunt bodied aircraft traveling supersonically in weather environments may be damaged by impacts with water droplets and other airborne particles, such as snow and ice. Prior to an impact, these particles will encounter a bow shock that causes a discontinuity in their relative velocity with the gas phase, which can lead droplets to breakup into smaller droplets. These smaller droplets are more easily diverted from colliding with the blunt body due to their significantly reduced inertia relative to the initial rain droplets. One-way coupled Euler–Lagrange simulations are used to study the dynamics of droplets approaching a blunt body in steady two dimensional and axi-symmetric flow fields using a stochastic version of the Taylor analogy breakup model for the breakup dynamics. Ultimately, the dominant mechanism determining engineering quantities of interest was observed to be a competition between breakup time and the time available for a droplet to reach the body after encountering the bow shock. At Mach numbers 2, 3, and 6, the competition between these mechanisms was the dominant factor determining the momentum transfer to the blunt body via droplet collisions, which can be well characterized by a scaling relation.

Evaluation of a high-speed hybrid OAM-OFDM-MDM multiplexed coherent FSO system under desert conditions

To meet the needs of future wireless optical networks, this paper introduces a high-speed, hybrid multiplexed, coherent free-space optical (FSO) communication system that integrates an orbital angular momentum (OAM) multiplexed signal with an orthogonal frequency division multiplexing (OFDM) technique. Two independent QAM polarized beams, each carrying in-phase and quadrature (I/Q) phase 16-QAM-OFDM modulated data, are combined using mode division multiplexing (MDM) to increase the capacity of the proposed system. The reason of choosing OFDM is its capability to support higher data rate, and mitigating intersymbol interference (ISI). The signal is detected using a coherent detection-based digital signal processing (DSP) algorithm at the receiver end. The proposed hybrid FSO system is evaluated in low and heavy dust environments using bit error rate (BER), link distance, optical signal-to-noise ratio (OSNR), and received optical power performance matrices. The simulation results demonstrate the successful transmission of a 120 Gb/s single carrier over the longest link ranges of 1.5 and 0.40 km, respectively, under low and heavy dust weather environments below the signal degradation threshold value (forward error correction (FEC) limit) of BER 2.2 × 10–3 in strong turbulent conditions.

Warning: Tourniquets Risk Frostbite in Cold Weather.

We sought to better understand the frostbite risk during first-aid tourniquet use by reviewing information relevant to an association between tourniquet use and frostbite. However, there is little information concerning this subject, which may be of increasing importance because future conflicts against near-peer competitors may involve extreme cold weather environments. Historically, clinical frostbite cases with tourniquet use occurred in low frequency but in high severity when leading to limb amputation. The physiologic response of vasoconstriction to cold exposure leads to limb cooling and causes a reduction of limb blood flow, but cold-induced vasodilation ensues as periodic fluctuations that increase blood flow to hands and feet. In animal experiments, tourniquet use increased the development of frostbite. Evidence from human experiments also supports an association between tourniquet use and frostbite. Clinical guidance for caregiving to casualties at risk for frostbite with tourniquet use had previously been provided but slowly and progressively dropped out of documents. Conclusions: The cause of frostbite was deduced to be a sufficiently negative heat-transfer trend in local tissues, which tourniquet use may worsen because of decreasing tissue perfusion. An association between tourniquet use and frostbite exists but not as cause and effect. Tourniquet use increased the risk of the cold causing frostbite by allowing faster cooling of a limb because of reduced blood flow and lack of cold-induced vasodilation. Care providers above the level of the lay public are warned that first-aid tourniquet use in low-temperature (<0°C [<32°F]) environmental conditions risks frostbite.

Multiple Complex Weather Tolerant and Low Cost Solution for Helmet Detection

With the development of construction industry, the traditional manual inspection has been gradually eliminated due to many shortcomings, such as low efficiency, time-consuming and labor-intensive. Meanwhile, the current helmet detection model on the market does not consider the interference of complex weather, which greatly affects the detection performance. A low-cost helmet detection scheme is proposed in this paper, which can be used in various complex weather environments such as heavy rain, fog and snow. Firstly, the monitoring video of the construction site is sliced as the helmet wearing detection data set, and improved on the basis of Yolo v5s model to make it meet the requirements of helmet detection. Secondly, data augmentation and oversampling are adopted to improve the accuracy for small targets. Finally, K-means++ clustering algorithm is utilized to change the dimension of anchor box for better detection performance, and MSRCR algorithm is used to filter complex weather conditions. Compared to the original Yolo v5s, the mean average precision of the proposed scheme achieves 94.27% under complex weather conditions. For images with a size of 300*300, the detection speed can reach 63 frames per second. Therefore, the scheme can realize a high-precision, real-time and low-cost helmet detection system which can be used in a complex weather environments effectively.

Visualization of 3D forest fire spread based on the coupling of multiple weather factors

Combustibles, topography, and weather factors are the three essential factors affecting forest fire behavior, and current forest fire spread models need to consider weather factors fully. This paper proposes a forest fire spread method based on environmental weather factors to present a visualized simulation of forest fire spread in the natural environment. Forest pyrolysis differs based on water content, so a single-tree pyrolysis model with temperature as its core has been constructed to describe the differences in forest pyrolysis during different seasons visually. In addition, based on the improved Huygens principle as the theoretical basis for forest fire spread, weather factors such as wind speed, wind direction, and precipitation controlled by weather are coupled with the forest fire spread process. And the forest fire spread in three-dimensional scenarios is simulated by considering environmental factors. The visualization of the forest fire extinguishing process caused by precipitation is realized. Finally, the interaction between rain and snow, terrain and trees is realized when precipitation affects the corresponding landscape and vegetation texture to enhance the realism of the constructed forest environment. In short, this paper proposes a forest fire spread method based on environmental weather factors, which intuitively expresses the influence of different weather factors on forest fire spread, thereby improving the immersive experience of the related senses and realizing realistic scene roaming.

Multi-Attention DenseNet: A Scattering Medium Imaging Optimization Framework for Visual Data Pre-Processing of Autonomous Driving Systems

The vision system is important for almost all kinds of autonomous driving systems. However, visual data interfered by scattering media, such as smoke, haze, water, and other non-uniform media will be degraded seriously, showing the characteristics of detail loss, poor contrast, low visibility, or color distortion. These characteristics can significantly interfere with the reliability of autonomous driving systems. In real environments the image degradation mechanism is complex, and the estimation of degradation parameters is difficult. This issue remains to be solved. In this study, we employed dense blocks as the framework and introduced the attention mechanism to our model from four dimensions: Multi-scale Attention, Channel Attention, Structure Attention, and ROI (region of interest) Attention. With the help of the training data provided by the weakly supervised model, the proposed method achieved excellent performance in the task of scattering medium imaging optimization in different scenes. Comparative experiments show that the proposed method is robust, and is superior to other state-of-the-art methods in image dehazing, and underwater image enhancement tasks. It is of great significance to improve the reliability of autonomous driving systems in underwater and severe weather environments.

Effect of Coating Process on Properties of Two-Component Waterborne Polyurethane Coatings for Wood

Acrylic acid has good environmental weather resistance, water resistance, alcohol resistance, dirt resistance, and other properties. An acrylic acid dispersion with a core–shell structure was prepared and reacted with a polyisocyanate-type curing agent to prepare a waterborne polyurethane topcoat suitable for wood. The prepared two-component polyurethane topcoat was used in combination with a waterborne primer and a waterborne sealing primer and applied to Pine (Pinus strobus) boards to carry out gloss, wear resistance, and adhesion experiments. The effects of different coating amounts and spraying times on the comprehensive properties of the wood coatings were studied. The experimental results showed that when the self-made two-component polyurethane topcoat for wood was matched with the primer and the sealing primer, the coating amount of the sealing primer had little effect on improving the adhesion and wear resistance but had a significant effect on the gloss. The spraying times of the primer and the topcoat greatly impacted the adhesion, but they had no decisive impact on the improvement of the gloss. When the coating amount was 60 g/m2 for the waterborne sealing primer, 100 g/m2 for waterborne primer, and 120 g/m2 for the self-made topcoat and the coating was sprayed twice, the comprehensive performance of the obtained film on the wood was the best. This research on the preparation and coating process optimization of the two-component waterborne polyurethane coatings for wood can provide a technical basis for the application of two-component waterborne polyurethane topcoats for wood.

Study on the CNN model optimization for household garbage classification based on machine learning

In order to solve the problem of household garbage classification accurately and efficiently, convolutional neural network classifier is an effective method. In this study, a garbage classification device was designed, and the image dataset Wit-Garbage for garbage classification was constructed based on the device by collecting garbage images under different light intensity and weather environment. The performances of the five network models VGG16, ResNet50, DenseNet121, MobileNet V2, Inception V3 on this dataset were compared by transfer learning. Then, the lightweight convolutional neural network MobileNet V2 was optimized by fine-tuning the hyperparameters, such as the type of optimizer, learning rate, Dropout parameter and number of freezing layers, respectively, and the training accuracy and efficiency were discussed in detail. Finally, the optimized model MobileNet V2 was deployed to the self-made garbage classification device for verification. The results show that the MobileNet V2 network model is superior to other networks in terms of training accuracy and efficiency on the proposed dataset, when the image input size was 224 ∗ 224 pixels, the Adamax optimizer was adopted, the learning rate was 0.0001, the Dropout was less than 0.5, and the number of frozen layers is less than 30. The actual verification results show that the average accuracy of the optimized network model trained on the proposed dataset for MSW classification was up to 98.75%, and compared with the model before optimization, the average accuracy was improved by 2.83%, and the average detection time was reduced by 69%.

Aspect-dependent bedrock weathering, cliff retreat, and cliff morphology in a hyperarid environment

Deciphering aspect-related hillslope asymmetry can enhance our understanding of the influence of climate on Earth’s surface morphology and the linkage between topographic morphology and erosion processes. Although hillslope asymmetry is documented worldwide, the role of microclimatic factors in the evolution of dryland cliffs has received little attention. Here, we address this gap by quantifying aspect-dependent bedrock weathering, slope-rill morphology, and sub-cliff clast transport rates in the hyperarid Negev desert, Israel, based on light detection and ranging (LiDAR)-derived topography, clast-size measurements, and cosmogenic 10Be concentrations. Cliff retreat rates were evaluated using extrapolated profiles from dated talus flatirons and 10Be measurements from the cliff face and sub-cliff sediments. We document systematic, aspect-dependent patterns of south-facing slopes being less steep and finer-grained relative to east- and north-facing aspects. In addition, cliff retreat and clast transport rates on slopes of the south-facing aspect are faster compared to the other aspects. Our data demonstrate that bedrock weathering of the cliff face and the corresponding grain size of cliff-derived clasts delivered to the slopes constitute a first-order control on cliff retreat and sediment transport rates. We demonstrate that the morphology of the cliff and the pattern of bedrock weathering co-vary with the solar radiation flux and hence that cliff evolution in hyperarid regions is modulated by aspect-dependent solar radiation. These results help to better understand interactions between climate and dryland surface processes.

Effect of different starch contents on physical, morphological, mechanical, barrier, and biodegradation properties of tapioca starch and poly(butylene adipate‐co‐terephthalate) blend film

AbstractStudy on degradation behaviors of biodegradable poly(butylene adipate‐co‐terephthalate) (PBAT) blended with different compositions of thermoplastic starch (TPS) under soil burial and natural weathering environments is vital in order to predict the product service‐life and planning for in situ biodegradation after product disposal. In this article, different compositions of TPS (0%, 20%, 40%, 50%, and 60%) were compounded with PBAT using single screw extruder. The samples were characterized for their tensile properties, fractured surface morphology, water barrier and surface hydrophorbicity properties in order to investigate the effect of starch fractions in PBAT blends. The degradation behavior under natural weathering and soil burial conditions was also determined during the 9 months duration by observing the change of physical appearance, weight loss, surface morphology, chemical structural, and tensile properties. The findings showed that the addition of TPS (20%, 40%, 50%, and 60%) had led to a reduction in tensile strength (41.47%, 60.53%, 63.43%, and 68.53%), and reduction in elongation at break (42.92%, 92.1%, 92.23%, and 93.22%, respectively) and water barrier properties. The findings also showed that there were distinct degradation behavior under both conditions. Upon exposure to natural weathering, photodegradation and Norrish type I &amp; II occurred whereas under the soil burial condition, hydrolytic, and enzymatic degradation take places. Sample with the highest starch contents underwent the highest weight loss and reduction in tensile properties under both environments. The findings in this study are useful in order to investigate the feasibility of PBAT/Tapioca starch blends for biodegradable plastic film for various industrial applications especially in packaging and agricultural mulch.

Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022

AbstractOn 3 February 2022, SpaceX Starlink launched and subsequently lost 38 of 49 satellites due to enhanced neutral density associated with a geomagnetic storm. This study examines the space weather conditions related to the satellite loss, based on observations, forecasts, and numerical simulations from the National Oceanic and Atmospheric Administration Space Weather Prediction Center (SWPC). Working closely with the Starlink team, the thermospheric densities along the satellite orbits were estimated and the neutral density increase leading to the satellite loss was investigated. Simulation results suggest that during the geomagnetic storm, pre‐launch Monte Carlo analyses performed by the Starlink team using empirical neutral density inputs from NRLMSISE‐00 tended to underestimate the impact relative to predictions from the operational coupled Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamics physics‐based model. The numerical simulation indicated this minor to moderate geomagnetic storm was sufficient to create 50%–125% density enhancement at altitudes ranging between 200 and 400 km. With the increasing solar activity of Solar Cycle 25, satellites in low‐Earth orbit are expected to experience an increasing number of thermospheric expansion events. Currently, no alerts and warnings issued by SWPC are focused on satellite users concerned with atmospheric drag and related applications. Thus, during geomagnetic storms, it is crucial to establish suitable alerts and warnings based on neutral density predictions to provide users guidance for preventing satellite losses due to drag and to aid in collision avoidance calculations.

Hierarchically Porous Fiber‐Based Nanofluidic Diode as an Efficient Multimode Hygroelectric Generator

AbstractHarvesting electrical energy from different forms of water by using the hydrovoltaic effect is a promising approach to green energy acquisition. However, at present, different devices are required to obtain electrical energy from different water forms, and the output performance of the devices is not high, which greatly reduces the convenience and effectiveness of harvesting electrical energy from different water forms. Here, a carbon nanofiber electrode with a hierarchical porous structure capable of promoting water molecule adsorption, rapid penetration, and diffusion is reported. Besides, the electrode acts as a nanofluidic diode with anodic aluminum oxide in which the built‐in electric field drives selective separation and directional transport of ions and efficient ion/electron current conversion at the electrodes. Unlike typical hygroelectric devices, the device can work with moisture (93% relative humidity, 25 °C, open‐circuit voltage (VOC) ≈ 1.1 V, short‐circuit current density (JSC) ≈ 27 µA cm−2), water droplets (2 µL, VOC ≈ 1.03 V, JSC ≈ 640 µA cm−2), and bulk water (VOC ≈ 1.15 V, JSC ≈ 256 µA cm−2). It provides an efficient way to generate electricity in multiform water environment, greatly broadening the working scene and improving the adaptability of the device in complex weather environments.

Wettability after Artificial and Natural Weathering of Polyethylene Terephthalate

The weathering of plastics is always accompanied by a change in surface properties, especially wettability in the case of water. For plastics weathering in an aquatic environment, wettability plays an important role in transport, sedimentation, and dispersion in the water body. To quantify wettability, contact angle measurement is a fast and convenient method that requires little experimental effort. This technique was used with the aim of systematically discussing how measured values of contact angles can contribute to the assessment of the weathering state. Using polyethylene terephthalate (PET), wetting was analyzed on samples from artificial weathering and from controlled, natural weathering. Surface analytical methods were used (Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), ultraviolet and visible light spectroscopy (UV/VIS)) to analyze the parameters affecting the contact angle: (i) chemical bond breaking and formation, (ii) eco-corona formation and biofilm growth, and (iii) change in surface structure and roughness. It was found that wettability with water increased during weathering in all cases. The reasons for this varied and depended on the method of weathering. The improved wettability during artificial weathering was due to changes in the polymer surface chemistry. In natural weathering, however, the formation of eco-corona and biofilm was responsible for the changes.

Soil core study indicates limited CO2 removal by enhanced weathering in dry croplands in the UK

The application of crushed silicate minerals to agricultural soils has been suggested as a route to enhance weathering rates and increase CO2 drawdown. Laboratory studies have attempted to evaluate the potential of enhanced weathering as a CO2 removal technique but do not simulate the geochemical complexity of soil environments, and studies in the field are limited in the nature of data they can collect. To overcome these limitations, this study uses an experimental set-up which fully encapsulates field conditions in a controlled setting using soil cores removed from UK cropland and treated with crushed basalt. Cores were exposed to natural weather conditions throughout a 14-month time series, and soil solution was sampled in 10–20 cm intervals in the core to provide insight into the fate of dissolution products with soil depth.This study assessed the rate and chemistry of basalt dissolution 8 months after addition at a high application rate (100 t basalt ha−1) using direct measurements from a UK soil. Assuming conclusions drawn from this study are representative of field-scale enhanced weathering, findings indicate that a set application of basalt to lime-rich, unirrigated UK soils releases alkalinity at a rate of 310 ± 30 eq ha−1 yr−1 and could remove 10.2 ± 0.8 kgCO2 ha−1 yr−1. Accumulation of undissolved basalt may also lead to large and irreversible changes to soil compositions following repeated application. When considering variation in hydrology around the UK, we assess the drawdown potential of application of basalt to all UK arable land as 1.3 ± 0.1 MtCO2 yr−1 which is equivalent to 3% of current UK agricultural CO2 emissions. This is 5- to 25- fold slower than previous modelled assessments, likely due to complexities of soil systems and to water limitation on alkalinity release. Further research is needed to fully assess controls on the potential of enhanced weathering in the real-world environment, across a range of hydrological and soil environments, before the approach is substantively scaled-up for CO2 removal.

Characterization of volcanic reservoirs; insights from the Badejo and Linguado oil field, Campos Basin, Brazil

The Badejo and Linguado oil fields are hosted within non-conventional volcanic reservoirs which produced commercial hydrocarbons from the Lower Cretaceous Cabiúnas Formation, Campos Basin, offshore Brazil. Despite over 30 years of production, limited characterization of the nature and reservoir properties of the volcanic reservoirs have been presented to date. A comprehensive reappraisal of the Cabiúnas Formation volcanic reservoirs is presented in this study incorporating extensive existing and new data including core descriptions, laboratory petrophysical analyses, petrography, QEMSCAN, SEM, wireline, and microtomography results from five cored intervals of wells spanning the main reservoirs of the Badejo and Linguado fields. Volcanic facies analyses of the sequences reveal a predominance of subaerial effusive basic volcanic rocks interbedded with sediment and in several cases comprising peperites revealing intricate lava-sediment interaction products. Four volcano-sedimentary units are identified, showing alternations between low (compound pahoehoe lava dominated Units 1 and 3) and high (tabular rubbly pahoehoe lava dominated Units 2 and 4) effusion rates. Paleoenvironmental conditions also varied between units with extensive oxidation present in Unit 1 inferred to relate to extended periods of subaerial exposure and weathering in an arid environment. Overlying Units 2–4 reveal an increase in humidity evidenced by an increase in the presence of well sorted fine-grained sediment interlayers, peperites, and non-marine ostracods. Both original facies and alteration reveal key controls on reservoir properties. Extensive weathering and alteration of Unit 1 caused pervasive filling of original porosity (vesicles and fractures) and resulted in reservoir degradation. Lesser weathering of overlying Units 2, 3 and 4, resulted in improved reservoir properties which can be clearly linked to volcanic intra-facies including vesiculated and autobrecciated lava flow tops which commonly reveal extensive oil staining. This study reveals the intricate interplay between primary volcanic facies and subsequent alteration history in dictating volcanic reservoir properties in a successful offshore oil field development.

Doppler Lidar and Mobile Radiosonde Observation-Based Evaluation of Warn-on-Forecast System Predicted Near-Supercell Environments during TORUS 2019

Abstract Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm. Significance Statement It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.

Approximate climate analysis for assessing the suitability of Indian climate zones for hydroponic fodder applications

Hydroponic fodder has been proposed as a novel low-tech solution to India’s fodder scarcity. Hydroponic fodder can reduce water requirements by up to 95% and use up to 70% less land due to their vertically scalable nature. The short crop cycles of approximately 7 days yields a seed to feed ratio of 1:8. This study analyses the external environmental weather conditions in Indian climate zones, assessing their suitability for the application of hydroponic fodder solutions. The weather of each climate zone was approximated by a representative city through parameter selection and optimisation by three-dimensional Euclidean distance minimisation. Simulation and analysis were carried out on the Rhinoceros software platform with the Grasshopper and LadyBug plug-ins. Hourly temperature plots, sun-path diagrams and psychrometric charts with modified predicted mean vote polygons superimposed were generated for each climate zone. The ambient weather suitability of the following representative cities was simulated to be as follows: Ahmedabad (86.70%), Kolkata (81.86%), Bengaluru (94.07%), Lucknow (73.23%) and Srinagar (30.85%). A cost benefit analysis was buttressed by a sensitivity analysis with respect to the selling price of green fodder. A preliminary cash flow analysis yielded a discounted payback period of 4 months for direct fodder sales and 20 months for sale of livestock products as a co-benefit of green fodder production.

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

With the increased awareness of environmental protection, people have higher requirements for the accuracy of environmental information of surrounding life. The current monitoring of urban environmental information mainly comes from local environmental weather stations. Although the monitoring equipment of environmental weather stations is better than personal monitoring equipment, the monitoring equipment of weather monitoring stations is too expensive and only suitable for large-scale coarse-grained monitoring. Because the environmental information of a city is affected by factors such as landforms, buildings, rivers, factories, population density, and traffic flow, there are great differences in the environmental information of different areas in a city. Therefore, this study proposes a method that can be used for small-scale and fine-grained environmental information monitoring: the task grid-based urban environmental information release mechanism for mobile crowd sensing (MCS). Through this mechanism, the monitoring area is divided into different task grids according to the characteristics of the area, and the environmental information is sensed by mobile crowd sensing. For the sensing data, through an efficient data fusion algorithm designed in this study, the sensing information is fused to obtain the fine-grained environmental information of different task grids in the area. Through the use of this mechanism, differentiated environmental information can be provided to users in different areas of the city. In a simulation, this mechanism showed higher information accuracy than traditional information release methods. Thus, the mechanism is scientific and has good application value.