Weather Situations Research Articles

Long term wind turbine sound monitoring

In the DLR Windpark Wiwaldi, consisting of two wind turbines, extensive measurements of sound and meteorology have been conducted. In the context of long-term wind turbine sound monitoring, the data from such measurements are used to understand and decipher physical effects. This kind of data suffers sometimes for reliability, because of many changing conditions and situations around the microphone. Therefore, in three different phases the sound of various sources were captured. Before the turbines were installed, initial measurements were made to identify background noise, followed by additional measurements to assess the impact of installing weather masts on the sound field. Ultimately, the wind turbines were operated reporting the exact state of the turbines (such as rotation speed, pitch, yaw, etc.) to relate to the associated sound levels. This presentation illustrates how all these measurements are used to create a consistent picture of the noise situation around the wind park depending on specific weather situation and discusses the need of accuracy. We document the limitations and uncertainties associated with the measurements and show these limitations when interpreting and reporting the results to ensure users in understanding the reliability and confidence level of such data.

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

Predictive Modeling and Forecasting of Solar Power Generation Using Machine Learning Techniques

The purpose of this research study is to investigate the predictive modelling and forecasting of solar power generation by utilising a variety of machine learning approaches. The paper addresses the significant obstacle that is presented by the variable production of solar energy, which is a barrier to the effective incorporation of solar power into the network of electrical power distribution systems. This research investigates the application of machine learning models such as Bayesian Ridge Regression, Gradient Boosting, and Linear Regression. The research makes use of historical meteorological data and solar power output to investigate the application of these models. The effectiveness of these models in estimating the amount of solar energy that will be produced under a variety of different weather situations is being investigated. Based on the findings, it is evident that the application of machine learning techniques has the potential to considerably improve the accuracy of solar power projections. This, in turn, will facilitate better grid integration and promote wider adoption of solar energy products. Through the demonstration of the potential of advanced machine learning approaches in enhancing the reliability and efficiency of solar power generation, this study makes a contribution to the expanding body of literature on the forecasting of renewable energy sources.

Atmospheric dynamics and fire-induced phenomena: Insights from a comprehensive analysis of the Sertã wildfire event

The Sertã wildfire started on October 15th, 2017, in a mountainous area in central Portugal, on the hottest day of an exceptionally dry month compared to the previous two decades. This dry spell was attributed to a persistent heatwave from October 1st to 16th, which impacted most of mainland Portugal. Due to the critical weather and vegetation conditions, the fire was very intense, generating a thermal plume that reached high altitudes. The main purpose of this work is to analyze and discuss the impact of the Sertã wildfire on the dynamics of atmospheric mountain circulation. To accomplish this, a numerical modeling system integrating meteorological, fire spread, and smoke dispersion models was employed. The results indicate that the interaction between the wildfire and the local mountain breeze in central Portugal, under the synoptic weather conditions in Europe, which included the Hurricane Ophelia over the Atlantic Ocean and a warm-sector polar frontal depression in northern Europe, led to a locally disturbed weather situation. The consistent, efficient, and uniform local transport of atmospheric properties at lower levels was disrupted, intensifying wind shear and developing more turbulent advection patterns. Moreover, the fire's impact contributed to the development of a baroclinic wave at 500 hPa over mass centers, which settled at the surface and enhanced cloud development over the region. This phenomenon aligns with the atmospheric circulation effects studied in the Bjerknes and Rossby theories.

Effects of Meteorological Conditions in Summer and Early Autumn on PM2.5 and O3 Pollution in Beijing-Tianjin-Hebei and Surrounding Areas

PM2.5 pollution remains prominent in autumn, whereas O3 pollution gradually manifests in summer. To understand the dual high characteristics and meteorological effects of PM2.5 and O3 in the summer and early autumn of 2021 in the Beijing-Tianjin-Hebei and surrounding areas, the spatiotemporal distribution characteristics of PM2.5 and O3 concentrations, as well as meteorological conditions, subtropical high index, and weather situation in the Beijing-Tianjin-Hebei and surrounding areas were analyzed. The results showed that PM2.5 concentration and DPO3 （O3 daily maximum 8h mean minus O3 concentration at 06：00） from June to September 2021 decreased compared with those in the same period in 2020 and 2022, and high concentrations were mainly occurring in June. The overall PM2.5 concentration and DPO3 showed a gradual decrease from the middle to the north and south, with synchronous spatiotemporal changes. The hourly value of PM2.5 concentration presented a multimodal distribution, reaching the peak at 07：00-10：00 and 22：00-24：00. O3 concentration showed an opposite trend of change with PM2.5 concentration, reaching their peak from 14：00-16：00. When DPO3 and the concentration of PM2.5 were high, the characteristics of near-surface meteorological elements were characterized by temperatures ranging from 24.0-28.0℃, relative humidity concentrated at 58.4%-76.3%, and wind speeds ranging from 1.5-3 m·s-1. There was a high lag correlation between the subtropical high index and DPO3. When the subtropical high was farther and stronger from the Beijing-Tianjin-Hebei and surrounding areas, DPO3 was higher. The major weather patterns with both high PM2.5 and O3 concentrations in the study area were near surface low-pressure front, high-pressure type, and frontal type. The high altitude was controlled by high-pressure ridges, and the subtropical high ridge line was stable between 21°-28°N.

Global assessment of production benefits and risk reduction in agroforestry during extreme weather events under climate change scenarios

Climate change and extreme weather events are threatening agricultural production worldwide. The anticipated increase in atmospheric temperature may reduce the potential yield of cultivated crops. Agroforestry is regarded as a climate-resilient system that is profitable, sustainable, and adaptable, and has strong potential to sequester atmospheric carbon. Agroforestry practices enhance agroecosystems’ resilience against adverse weather conditions via moderating extreme temperature fluctuations, provisioning buffers during heavy rainfall events, mitigating drought periods, and safeguarding land resources from cyclones and tsunamis-type events. Therefore, it was essential to comprehensively analyze and discuss the role of agroforestry in providing resilience during extreme weather situations. We hypothesized that integrating trees in to the agro-ecosystems could increase the resilience of crops against extreme weather events. The available literature showed that the over-story tree shade moderates the severe temperature (2–4°C) effects on understory crops, particularly in the wheat and coffee-based agroforestry as well as in the forage and livestock-based silvipasture systems. Studies have shown that intense rainstorms can harm agricultural production (40–70%) and cause waterlogging. The farmlands with agroforestry have been reported to be more resilient to heavy rainfall because of the decrease in runoff (20–50%) and increase in soil water infiltration. Studies have also suggested that drought-induced low rainfall damages many crops, but integrating trees can improve microclimate and maintain crop yield by providing shade, windshield, and prolonged soil moisture retention. The meta-analysis revealed that tree shelterbelts could mitigate the effects of high water and wind speeds associated with cyclones and tsunamis by creating a vegetation bio-shield along the coastlines. In general, existing literature indicates that implementing and designing agroforestry practices increases resilience of agronomic crops to extreme weather conditions increasing crop yield by 5–15%. Moreover, despite its widely recognized advantages in terms of resilience to extreme weather, the systematic documentation of agroforestry advantages is currently insufficient on a global scale. Consequently, we provide a synthesis of the existing data and its analysis to draw reasonable conclusions that can aid in the development of suitable strategies to achieve the worldwide goal of adapting to and mitigating the adverse impacts of climate change.

Photovoltaic energy harvesting booster under partially shaded conditions using MPPT based sand cat swarm optimizer

Photovoltaic (PV) systems perform a vital role in addressing the worldwide energy crisis and fulfilling the escalating energy demand. The variability in irradiance, temperature, and unpredictable weather conditions possess a direct impact on the productivity of PV systems. Furthermore, the existence of partially shaded conditions intensifies the complexity of PV systems, resulting in significant power degradation. These conditions present significant challenges for PV systems to achieve maximum power output and produce optimal energy. To address the prevailing challenges, this study introduces a maximum power point tracking (MPPT) control methodology utilizing a sand cat swarm optimizer (SCSO). This ingenious strategy adapts the sand cat hunting style. The investigation centers on optimizing energy harvesting in PV systems, with a specific emphasis on enhancing precision, rapid convergence, and minimizing oscillations. The suggested SCSO performance is evaluated under a variety of weather situations, including both instances of partially shaded and uniform irradiance. The SCSO results are juxtaposed with other existing bio-inspired algorithms, such as grey wolf optimization (GWO), particle swarm optimization (PSO), and tunicate swarm algorithm (TSA). The proposed SCSO technique achieves 99.94 % tracking accuracy on average and shows superior performance, with faster tracking response and less power oscillation. Moreover, the proposed SCSO generates significantly more energy than the rest compared algorithms. The performance of the suggested method is further validated through a hardware-based experimental assessment, demonstrating an optimal level of tracking performance.

Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC–MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17β-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC–MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.

Transport Characteristics and Potential Source Areas of PM2.5 in Yangquan City Under Different Pollution Levels

Based on the PM2.5 monitoring data, NCEP data, and the meteorological data of the weather situation analysis at the corresponding time in Yangquan City from 2020 to 2022, using the HYSPLIT4 backward trajectory model, multi-station potential source contribution factor analysis （MS-PSCF） and trajectory density analysis （TDA） were introduced to study the differentiation and classification of PM2.5 transport channels and potential sources in Yangquan City. The results showed that： ① The PM2.5 pollution in Yangquan was mainly concentrated in Yangquan and Pingding, whereas the pollution in Yuxian was relatively light. The proportion of days with different pollution levels and the average and maximum values of PM2.5 concentration in Yangquan and Pingding were significantly higher than those in Yuxian, and the distribution characteristics of PM2.5 were closely related to the local special terrain. ② The amount of PM2.5 pollution and the concentration of PM2.5 in different pollution levels were the highest in light wind weather. The influence of east-west regional transport on PM2.5 pollution times and PM2.5 concentration of Yangquan and Pingding was obvious, and the contribution of east wind was significant. The influence of local pollution sources was the main factor in the moderate pollution weather in Yuxian County. ③ There were four main ground conditions for the generation and maintenance of moderate or above pollution weather： warm low pressure type （22%）, high pressure front （bottom） type （54%）, high pressure back type （14%）, and pressure equalization field （10%）. High pressure front （bottom） type was the main ground situation causing the increase in PM2.5 concentration. There were two types of upper air conditions, namely, flat westerly flow type （78%） and northwest flow type （22%）. The upper westerly flow type was the main upper air condition that caused the increase in PM2.5 concentration. ④ The results of transport channels and potential source areas of PM2.5 with different pollution levels obtained by MS-PSCF and TDA were consistent. The main transport channels of PM2.5 were the northeast, southeast, and northwest channels, whereas the northeast and southeast channels were short-distance transport routes, which were the main routes causing the increase in PM2.5 concentration. The northwest channel was consistent with the northwest dust transport channel, belonging to long-distance transmission. The main potential source areas of PM2.5 pollution were located in the central and western parts of Hebei and the southeast part of Hebei, the northeast part of Henan and its junction with the southwest part of Shandong, and the southeast part of Shanxi.

Analysis of informativeness of features of classification of dangerous weather events based on radar observation results

One of the crucial factors affecting the safety and regularity of state and civil aviation flights is the meteorological situation. The European territory of Russia is most characterized by dangerous meteorological phenomena associated with cumulonimbus clouds: shower, thunderstorms, hail, accompanied by high atmospheric turbulence. Currently, meteorological radar stations are an indispensable source of information about the weather situation for air transport. The criteria for the classification of meteorological phenomena used in modern radar stations are formed for each event separately and are based on knowledge only about the altitude distribution of radar reflectivity and air temperature, despite the fact that radar data assess the wind characteristics of the atmosphere. It is shown that optimization of the classification criteria for the mentioned meteorological phenomena should be realized by generalization of the criteria and their construction in accordance with the theory of statistical hypothesis distinction, as well as by additional use of information on atmospheric turbulence. Based on the analysis of radar signals reflected from the meteorological events of shower, thunderstorm, and hail, probability distributions of reflectivity and specific dissipation rate of turbulent energy were obtained. Statistical analysis of probability distribution densities was carried out for: the maximum value of reflectivity Zmax, its dependence on height H(Zmax), as well as the maximum specific dissipation rate of turbulent energy EDRmax and the value H(EDRmax). The classification criterion based on the maximum probability functional was chosen to determine the structure of classification algorithms and decision rules. At the same time under the acceptable confidence is accepted the value of the probability of correct classification not lower than 0.8. For the accepted criterion the decision thresholds are constructed and the complete matrices of classification probabilities are calculated. The results of calculations showed that the worst informativeness in the classification of dangerous meteorological events of cumulonimbus cloudiness have parameters H(Zmax), H(EDRmax). Parameters Zmax, EDRmax have greater separating ability, but even for them the confidence of classification is unacceptable. In the article to increase the confidence of classification the joint use of features in the form of multivariate probability distribution densities of information parameters was applied. The best results are achieved when three p(Zmax,H(Zmax),EDRmax) and four p(Zmax,H(Zmax),EDRmax,H(EDRmax)) features are used. In the probability matrices for these cases, the maximum and acceptable at 0.8 level of probabilities of correct classification are achieved. Thus, the expansion of the feature space due to atmospheric turbulence is justified in the problem under consideration. These results will be refined with increasing observation time and will vary for different climatic zones. In general, the decision thresholds for classifying dangerous meteorological events of cumulonimbus cloudiness should be adaptive.

Spatial and temporal characteristics and weather situation analysis of summer monsoon rainstorms in Yangjiang

Abstract The annual average rainfall of summer monsoon rainstorms in Yangjiang is highest in the southeast, decreasing towards the wings. Rainfall most frequently occurs in the early morning and around noon in June, with the western region experiencing the peak slightly earlier than other areas. Short-duration intense rainfall in the south occurs most frequently between 02:00 and 08:00, while in the north, it is more common between 08:00 and 14:00; the 6-hour accumulated rainfall ≥50mm most frequently occurs between 02:00 and 08:00. The weather situation conducive to monsoon rainstorms involves the stability of the South Asian high pressure at 200hPa over the Indochinese Peninsula, with Yangjiang at 500hPa ahead of the southern trough. During the early morning, the southwest monsoon strengthens from 925hPa over the northern Bay of Bengal to the northern South China Sea, pushing northward to Yangjiang’s land, creating the atmospheric conditions for monsoon rainstorms. Moisture primarily enters from the Indochinese Peninsula through the South China Sea, gradually increasing at night and reaching its peak from late night to early morning. The moisture channel and convergence height are below 700hPa. Atmospheric upward motion extends from the mid-to-upper levels to near the surface in the early morning, with the strong center moving from south to north.

Optimizing ORB-SLAM For Varied Weather Conditions Using Genetic Algorithm

Abstract In the rapidly evolving domains of self-driving cars, the resilience of Simultaneous Localization and Mapping (SLAM) algorithms to varying environmental conditions remains a critical challenge. This paper leverages the CARLA simulator to create comprehensive datasets that encompass an array of weather scenarios, ranging from clear sky to complex combinations of fog and rain, during both daytime and nighttime. The primary objective of this study is to optimize the performance of ORB-SLAM2 under these harsh conditions, improving resilience and robustness against different weather conditions. The evaluation is conducted using the Root Mean Square Error (RMSE) as the key metric. Genetic Algorithm (GA) is developed to optimize the parameters of ORB-SLAM. The GA aims to reduce the RMSE for each unique weather situation. The results show a significant improvement in ORB-SLAM’s performance and resilience, contributing to its potential applications in the broader landscape of autonomous systems and intelligent mobility networks.

Convolutional Encoding and Normalizing Flows: A Deep Learning Approach for Offshore Wind Speed Probabilistic Forecasting in the Mediterranean Sea

Abstract The safe and efficient execution of offshore operations requires short-term (1–6 h ahead) high-quality probabilistic forecasts of metocean variables. The development areas for offshore wind projects, potentially in high depths, make it difficult to gather measurement data. This paper explores the use of deep learning for wind speed forecasting at an unobserved offshore location. The proposed convolutional architecture jointly exploits coastal measurements and numerical weather predictions to emulate multivariate probabilistic short-term forecasts. We explore both Gaussian and non-Gaussian neural representations using normalizing flows. We benchmark these approaches with respect to state-of-the-art data-driven schemes, including analog methods and quantile forecasting. The performance of the models and resulting forecast quality are analyzed in terms of probabilistic calibration, probabilistic and deterministic metrics, and as a function of weather situations. We report numerical experiments for a real case study off the French Mediterranean coast. Our results highlight the role of regional numerical weather prediction and coastal in situ measurement in the performance of postprocessing. For single-valued forecasts, a 40% decrease in RMSE is observed compared to the direct use of numerical weather predictions. Significant skill improvements are also obtained for the probabilistic forecasts, in terms of various scores, as well as an acceptable probabilistic calibration. The proposed architecture can process a large amount of heterogeneous input data and offers a versatile probabilistic framework for multivariate forecasting.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

E-suite for Soldiers based on Photovoltaic Cells

We are urgently attempting to power the technology that surround us with clean energy. However, It never occurs to me to make bulletproof coats intelligent. In addition to a basic Kevlar vest, soldiers who defend our nation should receive an E-Uniform that operates on sustainable energy. This study article suggests such a uniform. The attire improves a soldier's ability to withstand extremes of temperature in adverse weather situations. It has a solar panel installed that charges the battery. The necessary heating and cooling effect is provided by the mounted Peltier plate. Additionally, the soldier's clothing incorporates GPS to track their precise location and GSM to make touch with them. Additionally, it has a metal detector to locate metal objects like bombs and is naturally camouflaged. Keywords—Kevlar, Peltier plate, GPS, GSM, Metal Detector,Camouflage..

Unlocking Inventory Efficiency: Harnessing Machine Learning for Sales Surge Prediction

Purpose: Sales forecasting plays a crucial role in inventory optimization for retail stores, especially during special events such as promotions, advertisements, holiday season, weather, social and economic situations etc. These events drive significant changes in customer buying patterns. Some of these events are captured in the current forecasting models as part of trend, seasonality, and cyclicality. But many times, unexpected local events such as extreme weather conditions, riots, and regional events such as marathons, concerts have a significant impact on sales surges which are usually not captured in the sales forecast. This leads to inventory being out of stock and store managers placing last-minute manual orders. By accurately predicting these sales pattern changes, retailers can make informed decisions, ensuring optimal inventory levels and maximizing profits. Methodology: In this paper, a data-driven solution was discussed that leverages machine learning models to predict sales pattern changes and surges during local events in a specific geographical location. Web scraping can be used to gather data on local events from a range of online sources, including Google News, local news channels, and social media platforms. By extracting pertinent details about upcoming events, it is possible to compile a thorough database of local happenings. Findings: Data Analysis of historical sales data mapped to its local events can provide insights on key department-categories where there is a surge in sales. Machine learning models can be used to analyze, experiment and train historical sales data in conjunction with the events data. Unique contribution to theory, practice and policy: Machine learning models would be trained to capture the complex relationships between different local events and their impact on sales. Therefore the study recommends using the machine learning approaches specified to consider various factors, such as event type, location, duration, and historical sales patterns, our models can effectively predict sales fluctuations during specific events.

Using the Sensible Heat Flux Eddy Covariance-Based Exchange Coefficient to Calculate Latent Heat Flux from Moisture Mean Gradients Over Snow

In absence of the high-frequency measurements of wind components, sonic temperature and water vapour required by the eddy covariance (EC) method, Monin–Obukhov similarity theory (MOST) is often used to calculate heat fluxes. However, MOST requires assumptions of stability corrections and roughness lengths. In most environments and weather situations, roughness length and stability corrections have high uncertainty. Here, we revisit the modified Bowen-ratio method, which we call C-method, to calculate the latent heat flux over snow. In the absence of high-frequency water vapour measurements, we use sonic anemometer data, which have become much more standard. This method uses the exchange coefficient for sensible heat flux to estimate latent-heat flux. Theory predicts the two exchange coefficients to be equal and the method avoids assuming roughness lengths and stability corrections. We apply this method to two datasets from high mountain (Alps) and polar (Antarctica) environments and compare it with MOST and the three-layer model (3LM). We show that roughness length has a great impact on heat fluxes calculated using MOST and that different calculation methods over snow lead to very different results. Instead, the 3LM leads to good results, in part due to the fact that it avoids roughness length assumptions to calculate heat fluxes. The C-method presented performs overall better or comparable to established MOST with different stability corrections and provides results comparable to the direct EC method. An application of this method is provided for a new station installed in the Pamir mountains.

Analysis of Pothole Detection Accuracy of Selected Object Detection Models Under Adverse Conditions

Abstract Potholes detection is an essential aspect of road safety and road infrastructure maintenance. Potholes, which are typically caused by a combination of heavy traffic and weather, are depressions or holes in the road surface that can cause damage to specific parts of a vehicle. Autonomous vehicles, in particular, must be capable of detecting and avoiding them. Hitting a deep or sharp-edged pothole at high speed can lead to loss of control or even an accident. This makes pothole detection all the more important. The accuracy of pothole detection systems installed in autonomous vehicles may be significantly impaired by adverse weather and bad light conditions. Therefore, the classification accuracy of selected well-known computer vision models for pothole detection under these specific conditions has been investigated. The results were then compared with state-of-the-art methods. Our findings showed that we outperformed many of them when used under adverse weather and low light situations. This paper presents valuable insights into the precision of various computer vision models for potholes detection. It may aid in selecting the optimal model for a specific application.

Satellite-based entanglement distribution and quantum teleportation with continuous variables

Advances in satellite quantum communications aim at reshaping the global telecommunication network by increasing the security of the transferred information. Here, we study the effects of atmospheric turbulence in continuous-variable entanglement distribution and quantum teleportation in the optical regime between a ground station and a satellite. More specifically, we study the degradation of entanglement due to various error sources in the distribution, namely, diffraction, atmospheric attenuation, turbulence, and detector inefficiency, in both downlink and uplink scenarios. As the fidelity of a quantum teleportation protocol using these distributed entangled resources is not sufficient, we include an intermediate station for either state generation, or beam refocusing, in order to reduce the effects of atmospheric turbulence and diffraction, respectively. The results show the feasibility of free-space entanglement distribution and quantum teleportation in downlink paths up to the LEO region, but also in uplink paths with the help of the intermediate station. Finally, we complete the study with microwave-optical comparison in bad weather situations, and with the study of horizontal paths in ground-to-ground and inter-satellite quantum communication.

Meteorological Characteristics of a Continuous Ice-Covered Event on Ultra-High Voltage Transmission Lines in Yunnan Region in 2021

Yunnan plays a pivotal role in transmitting electricity from west to east within China’s Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China’s Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of “supercooling and warm rain” caused by the “warm–cold” temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events.