Snowfall Conditions Research Articles

Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions

The aim of this study is to highlight the issue of missed supercooled liquid water (SLW) detection in the current radar/lidar derived products and to investigate the potential of the combined use of the EarthCARE mission and the Arctic Weather Satellite (AWS)—Microwave Radiometer (MWR) observations to fill this observational gap and to improve snowfall retrieval capabilities. The presence of SLW layers, which is typical of snowing clouds at high latitudes, represents a significant challenge for snowfall retrieval based on passive microwave (PMW) observations. The strong emission effect of SLW has the potential to mask the snowflake scattering signal in the high-frequency channels (>90 GHz) exploited for snowfall retrieval, while the detection capability of the combined radar/lidar SLW product—which is currently used as reference for the PMW-based snowfall retrieval algorithm—is limited to the cloud top due to SLW signal attenuation. In this context, EarthCARE, which is equipped with both a radar and a lidar, and the AWS-MWR, whose channels cover a range from 50 GHz to 325.15 GHz, offer a unique opportunity to improve both SLW detection and snowfall retrieval. In the current study, a case study is analyzed by comparing available PMW observations with AWS-MWR simulated signals for different scenarios of SLW layers, and an extensive comparison of the CloudSat brightness temperature (TB) product with the corresponding simulated signal is carried out. Simulated TBs are obtained from a radiative transfer model applied to cloud and precipitation profiles derived from the algorithm developed for the EarthCARE mission (CAPTIVATE). Different single scattering models are considered. This analysis highlights the missed detection of SLW layers embedded by the radar/lidar product and the sensitivity of AWS-MWR channels to SLW. Moreover, the new AWS 325.15 GHz channels are very sensitive to snowflakes in the atmosphere, and unaffected by SLW. Therefore, their combination with EarthCARE radar/lidar measurements can be exploited to both improve snowfall retrieval capabilities and to constrain snowfall microphysical properties.

Numerical simulation of wind–snow flow on a roof considering time-varying snow phase boundaries

Large-span roofs can be damaged by uneven snow loads caused by snow drifting. To predict wind-induced snow loads more accurately, this paper proposes a numerical simulation method. The method considers the interaction of wind and snow, the changing characteristics of snow phase boundaries over time, and corrects for snow deposition and erosion flux. The effectiveness of the method was confirmed by simulating wind–snow flow on a three-centered cylindrical shell and comparing the results with wind tunnel measurements. The simulation considered different snowfall conditions and time-varying snow phase boundaries on a large-span shell-shaped roof. The study investigated the influence of snowfall conditions and dynamic changes in snow phase boundaries on snow cover distribution by comparing simulation results.

Regional variation in branch morphological and physical traits in Abies sachalinensis associated with winter climatic conditions

ABSTRACT In boreal forests, overwintering strategies, including snow adaptation, can be expected in the branches of coniferous trees. Although it is difficult to detect resistance to snow disturbances and few studies have quantified the functional traits related to snowfall resistance, the regional variation associated with winter conditions may provide cues for identifying adaptive traits against snow damage. In common garden trials, we examined the regional variation in branch functional traits in Abies sachalinensis, one of the most important conifers in boreal forest silviculture. Morphological traits reflecting susceptibility to snow accumulation, such as branch estimated area, Young’s modulus and flexural rigidity, which represent deformability to snow loads, and base diameter and xylem density were evaluated in five-year-old branches from eight provenances. The length and estimated area of a branch showed regional variation. Significant regional variation in flexural rigidity rather than Young’s modulus and no significant correlation between xylem density and mechanical traits indicated that the physical properties of branches depend on their size rather than on their materials. A significant correlation with winter climatic conditions was observed between flexural rigidity and total monthly precipitation in January. Our results showed that the thicker the branch is, the larger the branch area and the more rigid it is. This indicates that the branch size-dependent resistance to snow loads might be associated with winter adaptation to local snowfall conditions.

A snowfall climatology of the Ohio River Valley, USA

Snowfall in the Ohio River Valley, USA, presents a relatively unique challenge due to the large gradient of event frequency and magnitude, and subsequent levels of preparation within local communities. Even relatively small magnitude events can cause widespread impacts due to available infrastructure. Here we present a climatology of snowfall conditions and events over a 74-year period using a network of daily observational stations across the region. Snowfall totals and event frequencies both exhibit a southwest to northeast gradient of increasing snowfall, where the majority of snowfall (> 80%) occurs during the core winter months of December through February. There is a clear influence of Lake Erie on snowfall conditions in the northeast corner of the domain, where snowfall frequency, totals, and trends are substantially higher within the lake belt relative to areas further inland. Over time, snowfall significantly increased downwind of Lake Erie by as much as 42%, while significant decreases of over 55% occurred in central Tennessee and eastern Ohio. Intra-seasonally, snowfall totals trended significantly less during November and March for much of the domain, suggesting a compression of the snowfall season to more core winter months. Trends in snowfall frequency were apparent for many sub-regions, however evidence here suggests the trends in snowfall totals were primarily driven by trends in snowfall magnitude per event.

Assessing the Impact of Climate Change on Snowfall Conditions in Poland Based on the Snow Fraction Sensitivity Index

This study focuses on temperature and snowfall conditions in Poland, both of which were analyzed from 1981 to 2020. A 40-year record of daily snow fraction time series values was reconstructed using a unique and global multi-source weighted-ensemble precipitation (MSWEP) product, which provided a spatially and temporally consistent reference for the assessment of meteorological conditions. The average states and trends in snow fraction and temperature were analyzed across several years, focusing on the 6-month cold season (November–April). The impact of temperature on the snow fraction pattern was assessed by introducing a snow fraction sensitivity index. To predict short-term changes in snow conditions, a proxy model was established; it incorporated historical trends in the snow fraction as well as its mean state. This study provides clear evidence that the snow fraction is principally controlled by increases in temperature. A warming climate will thus cause a decline in the snow fraction, as we observed in vast lowland areas. Given the ongoing global warming, by the 2050s, snow-dominated areas may go from covering 86% to only 30% of the country’s surface; they will be converted into transient rain–snow areas. Our results demonstrate that a decline in snow water resources has already occurred, and these resources are expected to diminish further in the near future. New insights into the sensitivity of the snow fraction to climate warming will expand our collective knowledge of the magnitude and spatial extent of snow degradation. Such widespread changes have implications for the timing and availability of soil and groundwater resources as well as the timing and likelihood of floods and droughts. Thus, these findings will provide valuable information that can inform environmental managers of the importance of changing snowfall conditions, guiding them to include this aspect in future climate adaptation strategies.

Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model

As one of the state-of-the-art object detection algorithms, YOLOv5 relies heavily on the quality of the training dataset. In order to improve the detection accuracy and performance of YOLOv5 and to reduce its false positive and false negative rates, we propose to improve the Segment Anything Model (SAM) used for data augmentation. The feature maps and mask predictions generated by the SAM are used as auxiliary inputs for the Mask-to-Mask (M2M) module. The experimental results show that after processing the dataset with the improved Segment Anything Model, the detection performance of YOLOv5 is improved with 99.9% precision and 99.1% recall. The improved YOLOv5 model has a higher license plate recognition accuracy than the original detection model under strong snowfall conditions, and the incidence of false-negative and false-positive is greatly reduced. The enhanced model can meet the requirement of accurate real-time recognition of license plates under strong snowfall weather conditions.

Understanding LiDAR Performance for Autonomous Vehicles Under Snowfall Conditions

Understanding LiDAR Performance for Autonomous Vehicles Under Snowfall Conditions

On the Radar Detection of Cloud Seeding Effects in Wintertime Orographic Cloud Systems

Abstract Recent studies from the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) demonstrated definitive radar evidence of seeding signatures in winter orographic clouds during three intensive operation periods (IOPs) where the background signal from natural precipitation was weak and a radar signal attributable to seeding could be identified as traceable seeding lines. Except for the three IOPs where seeding was detected, background natural snowfall was present during seeding operations and no clear seeding signatures were detected. This paper provides a quantitative analysis to assess if orographic cloud seeding effects are detectable using radar when background precipitation is present. We show that a 5-dB change in equivalent reflectivity factor Ze is required to stand out against background natural Ze variability. This analysis considers four radar wavelengths, a range of background ice water contents (IWC) from 0.012 to 1.214 g m−3, and additional IWC introduced by seeding ranging from 0.012 to 0.486 g m−3. The upper-limit values of seeded IWC are based on measurements of IWC from the Nevzorov probe employed on the University of Wyoming King Air aircraft during SNOWIE. This analysis implies that seeding effects will be undetectable using radar within background snowfall unless the background IWC is small, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Significance Statement Operational glaciogenic seeding programs targeting wintertime orographic clouds are funded by a range of stakeholders to increase snowpack. Glaciogenic seeding signatures have been observed by radar when natural background snowfall is weak but never when heavy background precipitation was present. This analysis quantitatively shows that seeding effects will be undetectable using radar reflectivity under conditions of background snowfall unless the background snowfall is weak, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Alternative assessment methods such as trace element analysis in snow, aircraft measurements, precipitation measurements, and modeling should be used to determine the efficacy of orographic cloud seeding when heavy background precipitation is present.

Numerical method used for the simulation of fluid–solid transitions based on the VOF and SAM models and prediction of snow distributions on large-span roofs

During a snowfall, the surfaces of the snow beds on roofs change dynamically with time and wind conditions. The generation of body-fitted computational fluid dynamics grids for simulating dynamic snow surfaces using the moving-grid method remains challenging. Therefore, a numerical method was developed to simulate the fluid–solid transition based on the volume of the fluid model and solidification and melting models. The snowfall process was divided into n stable steps, and a stable state calculation method was adopted to calculate the air and snow phases. The dynamic snow boundary was simulated at each stage by converting the fluid or solid characteristics of the domain in which the grids resided. Based on the results of the (n−1)th stage, the fluid domain grids buried by the snow were filled with liquid and frozen into the solid domain. Instead, the fluid in the frozen grids, where snow was about to be eroded, was evacuated and converted into fluid-domain grids. Thus, a complex and changeable snow boundary could be adapted without re-meshing. A modified wall friction velocity equation was derived to correct the wall friction velocity at the snow boundary owing to the serrated boundary of the solidified grids, thereby improving calculation accuracy. The numerical simulation results were verified via field observations of snow distribution on the stepped flat and gable roof models. The snow variation on a full-scale, three-span, suspended cable roof under snow-fall conditions was predicted using the proposed method.

Identifying atmospheric processes favouring the formation of bubble-free layers in the Law Dome ice core, East Antarctica

Abstract. Physical features preserved in ice cores may provide unique records about past atmospheric variability. Linking the formation and preservation of these features and the atmospheric processes causing them is key to their interpretation as palaeoclimate proxies. We imaged ice cores from Law Dome, East Antarctica, using an intermediate layer core scanner (ILCS) and found that thin bubble-free layers (BFLs) occur multiple times per year at this site. The origin of these features is unknown. We used a previously developed age–depth scale in conjunction with regional accumulation estimated from atmospheric reanalysis data (ERA5) to estimate the year and month that the BFLs occurred, and then we performed seasonal and annual analysis to reduce the overall dating errors. We then investigated measurements of snow surface height from a co-located automatic weather station to determine snow surface features co-occurring with BFLs, as well as their estimated occurrence date. We also used ERA5 to investigate potentially relevant local/regional atmospheric processes (temperature inversions, wind scour, accumulation hiatuses and extreme precipitation) associated with BFL occurrence. Finally, we used a synoptic typing dataset of the southern Indian and southwest Pacific oceans to investigate the relationship between large-scale atmospheric patterns and BFL occurrence. Our results show that BFLs occur (1) primarily in autumn and winter, (2) in conjunction with accumulation hiatuses > 4 d, and (3) during synoptic patterns characterised by meridional atmospheric flow related to the episodic blocking and channelling of maritime moisture to the ice core site. Thus, BFLs may act as a seasonal marker (autumn/winter) and may indicate episodic changes in accumulation (such as hiatuses) associated with large-scale circulation. This study provides a pathway to the development of a new proxy for past climate in the Law Dome ice cores, specifically past snowfall conditions relating to synoptic variability over the southern Indian Ocean.

Synoptic Climatology of Central U.S. Snowfall

Abstract Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledges the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948 to 2021 focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or midlatitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain’s synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15% and 25% of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1% and 93.5% by SWT. Synoptic climatological investigations provide valuable insights into the unique weather systems that generate hydroclimatic variability. Significance Statement By evaluating the weather patterns that are responsible for snowfall in the central United States, key insights can be gained into how and why snowfall varies and potentially changes over space and time. Using an approach that categorizes weather patterns based on their similarities, here 10 unique snowfall-producing weather patterns are identified and analyzed from 1948 to 2021. Each pattern resulted in different snowfall amounts across the central United States, varying substantially spatially and within the calendar year. Approximately 65% of the time that these weather patterns occur, snowfall is observed in the region. The majority of snowfall-producing weather patterns are associated with low pressure systems, but in some regions up to 70% of snowfall is associated with instances of high pressure in which winds can cause upward motions associated with topography.

Drivers of Snowfall Accumulation in the Central Idaho Mountains Using Long-Term High-Resolution WRF Simulations

Abstract The western United States region, an economic and agricultural powerhouse, is highly dependent on winter snowpack from the mountain west. Coupled with increasing water and renewable electricity demands, the predictability and viability of snowpack resources in a changing climate are becoming increasingly important. In Idaho, specifically, up to 75% of the state’s electricity production comes from hydropower, which is dependent on the timing and volume of spring snowmelt. While we know that 1 April snowpack is declining from SNOTEL observations and is expected to continue to decline as indicated by GCM predictions, our ability to understand the variability of snowfall accumulation and distribution at the regional level is less robust. In this paper, we analyze snowfall events using 0.9-km-resolution WRF simulations to understand the variability of snowfall accumulation and distribution in the mountains of Idaho between 1 October 2016 and 31 April 2017. Various characteristics of snowfall events throughout the season are evaluated, including the spatial coverage, event durations, and snowfall rates, along with the relationship between cloud microphysical variables—particularly liquid and ice water content—on snowfall amounts. Our findings suggest that efficient snowfall conditions—for example, higher levels of elevated supercooled liquid water—can exist throughout the winter season but are more impactful when surface temperatures are near or below freezing. Inefficient snowfall events are common, exceeding 50% of the total snowfall events for the year, with some of those occurring in peak winter. For such events, glaciogenic cloud seeding could make a significant impact on snowpack development and viability in the region. Significance Statement The purpose and significance of this study is to better understand the variability of snowfall event accumulation and distribution in the Payette Mountains region of Idaho as it relates to the local topography, the drivers of snowfall events, the cloud microphysical properties, and what constitutes an efficient or inefficient snowfall event (i.e., its ability to convert atmospheric liquid water into snowfall). As part of this process, we identify how many snowfall events in a season are inefficient to determine the number of snowfall events in a season that are candidates for enhancement by glaciogenic cloud seeding.

Modeling Traffic Volume Reduction as a Function of Winter Weather Factors for a Cold Region Highway

This research aims to quantify the impact of winter weather conditions (cold temperatures and snowfall) on traffic volume. Traffic data and winter weather data (temperature and snowfall intensities from November to March) were collected for 5 years from a cold region highway in Alberta, Canada. The study results suggest that, overall, traffic volume substantially reduces under extremely cold temperatures and heavy snowfall conditions. Yet, truck traffic was estimated to increase in the combined presence of some weather conditions, which could be due to more trucks being used and/or trucks potentially being rerouted from other routes. Highway agencies may use the proposed methodology to determine (1) the optimal snowplowing schedules, and (2) when to close (or reopen) highways in winter.

THE HEAVY SNOWFALL IN THE LVIV REGION

The relevance of determining the type of circulation and thermodynamic conditions of heavy snowfall in the Lviv region is due to the significant complication of meteorological conditions for aircraft flight due to heavy snowfall. Revealed the significant annual snowfall in the Lviv region from 2011 to 2021 at each station of the region with an increase in the number of cases towards the Carpathian Mountains. Established that heavy snowfall was observed at the stations of the Lviv region once every 3-5 years, with an increase in the number of cases after 2016, and the maximum of the annual flow occurred in February and December. Determined that heavy snowfalls were mainly formed under the influence of the occluded front during the movement or formation of the cyclone center over the northwest of Ukraine within the cold high-altitude baric basin at the level of 500 hPa during the north-west transport of air masses at altitudes from 750 to 3000 m.

Multi-objective optimization of operation strategy in snow melting system for airfield runway using genetic algorithm: A case study in Beijing Daxing International Airport

A hydronic snow melting pavement system is an efficient and sustainable snow removal approach. However, the conventional operation method remained unclear and usually led to a waste of energy. This study developed a thermal-economic model to assess this system's economic cost and snow melting effect. The multi-objective optimization (MOO) model and genetic algorithm (GA) were adopted to optimize the operation strategy, which includes fluid temperature (FT) and idling duration (ID). The accuracy of the MOO model was verified by a convergence study. The optimal results were organized in nomograms and implemented at Beijing Daxing International Airport. The results showed that the tournament selection method performs better in MOO solving. The optimal results indicated that it would obtain a better effect to balance the economic benefit and snow melting effect by adjusting FT than ID. And the MOO model can export the appropriate strategy in response to various snowfall conditions. Specifically, the optimal strategy implementation in the case study improved the snow-free duration ratio by 126% and saved the economic cost by 7% compared to the conventional strategies. The investigation provided an alternative operation strategy to minimize the energy cost for this system while ensuring the snow melting effect.

CFD Simulations of Snowdrifts on a Gable Roof: Impacts of Wind Velocity and Snowfall Intensity

Roof structures are suffering serious threats caused by unbalanced snow distribution, especially long-span spatial structures, such as gable roofs. The formation of unbalanced snowdrifts on the gable roof is affected by the meteorological condition and the drifting snow. This study was conducted to explore the snowdrift characteristics on gable roofs under different snowfall conditions based on a new Eulerian–Eulerian multiphase approach. To consider the diffusive process of snow in different states, the governing equations of air and snow phases were modified separately according to the actual transport process. Additional terms based on the deposition/erosion process were inserted into the governing equations to consider the processes of snow particles being trapped or ejected by snow surface. The feasibility of the new model for the snowdrift was validated by comparing with a field observation. Then, the snowdrifts characteristics on typical gable roofs were investigated under different wind velocity and snowfall intensity conditions. The formation mechanism of snowdrifts and the influence of meteorological conditions on snowdrifts were clarified by analysis. The results show that the uneven distribution of snow on the gable roof becomes more significant with the increase in wind velocity. Furthermore, the distribution of snow on the roof tends to be more even in the case of heavier snowfall.

Field measurement and outdoor wind tunnel test of snow-drifting: Snow distribution characteristics of railway subgrade and deposition mechanism

The snow deposits caused by snow-drifting are closely associated with railway cutting forms, which affect wind distribution and snow particle motion. Neither indoor wind tunnel tests using simulated material, nor tests which involve moving snow into a low-temperature wind tunnel, can reproduce the real properties and states of snow particles. This paper introduced a set of mobile low-speed direct current wind tunnel equipment, and outdoor wind tunnel tests were carried out to study the snow-drifting effect of railway cutting during snowfall.By adjusting the wind tunnel to simulate the natural flow field, and compared with the field monitoring results to verify the rationality of the wind tunnel design and the accuracy of results. According to the actual snowfall conditions, snow-drifting was tested under different forms of railway cutting and different inflow conditions, and the real-time kinematic photogrammetry was used to establish three-dimensional models for snow depth analysis. Finally, the mechanism of snow deposition was explained from the dynamic equilibrium shift of snow-drifting movement.

Modeling and assessment of operation economic benefits for hydronic snow melting pavement system

Hydronic snow melting pavement systems have been proposed as an energy conservation technology for pavement snow removal in response to global climate change. However, the quantification of these systems’ economic benefits remains unclear, and their operational decision-making does not consider the economic cost. This study proposes a novel method for calculating the hydronic snow melting pavement system’s economic cost and investigating the economic benefit evolution. A 2D thermo-economic model is developed. Measured data from the Beijing Daxing International Airport are utilized to validate the accuracy of the model, and the economic cost temporal variation under given snowfall conditions is demonstrated. Moreover, an extensive parametric analysis of economic cost is conducted, and sensitive parameters are identified. Then, a real operation case study under a comprehensive scenario is presented. Results show that economic cost can be divided into rapid reduction period (RRP) and stable period (SP). Fluid temperature can affect economic cost with a high fluid temperature resulting in a high economic cost in SP and a prolonged RRP. In the case study, the operation method with 0 h idling time can improve the snow melting performance and increase the economic cost by 7% compared with the method of opening after snowfall. The method with 3 h idling time can achieve optimal snow melting performance. However, its economic cost is 32 % higher than that of the method with 0 h idling time. Furthermore, staged heating method can reduce the economic cost by 7.9 % relative to constant heating method, thus providing an alternative cost-efficient solution.

Unchanged Fatality Rate on Austrian Ski Slopes during the COVID-19 Lockdown

Fatalities on ski slopes are very rare, with about one death per one million skier days. Whether the fatality rate is affected by substantial changes in the number of skier days and potentially associated alterations in the structure of the skier population is unknown. Thus, we compared the fatality rate on Austrian ski slopes in the winter season of 2020/21, when skiing activities were dramatically restricted during the COVID-19 lockdown, with those of the previous winter seasons. As a consequence of COVID-19 measures, the number of skier days dropped from over 50 million in previous years to 9.2 million skier days in the winter season of 2020/21. Still, the fatality rate (6.5 deaths/10 million skier days) was not different when compared to any of the seasons from 2011/12 to 2019/20. Despite the lack of international skiers and the reduction in skier days by more than 80%, the fatality rate remained surprisingly unchanged. The weather and snowfall conditions were on average comparable to those of previous winters, and, except for nationality, the composition of the skier population appears to have remained relatively unaltered. In conclusion, the fatality rate during downhill skiing is low and the absolute fatality numbers are primarily a function of the number of skier days.

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.