Snow Drift Research Articles

Comparison of varied complexity parameterizations in estimating blowing snow occurrences

Blowing snow has a major impact on the spatial–temporal evolution of snow cover. An accurate prediction of the initiation of snow particle movement is one of the prerequisites for blowing snow modeling. Previous studies have proposed varied complexity parameterizations to estimate the occurrence of blowing snow. However, a quantitative evaluation of the performance of different schemes in blowing snow identification remains lacking, particularly outside the regions where empirical schemes were proposed. This study details a comparative study of the performance of five distinct parameterizations with varying degrees of complexity in estimating the occurrence of blowing snow over the central French Alps. Our results show that less than half of blowing snow events can be accurately detected at most stations by traditional schemes, for example, the constant and temperature-based empirical threshold wind speed schemes, probability estimation, and physically based methods. In addition, the spatial variability of wind speed can result in considerable spatial variability in the occurrence of blowing snow, which cannot be captured by traditional methods. Decision tree-based models can detect blowing snow occurrences with higher accuracy, and the spatial variation in snow transport can be captured by including information on blowing snow occurrences at low wind speed stations. However, regardless of the training strategies employed, the decision tree-based model employed here estimated far too many false alarms. This study contributes to blowing snow modeling by presenting the current state-of-the-art and limitations of various schemes for detecting blowing snow occurrences, and highlights the importance of parameterization modification, key parameter calibration, and optimization.

Impact of Changing Arctic Sea Ice Extent, Sea Ice Age, and Snow Depth on Sea Salt Aerosol From Blowing Snow and the Open Ocean for 1980–2017

AbstractWe evaluate the effects of rapidly changing Arctic sea ice conditions on sea salt aerosols (SSA) produced by oceanic wave‐breaking and the sublimation of wind‐lofted salty blowing snow on sea ice. We use the GEOS‐Chem chemical transport model to assess the influence of changing extent of the open ocean, multi‐year sea ice (MYI), first‐year sea ice (FYI), and snow depths on SSA emissions for 1980–2017. We combine snow depths from the Lagrangian snow‐evolution model (SnowModel‐LG) together with an empirically‐derived snow salinity function of snow depth to derive spatially and temporally varying snow surface salinity over Arctic FYI. We find that pan‐Arctic SSA surface mass concentrations have increased by 6%–12% decade−1 during the cold season (November–April) and by 7%–11% decade−1 during the warm season (May–October). The cold season trend is due to increasing blowing snow SSA originating from FYI: as MYI is replaced by FYI with thinning snow depths, snow surface salinity increases by more than 11% decade−1. During the warm season, rapid sea ice loss and thus increasing open ocean SSA are the cause of modeled SSA trends. Observations of SSA mass concentrations at Alert, Canada display positive trends during the cold season (10%–12% decade−1), consistent with our pan‐Arctic simulations. During fall, Alert observations show a negative trend (−18% decade−1), due to locally decreasing wind speeds and thus lower open ocean emissions. These significant changes in SSA concentrations could potentially affect past and future bromine explosions and Arctic climate feedbacks.

Avalanche Hazards Around Kedarnath Temple, Mandakini River Valley, Uttarakhand – A Case Study

Abstract The Shri Kedarnath town is located in the western extremity of the Central Himalaya (30°44'6.7" N; 79°04'1"E) in the Mandakini River valley. This has a total catchment area of ~67 km2 (up to Rambara), out of which 23% area is covered by glaciers. In September 2022, avalanches were observed repeatedly in the mountains to the north of Kedarnath town. Based on aerial survey and ground observations of the area around Kedarnath, we found that the avalanches initiated ~6 km (Aerial distance) uphill of the Kedarnath Temple from the peaks of the Mahalaya Parvat (5970 m asl) of the Companion Glacier adjacent to the Chorabari Glacier. The Companion Glacier does not have a well-defined accumulation zone but receives accumulation mostly from the avalanches. The run-out and deposition zones of the avalanches are located ~5 km uphill of the Kedarnath Temple. The avalanche activity thus poses no threat to the Kedarnath Temple and township. In addition, fifteen avalanche sites (A1-A15) have been identified based on slope morphology, grass clearing, the presence of snow drifts, and debris cones in the area. We observed that the Temple of Shri Kedarnath Complex is located at a reasonably safe place as far as avalanche hazards are concerned.

Editorial: Snow and ice disaster: Formation mechanism and control engineering

Snow is one of the most active natural elements on the earth. It is an important material source of the 6 polar ice sheet and alpine glaciers. The spatial and temporal distribution and evolution of snow cover 7 profoundly affect the global hydrological cycle, ecosystems, climate evolution, and other natural 8 processes. On the other hand, snow and ice disasters, such as snow avalanches, snowstorms, wind 9 blown snow, snowmelt floods, building collapses, traffic hazards, etc, can cause great damage to 10 human lives and social economies. Thus, studies on the physical processes and formation mechanism 11 of snow/ice disasters, prediction and early warning systems, as well as control engineering are of 12 great significance to preventing snow/ice disasters from damaging society. The research on related 13 disasters also involves multi-physical field coupling, strongly nonlinear, multi-scale, and other 14 scientific common problems. As well as the interaction between atmospheric turbulence and particle The cryosphere is a key component of the Earth system. The distribution of regional snow/ice cover 36 is affected mainly by factors such as turbulent wind field, topography, snowfall, and temperature. As global warming continues, extreme weather and climate events frequently occur, including snow/ice 38 disasters. Therefore, future studies are still needed on the improvement of the snow/ice disaster 39 prediction model and formation mechanism.

Blowing Snow at McMurdo Station, Antarctica During the AWARE Field Campaign: Multi‐Instrument Observations of Blowing Snow

AbstractIn polar regions, blowing snow (BLSN) can play a substantial role in regional thermodynamics and radiation properties. The extent of the impact depends upon the depth of the BLSN layer, a property that is difficult to measure and not well understood. The West Antarctic Radiation Experiment (AWARE) Field Campaign saw the deployment of a large suite of observational instruments to McMurdo Station, Antarctica, allowing for in‐depth investigation of BLSN. During the year of the campaign, BLSN occurred ∼7.4% of the time. In this study, additional remote sensing observations are used to supplement an existing ceilometer‐based BLSN characterization algorithm creating a multi‐instrument (MI) depth estimation algorithm to yield layer depths with more certainty. Backscatter coefficient and linear depolarization ratios from the micropulse and high spectral resolution lidar (HSRL) are incorporated along with color ratio derived from HSRL backscatter and Ka‐band radar reflectivity observations. This provided estimates of BLSN depth that were less impacted by noise/artifacts in any one set of observations. When applied to the data from AWARE, the MI algorithm yielded an average depth of 168.2 m with ∼60% of plumes less than 200 m in depth. Events occurring with precipitation saw the most value in the additional instruments, with the suite of observations allowing for more separation of BLSN particles from fallstreaks.

Analysis of Multiyear Blowing Snow Occurrences in the French Alps

Abstract Wind-driven snow transport has important implications for spatial–temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the French Alps. Due to the paucity of near-surface observations, our knowledge on the spatiotemporal variability of blowing snow occurrences is rather limited. Based on multiyear in situ observations, the spatial–temporal variability in the occurrence of blowing snow events in the French Alps was presented to investigate potential links with ambient meteorological conditions. Statistical analysis of the observations demonstrates that blowing snow events are frequently observed with substantial spatiotemporal variability. Most stations experienced snow transport one out of every five days throughout winter, and the corresponding cumulative hours with blowing snow occurrence accounted for 8% of the month in winter. Blowing snow events generally last 4–8 h in winter and early spring. The likelihood of blowing snow occurrences increases with wind speed but with divergent patterns across snow types. The frequency of blowing snow occurrences with concurrent snowfall is substantially higher than that without concurrent snowfall, although high spatiotemporal variability was found. The considerable variation in snow transport frequency can be explained by contrasting meteorological conditions, local climate, snowpack properties, and topography (elevation and aspect). The temperature-based empirical scheme failed to recognize individual occurrence of blowing snow events because of the significantly overestimated threshold wind speeds, highlighting the importance of validation using in situ observations. Our results contribute to the understanding of spatiotemporal occurrence of blowing snow events and facilitate the development of blowing snow models.

Analysis of physicochemical and antioxidant properties of Malus spp. petals reveals factors involved in flower color change and market value

Flower color is the most important ornamental character trait of Malus species. The study aimed to clarify the roles of phenolic compounds and compositions in the petal color change of Malus spp. cultivars at various flowering stages. Meanwhile, the antioxidant activity in the petals of Malus spp. was detected to estimate the potential of market application. Eight Malus spp. cultivars were divided into four systems according to the flower color, namely dark-red (‘Royalty’ and ‘Perfect Purple’), rose-red (‘Kelsey’ and ‘Sparkler’), pink-red (‘Strawberry Parfait’ and ‘Pink Spires’), and white (‘Jewelberry’ and ‘Snow Drift’). Total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), and antioxidant activity of the petals were measured. The results revealed that high levels of TPC, TFC, TAC, and antioxidant activity in petals significantly decreased during flower development. These indicators were significantly and positively related to each other. Abundant phenolic metabolites in petals were determined by HPLC-DAD, and these compounds included anthocyanins, flavonols, flavanols, flavonones, phenolic acids, and dihydrochalcones. Anthocyanin concentrations and ratios in petals were notably different among the four color systems, with the highest levels noted in dark-red colored flowers, followed by rose-red, pink-red, and white flowers. Cyanidin-3-O-galactoside was the major anthocyanin noted. Its concentration reduced significantly with flower development, which is described as the primary cause of petal color fading. In addition, the petals contained abundance quercetin-3-O-glucuronide, hyperoside, taxifolin, chlorogenic acid, ferulic acid, phloridzin and other substances known for their antioxidant activities. Given these findings, we propose that the flowers of Malus spp. hold good utilization value as new sources of bioaccessible polyphenols and strong potential for development. Especially, the flowers at early stages will be supplements to the natural colorant market.

Impact of Clouds and Blowing Snow on Surface and Atmospheric Boundary Layer Properties Over Dome C, Antarctica.

Clouds and blowing snow (BLSN) occur frequently over Antarctica, where it is critical to understand their feedbacks to surface and atmospheric boundary layer processes. Dome C, an elevated East Antarctic station, dominated by lengthy periods of surface longwave (LW) radiative cooling, is selected to reveal cloud and BLSN impacts within a largely stable environment. The sky condition is classified as clear, cloudy, or BLSN, using 3years of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite data. Co-located and contemporaneous in situ observations are used to investigate the relationship of sky condition with surface and atmospheric boundary layer thermal structure, focusing on seasonal variability. Results show that increased downwelling LW radiation from clouds abate surface radiative cooling losses, contributing to warming during all seasons. An increase of 3°C in the mean surface air temperature is observed during spring, whereas, a more dramatic rise (around 10°C), due to accompanying large-scale subsidence, is observed during fall and winter in association with clouds. For all seasons, the wind speed and wind speed shear are strongest during BLSN events, and the surface-based inversion is weakened by cooling which peaks in a shallow above-surface turbulent layer. The stronger background stability during fall and winter seasons, restricts turbulence and BLSN depths generally to the lowest tens of meters. The Earth's cryosphere is among the most rapidly evolving yet least well-observed regions, and knowledge of clouds and BLSN interactions with the typical stable atmospheric boundary layer can help further understand energy and moisture exchanges.

Drivers of spatiotemporal patterns of surface water inputs in a catchment at the rain-snow transition zone of the water-limited western United States

Spatial and temporal dynamics of rainfall and snowmelt (i.e., surface water inputs, SWI) control soil moisture, groundwater recharge, and streamflow at annual, seasonal, and event scales. In the rain-snow transition zone, comprising a large portion of the mountainous western United States, there is limited understanding of the sensitivity of spatiotemporal SWI dynamics across hydrologically variable water years (WYs). We modeled rainfall and snowpack dynamics in a small headwater catchment (1.8 km2) spanning the rain-snow transition in southwestern Idaho, USA, for two hydrologically distinct WYs (2011 and 2014). In wet WY 2011 and dry WY 2014, total precipitation drove spatial variability in annual SWI. Snow drifts generated more SWI (901–2080 mm) than high-elevation scour zones (442–640 mm), which generated less SWI than mid-elevation, non-drift locations (452–784 mm). Seasonally, energy fluxes differed most during the snowmelt period, where higher net radiation at lower elevations and south-facing slopes drove SWI production. At the rain-on-snow (ROS) event scale, higher elevations and north-facing slopes generated 15–20 % of annual SWI, due mainly to higher turbulent fluxes. The most productive ROS events occurred after peak snow water equivalent (SWE), when rainfall fell onto ripe snowpacks. Snow drift locations were less susceptible to melt during ROS events, offset by the larger cold content and snowpack mass. Thus, catchment water resources depend on SWI magnitude, location, and timing, which are moderated by drift persistence at all temporal scales. As the climate warms, shifts in spatiotemporal SWI distribution are expected with declines in snowfall and snowfall redistribution in this area.

Field Measurements and Numerical Simulation of Snowdrift on Railway Subgrade

Abstract Snowdrift is a major natural hazard, endangering basic installation and human life in cold regions. Drifting snow can cause snow grains to accumulate on the road, blocking transportation, reducing visibility, and even leading to fatal traffic accidents. Field measurements and numerical simulations of roadbed snow characteristics were carried out along the railway in snowy weather to study the effect of snowdrift on railway subgrade. The influence of a typical roadbed structure is considered, and the basic model of a typical subgrade structure under the action of snowdrift is proposed. Snow particles that accumulate on the embankment present a U-shaped distribution along the inflow direction. Within the increase of embankment height, wind velocity on the roadbed surface is accelerated, and snow cover is reduced, thus mitigating the snow drift disaster of the embankment. Snow covers at embankment heights of 1 and 2 m are considerably greater than those at 3 and 5 m, and identical with those at roadbed heights of 3 and 5 m. Along the inflow direction, the snow cover on the cutting presents an M-shaped distribution. As cutting depth is reduced, wind velocity inside the cutting is decelerated, and snow cover is increased, which aggravates the snow drift disaster of the cutting. The snow depths of roadbed parts at 1-, 2-, and 3-m depths of the cutting are nearly the same, and that at a 5-m depth of the cutting is relatively small but still larger than that on the embankment. Numerically simulating the snow-driving wind via computational fluid dynamics, which can consider the motion characteristics of snow particles and their influence on flow field, is feasible. Simulation results agree with the measured results. Thus, this research provides a path for the further prediction of snow cover pattern.

Optimizing embankment structures in a snowy permafrost region of the pan-Arctic based on a coupled numerical model

In pan-Arctic permafrost regions, snow drift may lead to more snow accumulation around the road, which prevents the heat release from the underlying soil in winter and aggravates the permafrost degradation beneath the road. However, the study of snow distribution patterns around the road is insufficient, which makes it difficult to accurately evaluate the long-term stability of the road. To investigate the thermal regime of the road and optimize the road structure in permafrost regions under the effect of snow drift, based on heat transfer and multiphase flow theories, a coupled numerical model is presented. The model includes snow transportation around the embankment, natural convection of air in the crushed-rock layer, and heat conduction in soil and snow layers. The results of model simulation show that an embankment with a height of 4 m leads to heavier snow accumulation on both sides of the embankment in winter compared to an embankment with a height of 3 m, which accelerates permafrost degradation. The full crushed-rock embankment with a height of 3 m can be used to counteract the permafrost degradation caused by snow accumulation to some extent, while the soil temperature beneath the toe of the embankment slope is still noticeably higher. By contrast, when the U-shaped crushed-rock embankment (UCRE) with a height of 3 m and 1:6(vertical: horizontal) side slopes is used, the snow accumulation around the embankment is greatly reduced, the embankment slope delivers better cooling performance. Considering the climate warming at a rate of 0.067 ℃ year−1 in pan-Arctic regions, the thermal stability of the UCRE can still be ensured after 20 years of its construction. These results provide a valuable reference for road construction in pan-Arctic snowy permafrost regions.

Evaluation of the long-term thermal stability of a crushed-rock revetment embankment in pan-Arctic permafrost regions under the effect of snow drift

In the pan-Arctic region, snow drift events usually result in heavy snow accumulations on both sides of road embankment which severely affects the energy exchange process at the embankment slopes and further changes the temperature distribution in the embankment and its subsoil. However, there is a lack of research on the prediction of snow distribution patterns around the embankment, which makes it difficult to accurately assess the impact of snowdrift on the thermal stability of the embankment in permafrost regions, and the embankment structure cannot be reasonably optimized accordingly. Herein, using the theories of two-phase flow and heat transfer, a numerical model is developed to evaluate the long-term thermal stability of embankments with different structures under the effect of snow drift. The results show that snow deposition occurs at the toe of the windward side slope and the whole leeward side of the embankment under the effect of snow drift. If the side slope of the embankment is reduced from 1:2 to 1:5 (vertical:horizontal), the snow accumulations on both sides of the embankment are significantly reduced, and the subsoil refreezes more effectively in cold season. When a crushed-rock revetment embankment (CRRE) with 1:5 side slopes is used, the soil beneath the embankment is sufficiently cooled, the warming of permafrost beneath both sides of the embankment caused by snow accumulation is eliminated, and the temperature distribution in the embankment and subsoil has a good symmetry in warm season. The crushed-rock revetment is thus proved to be an applicable and effective structure to ensure the long-term stability of the embankment in pan-Arctic permafrost regions.

Interaction between games give rise to the evolution of moral norms of cooperation.

In many biological populations, such as human groups, individuals face a complex strategic setting, where they need to make strategic decisions over a diverse set of issues and their behavior in one strategic context can affect their decisions in another. This raises the question of how the interaction between different strategic contexts affects individuals' strategic choices and social norms? To address this question, I introduce a framework where individuals play two games with different structures and decide upon their strategy in a second game based on their knowledge of their opponent's strategy in the first game. I consider both multistage games, where the same opponents play the two games consecutively, and reputation-based model, where individuals play their two games with different opponents but receive information about their opponent's strategy. By considering a case where the first game is a social dilemma, I show that when the second game is a coordination or anti-coordination game, the Nash equilibrium of the coupled game can be decomposed into two classes, a defective equilibrium which is composed of two simple equilibrium of the two games, and a cooperative equilibrium, in which coupling between the two games emerge and sustain cooperation in the social dilemma. For the existence of the cooperative equilibrium, the cost of cooperation should be smaller than a value determined by the structure of the second game. Investigation of the evolutionary dynamics shows that a cooperative fixed point exists when the second game belongs to coordination or anti-coordination class in a mixed population. However, the basin of attraction of the cooperative fixed point is much smaller for the coordination class, and this fixed point disappears in a structured population. When the second game belongs to the anti-coordination class, the system possesses a spontaneous symmetry-breaking phase transition above which the symmetry between cooperation and defection breaks. A set of cooperation supporting moral norms emerges according to which cooperation stands out as a valuable trait. Notably, the moral system also brings a more efficient allocation of resources in the second game. This observation suggests a moral system has two different roles: Promotion of cooperation, which is against individuals' self-interest but beneficial for the population, and promotion of organization and order, which is at both the population's and the individual's self-interest. Interestingly, the latter acts like a Trojan horse: Once established out of individuals' self-interest, it brings the former with itself. Importantly, the fact that the evolution of moral norms depends only on the cost of cooperation and is independent of the benefit of cooperation implies that moral norms can be harmful and incur a pure collective cost, yet they are just as effective in promoting order and organization. Finally, the model predicts that recognition noise can have a surprisingly positive effect on the evolution of moral norms and facilitates cooperation in the Snow Drift game in structured populations.

Numerical Simulation and Forecasting Model of Snow Drift Hazard in Yanchong Expressway with Embankment Form

To reasonably predict the snow drift hazard of expressway embankment in alpine mountainous area, in this paper, the computational fluid dynamics (CFD) numerical simulation method is used to analyze the distribution law of flow field of snow drift, the regression orthogonal design method is used to establish the prediction model of snow drift hazard in the expressway with the embankment form. Results show that different factors have different effects on snow drift hazard, the influence of embankment slope is the largest, followed by the coupling term of embankment slope and wind speed, the wind speed, the coupling term of embankment height and embankment slope, the coupling term of the mountain relative height and embankment slope, and the embankment height, while the influence of the distance between embankment and mountain-embankment slope coupling is the smallest. Numerical simulation and field observation have confirmed that the combined action of the terrain and embankment of Yanchong expressway Chicheng branch line leads to great changes in wind speed, resulting in uneven distribution of snow. A large amount of snow can be accumulated at the foot of the leeward slope of the mountain and the windward slope of the embankment, and there is less snow on the embankment pavement. By comparing the snow drift prediction model and CFD numerical simulation results, the error of the prediction model established in this paper is only 8.67%, which has high applicability. The research of this paper can provide a theoretical reference for the prediction and prevention of snow drift hazard on expressway embankment in alpine mountainous area.

SAFETY REQUIREMENTS FOR SNOW LOAD ON UNIQUE TRANSPORT INFRASTRUCTURE FACILITIES

Introduction: Wind-induced snow drift is the main reason behind the non-uniform snow load on a snow-covered area. As known, snow load poses a serious hazard to the roofing of buildings and structures. According to the applicable regulatory documents, snow loads for non-standard roofs must be determined based on the results of model tests in wind tunnels. Purpose of the study: We aimed to study snow load on a unique transport infrastructure facility — the world’s first crossborder cable car connecting Blagoveshchensk in Russia to Heihe in China. Methods: We performed model tests to study snow accumulation and drifting with the use of a unique research setup — the Large Gradient Wind Tunnel, courtesy of the National Research Moscow State University of Civil Engineering. Results: Based on climate analysis and tests under different wind attack angles, we obtained distribution patterns for snow deposits on the roofing of the unique transport infrastructure facility under consideration and derived the values of the coefficient μ (the coefficient of transition from the weight of the snow cover on the ground to the snow load on roofing).

Considering snow depositions and transport mechanisms in meteorological analysis of predicting snow loads on buildings

The meteorological conditions determine snow evolutions on and around buildings. Those climatic impacts have been independently considered in the current design codes of buildings, namely the wind pressure and the snow load. But snow load estimates are only based on precipitation assessments without considering the joint effect of wind and snow precipitation which lead to wind dominated deposition and snow transport. Thus, the statistics-based data provided by those building codes seem practically inadequate or at least incomplete. Using the data provided by upper-air and ground meteorological stations in northern China, including snowfall, wind velocity, and temperature, as indicators, we propose a series of statistical relationships covering aspects of snow precipitation and snow drifting. For those meteorological indicators that can affect building snow evolutions, more detailed analyses on their spatial and temporal changes have been achieved. The regionalization of snow precipitation features in northern China has also been proposed based on the clustering analysis on meteorological joint effects. The result shows that the snowfall in most northern regions of China is generally not heavy and in the form of a uniform deposition. The wind dominated depositions around buildings are more likely to appear in the regions around the Bohai Sea.

FlakeOut: A geometric approach to remove wind-blown snow from terrestrial laser scans

Wind-blown snow particles often contaminate Terrestrial Laser Scanning (TLS) data of snow covered terrain. However, common filtering techniques fail to filter wind-blown snow and incorrectly filter data from the true surface due to the spatial distribution of wind-blown snow and the TLS scanning geometry. We present FlakeOut, a filter designed specifically to filter wind-blown snowflakes from TLS data. A key aspect of FlakeOut is a low false positive rate of 2.8x10-4—an order of magnitude lower than standard filtering techniques—which greatly reduces the number of true ground points that are incorrectly removed. This low false positive rate makes FlakeOut appropriate for applications requiring quantitative measurements of the snow surface in light to moderate blowing snow conditions. Additionally, we provide mathematical and software tools to efficiently estimate the false positive rate of filters applied for the purpose of removing erroneous data points that occur very infrequently in a dataset.

Numerical study on snow erosion and deposition around an embankment with a snow fence under snowfall conditions

Snow drift which usually occurs in conjunction with snowfall can significantly alter the distribution pattern of snow cover around roads creating travel hazards for vehicles. To study the snow distribution around a road and evaluate the snow prevention efficiency of snow fence, based on the theories of two-phase flow and snowdrift erosion and deposition, a numerical model is developed. The model includes snowfall during snow drift, spatial distribution characteristics of wind speed and snow phase volume fraction, and dynamic changes of snow drift shape on the ground during drifting. We simulate the distribution characteristics of snowdrift around an embankment without and with the protection of a snow fence and under the conditions of no snowfall and snowfall. The results indicate that snow deposition is greatest on the leeward side of the embankment in comparison to the windward side of the embankment and takes a longer time to reach equilibrium. On the leeward side of the embankment, the snow accumulation rate under the condition of snow falling is higher than that under the condition of no snow falling. Nonetheless, the two conditions both suggest that installation of the snow fence intercepts a large amount of snow behind the snow fence, decreases the snow phase volume fraction near the ground, and reduces the snowdrift accumulation on the leeward side of the embankment within a certain period.

Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems

Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events.

Observations of Drifting Snow Using FlowCapt Sensors in the Southern Altai Mountains, Central Asia

Drifting snow is a significant factor in snow redistribution and cascading snow incidents. However, field observations of drifting snow are relatively difficult due to limitations in observation technology, and drifting snow observation data are scarce. The FlowCapt sensor is a relatively stable sensor that has been widely used in recent years to obtain drifting snow observations. This study presents the results from two FlowCapt sensors that were employed to obtain field observations of drifting snow during the 2017–2018 snow season in the southern Altai Mountains, Central Asia, where the snow cover is widely distributed. The results demonstrate that the FlowCapt sensor can successfully acquire stable field observations of drifting snow. Drifting snow occurs mainly within the height range of 80-cm zone above the snow surface, which accounts for 97.73% of the total snow mass transport. There were three typical snowdrift events during the 2017–2018 observation period, and the total snowdrift flux caused during these key events accounted for 87.5% of the total snow mass transport. Wind speed controls the occurrence of drifting snow, and the threshold wind speed (friction velocity) for drifting snow is approximately 3.0 m/s (0.15 m/s); the potential for drifting snow increases rapidly above 3.0 m/s, with drifting snow essentially being inevitable for wind speeds above 7.0 m/s. Similarly, the snowdrift flux is also controlled by wind speed. The observed maximum snowdrift flux reaches 192.00 g/(m2·s) and the total snow transport is 584.9 kg/m during the snow season. Although drifting snow will lead to a redistribution of the snow mass, any accumulation or loss of the snow mass is also affected synergistically by other factors, such as topography and snow properties. This study provides a paradigm for establishing a field observation network for drifting snow monitoring in the southern Altai Mountains and bridges the gaps toward elucidating the mechanisms of drifting snow in the Altai Mountains of Central Asia. A broader network of drifting snow observations will provide key data for the prevention and control of drifting snow incidents, such as the design height of windbreak fences installed on both sides of highways.