Snowy Conditions Research Articles

Heat storage and release performance experiment of externally hung phase change solar greenhouse in severe cold regions of Northeast China - Taking Fuxin City as an example

The traditional solar greenhouses in severe cold regions of northeast China have poor heat storage and thermal insulation performance, and the abundant solar energy resources cannot be utilized rationally. Phase change energy storage is considered to be one of the effective ways to cope with this problem. The price, encapsulation leakage and arrangement of PCM are the bottleneck problems restricting its practical application. This paper used the modified calcium chloride hexahydrate-based phase change material as the energy storage medium. A PVC-U tube was used for encapsulation and external hanged on the inner surface of the north wall of the partitioned greenhouse. Based on the 51 days of field comparison test, the comparative greenhouse heat storage and release performance under typical sunny, cloudy and snowy conditions was discussed. Through the experiments, it was found that the externally hung phase change energy storage device increased the effective accumulated temperature by 21.1 %, changing the dynamic heat transfer relationship between the envelope structure and indoor air. It confirmed the feasibility of promoting the externally hung phase change energy storage device in the severe cold region of Northeast China.

Objects Detection With 3-D Roadside LIDAR Under Snowy Weather

Rain and snowfall will increase noise, change the resolution of objects in the point cloud and present great challenges to the accurate recognition of traffic objects. Accordingly, this article proposes an efficient real-time method for roadside 3-D light detection and ranging (LIDAR) background point cloud extraction and object segmentation under snowy weather. We first use a historical point cloud sequence to quickly construct a background model, extract the background point cloud from the current frame by using a background difference method and update the background model in real-time. Then, the noise caused by snowfall in the non-background point cloud is filtered based on the beam density difference of the object point clouds. Finally, the remaining object point cloud is accurately segmented based on the proposed hierarchical object clustering method. We use an intelligent roadside system equipped with 3-D roadside LIDAR to collect point cloud data in a snowfall environment and evaluate the proposed method qualitatively and quantitatively. Experimental results show that the proposed method effectively avoids the problem of under-segmentation and over-segmentation of object point clouds under snowy conditions, and the precision and recall rate of traffic object segmentation reached 96.41% and 95.02%, respectively, thereby indicating a significant improvement in the accuracy and reliability of traffic object detection using roadside LIDAR under snowy conditions.

Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Abstract. Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek the optimal parameter values in the snow-related processes – snow cover, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in situ surface observations and remotely sensed satellite data. Snow data from observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the fractional snow cover, maximum snow albedo of fresh snow, and fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of fractional snow cover on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root mean squared errors by 17.0 %, 6.2 %, and 3.3 % in snow depth, snow albedo, and fractional snow cover, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 44.2 % and 31.0 %, respectively.

Extracting High-Precision Vehicle Motion Data from Unmanned Aerial Vehicle Video Captured under Various Weather Conditions

At present, there are many aerial-view datasets that contain motion data from vehicles in a variety of traffic scenarios. However, there are few datasets that have been collected under different weather conditions in an urban mixed-traffic scenario. In this study, we propose a framework for extracting vehicle motion data from UAV videos captured under various weather conditions. With this framework, we improve YOLOv5 (you only look once) with image-adaptive enhancement for detecting vehicles in different environments. In addition, a new vehicle-tracking algorithm called SORT++ is proposed to extract high-precision vehicle motion data from the detection results. Moreover, we present a new dataset that includes 7133 traffic images (1311 under sunny conditions, 961 under night, 3366 under rainy, and 1495 under snowy) of 106,995 vehicles. The images were captured by a UAV to evaluate the proposed method for vehicle orientation detection. In order to evaluate the accuracy of the extracted traffic data, we also present a new dataset of four UAV videos, each having 30,000+ frames, of approximately 3K vehicle trajectories collected under sunny, night, rainy, and snowy conditions, respectively. The experimental results show the high accuracy and stability of the proposed methods.

Ice Crystal Drag Model Extension to Snowflakes: Experimental and Numerical Investigations

To ensure safe flight under snowy conditions, aircraft manufacturers must demonstrate that each engine and its air inlet system can operate throughout the flight power range of the engine (including idling) in both falling and blowing snow conditions. This study is part of an effort to develop models for snow accretion. The focus here is on the modeling of snowflake transport by the carrier airflow and more specifically on the modeling of the drag coefficient of snowflakes. Based on comparisons between numerical and experimental results, we show that, provided that the complex shape of the snowflakes is accounted for, the models initially developed for ice crystal icing can also be used to estimate the drag coefficient of a snowflake. We also propose a methodology to estimate the geometric parameters of the drag models from two-dimensional images of the particles, which in general are the only available experimental data.

LiDAR- and Radar-Based Robust Vehicle Localization with Confidence Estimation of Matching Results

Localization is an important technology for autonomous driving. Map-matching using road surface pattern features gives accurate position estimation and has been used in autonomous driving tests on public roads. To provide highly safe autonomous driving, localization technology that is not affected by the environment is required. In particular, in snowy environments, the features of the road surface pattern may not be used for matching because the road surface is hidden. In such cases, it is necessary to construct a robust system by rejecting the matching results or making up for them with other sensors. On the other hand, millimeter-wave radar-based localization methods are not as accurate as LiDAR-based methods due to their ranging accuracy, but it has successfully achieved autonomous driving in snowy environments. Therefore, this paper proposes a localization method that combines LiDAR and millimeter-wave radar. We constructed a system that emphasizes LiDAR-based matching results during normal conditions when the road surface pattern is visible and emphasizes radar matching results when the road surface is not visible due to snow cover or other factors. This method achieves an accuracy that allows autonomous driving to continue regardless of normal or snowy conditions and more robust position estimation.

Environmental drivers of autumn migration departure decisions in midcontinental mallards

BackgroundThe timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale.MethodsWe combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion.ResultsDeparture was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%).ConclusionsOur results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.

Physical and economic impacts of studded tyre use on pavement structures in cold climates

ABSTRACT In cold regions like Alaska of USA, Canada and the northern parts of Europe, using studded tyres is common among the public when driving in icy and snowy conditions. However, studded tyres cause extensive wear to asphalt pavement, reducing pavement life. This study addresses the physical and economic impacts of winter studded tyres on the roadway system to better inform decision makers as they develop alternative solutions and future polices. The approach is applied in a case study from a sample of Alaska statewide road segments. Surveys were employed to examine the extent of the use of studded tyres and cost-effective alternatives. A pavement life-cycle cost review was established considering several variables to discover a realistic cost of roadway resurfacing and rehabilitation. Wear rates due to studded tyres and rut rates due to wheel loads were found for different highway classes. The results indicate higher average wear rates due to studded passenger vehicles on freeways than average rut rates due to heavy wheel loads. The results also indicate lower average wear rates on arterial and collector roads. The estimates show that studded tyre use reduced asphalt surface life by about 7 years on the selected freeway sample in the case study, which is about 47% loss in pavement life based on the initial design life of 15 years. Other road classes experienced lower reductions in service life. Finally, cost analysis was provided to reflect the impact of studded tyres on the state's budget. Countermeasures were suggested, which in turn may help other cold regions develop strategies on the use of new winter tyre technology.

The Impact of Weather and Slope Conditions on the Productivity, Cost, and GHG Emissions of a Ground-Based Harvesting Operation in Mountain Hardwoods

Mountainous hardwood mixed stands offer challenges to timber harvesting operations in practice, including a harsh climate, variable topography, steep terrain, and large-sized timbers. This paper aims to develop productivity and cost models for a mountain-ground-based harvesting operation across the terrain (e.g., slope conditions), stand (e.g., tree volume) environmental (e.g., weather), and yard (e.g., winching distance) variables and to assess GHG emissions related to the equipment in use. This development was implemented in a timber harvesting practice under single-tree selection in mountainous forests of Iran where a motor-manual chainsaw is used for felling and a rubber-tired cable skidder is used for log extraction. The average delay-free productivity was 4.55 m3 for felling and 14.73 m3 h−1 for skidding. Lower production costs and higher productivity rates were observed over the gentle slopes and in sunny conditions. The average production costs ranged between USD 4.27 m−3 for felling and USD 5.35 m−3 for skidding. The average emissions ranged between 0.96 kg m−3 for felling and 7.06 kg m−3 for skidding in snowy conditions over steep slopes. The study’s results confirm avoiding harvesting operations on steep slopes (greater than 35%) and in extreme weather conditions to obtain higher work efficiency and to minimize adverse effects of machinery on forest ecosystems. The results should be of use to harvest managers and forest planners considering the application of ground-based harvesting operations using a semi-mechanized system on a range of operating conditions in mountain hardwood stands.

Camera-Based Lane Detection—Can Yellow Road Markings Facilitate Automated Driving in Snow?

Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving.

Do We Need a Change in Road Winter Maintenance to Accommodate for Automated Vehicles? A State-of-the-Art Literature Review Considering Automated Vehicle Technology’s Usage of Road Infrastructure During Winter

For automated vehicles to be allowed to join the modern car fleet, and, in the future, replace human drivers, they must be able to handle adverse weather, including snowy conditions. This literature review focuses on how automated vehicles utilize the road and how this use is suitable for winter maintenance strategies. Where global navigation satellite system (GNSS) service is unavailable, automated vehicles need bare roads to perform relative navigation based on real-time data about lane markings, obstacles, and road infrastructure. Snow-covered tracks hinder vehicle navigation and lane marking detection, which might generate wheel slippage that in turn causes emergency stop and challenging friction estimates. Although the entry of automated vehicles into the car fleet does not demand change in the strategies of winter maintenance, it does demand higher level of service than today. Maintaining an entire road network on which autonomous vehicles always can operate is tremendously expensive and likely not feasible. One solution could be to add another maintenance class in a bare road strategy, that is, an automated vehicle maintenance class with a high level of service and a set of operational criteria allowing automated vehicles to operate. The maintenance class should be used for certain main routes where there is a high frequency of automated vehicles. A model that recommends preferable routes to the destination based on current road conditions within the operational envelope should be provided to the automated vehicle system.

Correlates of Pedestrian and Cyclist Falls in Snowy and Icy Conditions

Existing international studies consistently report increased numbers of pedestrian and cyclist falls in the winter months, particularly in icy/snowy conditions. Until now, reliable information on the factors contributing to these falls has been rare. This study investigates possible correlates of pedestrian and cyclist falls in icy/snowy conditions in Germany and takes into consideration factors such as behavior and surface conditions with the aim to develop recommendations for improving safety for vulnerable road users. As part of this study, 3,300 people in online surveys and 2,100 in field surveys were asked about their experiences and opinions in relation to icy/snowy conditions and winter maintenance. Of those surveyed, 1,100 pedestrians and 2,200 cyclists also reported details about their most recent fall within the last five years. Furthermore, the behavior of 7,000 pedestrians and 4,500 cyclists was observed with cameras in both snowy and other weather conditions. The presence of snow/ice itself was indicated as a reason for three quarters of pedestrian and two thirds of cyclist falls in snowy/icy conditions. For nearly all of the falls in snowy/icy conditions, surfaces were reported as poorly cleared or not cleared at all. Compared to other weather more pedestrians fell on sidewalks and less while crossing the street during snowy and icy conditions. Cyclists increased their speed on downhill sidewalks in dry conditions and reduced their speed in snowy conditions compared to flat or uphill sidewalks. Most cyclists chose to cycle on sidewalks instead of their own dedicated, but not maintained, facilities (even though they are obliged by law to cycle on the carriageway in such cases). In snowy/icy conditions, 5 % of pedestrians varied their pace or step sequence and 8 % of cyclists drove "wavy lines" or made obvious corrective steering movements, particularly on poorly maintained facilities (compared to very few of such observations in other weather conditions). Half of the respondents rated the winter maintenance at pedestrian facilities to be good and three quarters rated the maintenance at public transport stops as good. In contrast, only 12 % rated winter maintenance on cycling facilities as good. Reasons for not being satisfied included poor quality, delay and insufficient frequency of cleaning as well as the type of grit used. The lack of seamless reliably-cleaned cycle routes was listed as a major problem by most respondents. More than 50 % claimed that previously cleaned pedestrian or cyclist facilities were re-covered with snow from clearing the carriageway.

Connected and Automated Driving in Snowy and Icy Conditions—Results of Four Field-Testing Activities Carried Out in Finland

Connected and Automated Driving in Snowy and Icy Conditions—Results of Four Field-Testing Activities Carried Out in Finland

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

Saturation Flow Rate of Urban At-Grade Signalized Intersection Under Different Climatic Conditions (Case Study: Sattari-Mokhberi Intersection)

This research aimed to compare the saturation flow rates of an urban at-grade signalized intersection under different climatic conditions in order to optimize the traffic light control system of this intersection. Data collection was performed with a video camera. The case studied was the intersection of Sattari-Mokhberi Intersection, in Tehran. The road surface conditions occurring in this intersection were divided into three classes: dry, wet, and snowy. Modeling and simulations were performed with the help of the software Vissim and Synchro. Model calibration was performed using the parameters “desired speed” and “acceleration/deceleration at zero speed”. Data analysis was performed using descriptive statistics and inferential statistics, including analysis of variance (ANOVA), Kolmogorov–Smirnov test, and Tukey and Scheffe tests. The saturation flow for dry, wet, and snowy road conditions was estimated to 1967, 1722, and 1607, respectively. Also, the relationship between saturation headway and sight distance in meters was modeled linearly and logarithmically. The analysis of videos showed that the mean desired speed in wet and snowy road conditions was, respectively, 23% and 36% lower than in normal conditions. The mean desired acceleration at zero speed in wet and snowy road conditions was, respectively, 24% and 30% lower than normal. The simulation results showed a 15% reduction in the total delay of the intersection after the implementation of weather-responsive schedules. The results of this research show that the benefits of adjusting the schedule of traffic lights based on weather are more pronounced in snowy conditions than in rainy conditions.

FEATURES OF SNOW COVER OF SEMI-DESERTS AND DRY STEPPES OF THE CASPIAN SEA ACCORDING TO SATELLITE DATA FOR THE PERIOD 2001...2020

The Snow Depth FEWS NET daily product was used to analyze snowy regime of the upper part of the River Emba basin from January 1 to April 30 for the period of 2001...2020. The Emba River basin is situated in Kazakhstan at the Eastern coast of the Caspian Sea. The area is characterized by the arid and extreme continental climate with dry-steppe and semi-desert landscapes. The population is small and the anthropogenic impact on the snow cover is minimal there. These conditions give an opportunity to identify the natural tendency in long-term changes of snow covering in semidesert zone of Kazakhstan. This paper describes the characteristics of the formation and destruction of the snow cover in the last 20 years. It was indicated that snowy regime has a trigger structure including two states; low-snowy regime and others years. It was shown that the snowy conditions are triggered. There are two modes, the first, as a low-snowy regime (up to 50 % of the entire sample) and the second mode includes other years. Significant variations of snow depth in various years masked many years’ tendencies of snow cover characteristics. But low-snowy regime was observed four times during five last years that can relate with modern decreasing snow covering in semi-desert zone of Kazakhstan.

An evaluation of machine learning methods for speed-bump detection on a GoPro dataset.

Every day, new applications arise relying on the use of high-resolution road maps in both academic and industrial environments. Autonomous vehicles rely on digital maps to navigate when optical sensors cannot be trusted, such as heavy rainfalls, snowy conditions, fog, and other situations. These situations increase the risks of accidents and disable the potentials of real-time mapping sensors. To tackle those problems, we present a methodology to automatically map anomalies on the road, namely speed bumps in this study, using an off-the-shelf camera (GoPro) and Machine Learning (ML) algorithms. We acquired data over a series of differently shaped speed bumps and applied three classification techniques: Naive Bayes, Multi-Layer Perceptron, and Random Forest (RF). With over 96% of classification accuracy, then RF was able to identify speed bumps on a GoPro dataset automatically. The results show a potential of the proposed methodology to be developed in surveying vehicles to produce highly-detailed maps of vertical road anomalies with a fast and accurate update rate.

Dynamic Behavior of Fractional Viscoelastic Tire Tread Blocks on Different Ice Surfaces

ABSTRACT The component of vehicle tires that contacts the road is the tire tread. It consists of several tread blocks with different shape and can be realized by siped structures to improve the traction on the pavement, in particular in wet, icy, or snowy conditions. The blocks are usually made of elastomer materials, which possess viscoelastic behavior. Viscoelasticity can be described by classical rheological models or, alternatively, as shown in this paper, by fractional viscoelastic models. Fractional models can fit the material properties better with fewer parameters. Each single tire tread block shows periodically frictional impacts with the pavement. The contact configurations and transitions among stick and slip or impact and detaching can be formulated as a linear complementarity problem, whose solution estimates the local contact forces. We simulate transient deformations of a tire tread block by developing a discretized fractional viscoelastic model. The focus is on the deformation process of a tread block with siped structure on different ice tracks with frictional contact. In particular the interlocking of the tread block lips with the edges on the ice surface and the self-contact of the lips are considered. The simulation results are compared to measurements on a high velocity linear testing bench. The contact forces of the frictional contact for plane and rough surfaces on ice and also the deformation process of siped tread blocks, where self-contact can occur, are investigated. The qualitative comparison between results out of simulations and experiments shows a good agreement.

防災科学技術研究所による山地積雪環境の長期変動モニタリング 2 −積雪・気象監視ネットワーク(SW-Net)−

防災科学技術研究所による山地積雪環境の長期変動モニタリング 2 −積雪・気象監視ネットワーク(SW-Net)−

Method for black ice detection on roads using tri-wavelength backscattering measurements.

This paper provides a method of detecting black ice on a road surface by multiwavelength noncontact optical technology. The laser sources, wavelengths of 1310, 1430, and 1550 nm, were irradiated on the road surface. Then, we define the ratio of the backscattering power under a certain condition to the backscattering power under dry conditions as the normalized reflectance. It is found that the normalized reflectance under dry, water, black ice, icy, and snowy conditions is different. Therefore, the normalized reflectance can be used to identify black ice on a road surface.