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Abstract In this work, snowdrift experiments which are equivalent to one drifting snow event are performed by the snowdrift model. The model consisted of the computational fluid dynamics part of the large-eddy simulation with the lattice Boltzmann method and the drifting snow part of the conventional advection algorithm for representative Lagrangian particles. The observed vertical wind profile of a 4 h drifting snow event in Teshikaga Town was used as the inflow boundary conditions in the model to compare the results of the snowdrift estimated by the model and the observed snowdrift distribution. Parallelization enabled us to simulate the snowdrift distribution in a realistic domain and on the time scale of a single drifting snow event. We demonstrated that the upgraded model could quantitatively reproduce the height and position of the observed snowdrift along the center of a three-dimensional fence.

In this work, snowdrift experiments which are equivalent to one drifting snow event are performed by the snowdrift model. The model consisted of the computational fluid dynamics part of the large-eddy simulation with the lattice Boltzmann method and the drifting snow part of the conventional advection algorithm for representative Lagrangian particles. The observed vertical wind profile of a 4 h drifting snow event in Teshikaga Town was used as the inflow boundary conditions in the model to compare the results of the snowdrift estimated by the model and the observed snowdrift distribution. Parallelization enabled us to simulate the snowdrift distribution in a realistic domain and on the time scale of a single drifting snow event. We demonstrated that the upgraded model could quantitatively reproduce the height and position of the observed snowdrift along the center of a three-dimensional fence. [ABSTRACT FROM AUTHOR]

The results of numerical modeling of the influence of geometric characteristics of snow-protecting fences on the intensity of snow deposition at the initial stage of formation, that is, without taking into account the influence of the dynamics of the shape of the snow cover surface, are presented. In the most industrialized and densely populated region on the Arctic Krasnoyarsk Territory - the industrial City of Norilsk, daily snowfall can exceed 50 mm, the snow depth reaches, on the average, 47 cm (the largest is 70 cm), while the wind speed - 25-30 m/s. This promotes formation of snow deposition on roads, in residential areas as well as in industrial sites and infrastructure facilities, which hampers and sometimes completely stops operation of them. As part of the solution of these problems, a software package has been developed aimed at numerical modeling of snow transport processes and implementing the snow protection measures. To simulate the dynamics of the windinduced snow drift, a microscale model of the atmospheric boundary layer was used together with a diffusion-inertial description of the transport of the snow dispersed phase. Analysis of the calculation results shows that the width of the plates, as well as their spatial orientation, have insignificant effect on the snow-holding capacity of fences. The size of the gaps between the rails and the height of the lower gap exerts the greatest influence on the distribution of the intensity of snow deposition, both on the leeward and windward sides of the fence. In general, we can talk about the relationship between the wind speed field formed during the drift around the fence and the distribution of the snow deposition intensity. Thus, a relative decrease in the average wind speed from the leeward side of the fence increases the precipitation intensity. The presented results of numerical modeling do not contradict data of field observations previously obtained by other authors, and, thus, the developed software package allows comparing effectiveness of different snow-protecting constructions.

The surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very simplified way in current meteorological and climatological models. To address this problem, the CRYOWRF model has been developed, a coupled model between the meteorological Weather Research and Forecasting model (WRF) and the snow model SNOWPACK, augmented by a detailed treatment of drifting and blowing snow. Applying CRYOWRF to the SMB of Antarctica, we find that the model reproduces measurements of SMB with similar errors than current models. Drifting and blowing snow and its sublimation play a particularly important role, especially in regions of strong katabatic winds. The CRYOWRF simulations are also in line with satellite estimates of blowing snow frequency. There is a need to further consolidate results by simulations with a higher grid resolution and by including more measurements of SMB contributions from snow fall to transport and sublimation. Plain Language Summary: Assessing current and predicting future sea level rise in connection with the general fate of our snow and ice masses on Earth requires understanding snow precipitation in extreme environments and the dynamics of snow on the surface. Over large parts of Antarctica, drifting and blowing snow and sublimation, which is the phase change of ice back to atmospheric vapor, are the only surface ablation processes and need therefore to be well quantified. With a new model, that shows similar performance to other models, we find that drifting and blowing snow and its sublimation play an important role for the snow mass balance especially in regions with strong winds. This has consequences not only for the snow mass balance alone but for the whole ice sheet dynamics as well as for estimating precipitation in these extreme environments. Key Points: Evaluation of the coupled Weather Research and Forecasting (WRF) and SNOWPACK model CRYOWRF, including a blowing snow module, shows reasonable representation of blowing snowBetter representation of atmospheric conditions at a station level by CRYOWRF compared to WRFBlowing snow is an important component of the Antarctic surface mass balance, especially in the katabatic regions of the continent [ABSTRACT FROM AUTHOR]

Afterimages of snow particles in blowing snow may cause lower visibility of the human eye compared to images captured by a digital video camera. However, the impact of the afterimage of snow particles on the visibility of the human eye in blowing snow has not been investigated quantitatively. Here we examined visibility taking the effect of afterimages of snow particles on the human eye into consideration. By considering the duration range of afterimages, and changing the windspeed while the snow density remains constant, our numerical model of visibility for the visual duration range of 0.12 to 0.04 s could accurately explain the visibility of the human eye previously observed in blowing snow. [ABSTRACT FROM AUTHOR]

We 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. Plain Language Summary: Suspended sea salt particles affect the climate and chemistry of the atmosphere. The main source of these particles is ocean wave breaking. In polar regions an additional source is the wind lofting of salty snow that is present on top of sea ice. Here we use model simulations to examine how changing Arctic conditions have impacted sea salt particles in the atmosphere. Our simulations show that the concentrations of sea salt particles have increased by more than 6% per decade over the 1980–2017 period. During cold months, this increase is due to saltier blowing snow particles as older sea ice with less salty snow is being replaced by younger sea ice with more salty snow. During warmer months, this increase is the result of larger open ocean emissions as sea ice is melting. These changes in sea salt particles could potentially affect Arctic bromine activation and climate. Key Points: Model predicts pan‐Arctic increase in sea salt aerosol (SSA) concentrations by 6%–12% decade−1 for 1980–2017Younger Arctic sea ice with thinner and saltier snow cause increasing blowing snow SSA emissions in winter‐springDuring summer‐fall, rapidly diminishing sea ice extent results in increasing open ocean SSA emissions [ABSTRACT FROM AUTHOR]

In 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. Plain Language Summary: This study used multiple lidar and radar instruments to identify the height of blowing snow (BLSN) layers at McMurdo Station in Antarctica. This information can be used to evaluate and improve BLSN models which have had minimal verification above ground. Results of this study compare well to prior studies, however, the suite of instruments demonstrate that agreement is partially due to compensating errors within single instrument (ceilometer) studies. Key Points: Inclusion of additional lidars and radar improve estimates of blowing snow layer heightsCeilometer only algorithms are prone to offsetting height errors due to extinction and fallstreaksFuture in situ observations are paramount to improving and validating this experimental algorithm [ABSTRACT FROM AUTHOR]

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 3 years 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. Plain Language Summary: The sky‐condition over Antarctica can be cloudy, clear, or characterized by blowing snow (BLSN) which is a phenomenon where snow is lifted, sometimes up to few hundreds of meters, and transported across the continent by winds. Knowing the impact of clouds and BLSN on the surface and the atmospheric boundary layer, which is the lower portion of the atmosphere that is directly linked to the surface, is crucial for understanding Antarctic climate. Dome C, an elevated dome‐shaped peak in East Antarctica, is chosen to study these impacts. In this location, an investigation covering a 3 year period using satellite observations and in situ measurements reveals that clouds can warm the surface and atmospheric boundary layer by increasing the downwelling longwave radiation. BLSN is accompanied by relatively stronger winds, and the wind‐driven atmospheric turbulence tends to weaken the strong positive temperature gradient in the atmospheric boundary layer, which is known as the temperature inversion. Temperature inversions are common at Dome C, and are most intense in the fall and winter seasons during which time they seemingly restrict the atmospheric turbulence and BLSN layer depth to a few tens to several tens of meters. Key Points: Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations and co‐located in situ observations allow study of cloud and blowing snow (BLSN) impacts on Antarctic atmosphereAt Dome C, clouds abate surface longwave radiative cooling losses, causing warming in all seasonsBLSN, accompanied by intense winds, weakens the temperature inversion, and is vertically restricted by strong background stability [ABSTRACT FROM AUTHOR]

Abstract We developed a snowdrift model to evaluate the snowdrift height around snow fences, which are often installed along roads in snowy, windy locations. The model consisted of the conventional computational fluid dynamics solver that used the lattice Boltzmann method and a module for calculating the snow particles’ motion and accumulation. The calculation domain was a half channel with a flat free-slip boundary on the top and a non-slip boundary on the bottom, and an inflow with artificially generated turbulence from one side to the outlet side was imposed. In addition to the reference experiment with no fence, experiments were set up with a two-dimensional and a three-dimensional fence normal to the dominant wind direction in the channel center. The estimated wind flow over the two-dimensional fence was characterized by a swirling eddy in the cross section, whereas the wind flow in the three-dimensional fence experiment was horizontally diffluent with a dipole vortex pair on the leeward side of the fence. Almost all the snowdrift formed on the windward side of the two-dimensional and three-dimensional fences, although the snowdrift also formed along the split streaks on the leeward side of the three-dimensional fence. Our results suggested that the fence should be as long as possible to avoid snowdrifts on roads.

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. [ABSTRACT FROM AUTHOR]

Snow, a critical element influencing surface energy/mass balance of the Arctic, can also drift in the air to complicate the surface–atmosphere interaction. This complexity can be further enhanced when the surface includes polynya. These processes, however, have not been well studied and are often unrepresented in climate and weather models. We address this by applying a snow/ice-enhanced version of the Weather Research and Forecasting model to examine the impacts of blowing snow and polynya on the surface–atmosphere interaction during an extreme Arctic wind event in February 2018, when an unprecedented polynya occurred off the north coast of Greenland. The results indicate that blowing snow and the polynya contribute opposite signs to the changes of surface sensible/latent heat fluxes, but both cause enhanced downwelling longwave radiation. Process analysis shows that the thermodynamic moistening/cooling effects due to the blowing snow sublimation are amplified by increased surface winds, reduced temperature inversion, and upward wind anomaly associated with the polynya. Enhanced surface–atmosphere interaction over a polynya due to blowing snow sublimation can potentially sustain the continuing development of the polynya.

The Antarctic Ice Sheet experiences perpetual katabatic winds, transporting snow, and moisture from the interior towards the periphery. However, the impacts of Antarctic moisture and drifting snow on cloud structure and surface energy fluxes have not been widely investigated. Here, we use a regional climate model with a newly developed drifting snow scheme to show that accounting for drifting snow notably alters the spatial distribution, vertical structure and radiative effect of clouds over Antarctica. Overall, we find that accounting for drifting snow leads to a greater cloud cover providing an increase of +2.74 Wm−2 in the surface radiative energy budget. Additionally, a comparison with 20 weather stations reveals a 2.17 Wm−2 improvement in representing the radiative energy fluxes. Our results highlight the need to study the impact of drifting snow processes on the future evolution of clouds, the surface energy budget and the vertical atmospheric structure over Antarctica. Plain Language Summary: Antarctica is the continent with the strongest winds on Earth. These winds pick up a lot of snow on their way from the interior towards the ocean, forming drifting snow clouds. Drifting snow clouds can extend over 1,000 km horizontally and multiple 100 m vertically. Like a normal cloud, they can reflect incoming sunlight like a mirror and trap heat like a blanket. However, most of our climate models don't yet incorporate these drifting snow clouds and therefore might be missing an important part of the Antarctic climate system. In this study, we show that when we account for drifting snow clouds the Antarctic surface receives notably more thermal radiation. Additionally, we also show that we significantly improve our model when we include drifting snow by comparing our outputs to weather station observations over Antarctica. Therefore, we conclude that accurate Antarctic climate projections need to account for drifting snow. Key Points: Accounting for drifting snow over Antarctica leads to a radiative forcing of +2.7 Wm−2 over the grounded ice sheetAccounting for drifting snow increases the cloud cover over Antarctica by 18.6%Drifting snow is an important‐yet in climate models and observations often neglected‐component of the Antarctic surface energy budget [ABSTRACT FROM AUTHOR]

We developed a snowdrift model to evaluate the snowdrift height around snow fences, which are often installed along roads in snowy, windy locations. The model consisted of the conventional computational fluid dynamics solver that used the lattice Boltzmann method and a module for calculating the snow particles' motion and accumulation. The calculation domain was a half channel with a flat free-slip boundary on the top and a non-slip boundary on the bottom, and an inflow with artificially generated turbulence from one side to the outlet side was imposed. In addition to the reference experiment with no fence, experiments were set up with a two-dimensional and a three-dimensional fence normal to the dominant wind direction in the channel center. The estimated wind flow over the two-dimensional fence was characterized by a swirling eddy in the cross section, whereas the wind flow in the three-dimensional fence experiment was horizontally diffluent with a dipole vortex pair on the leeward side of the fence. Almost all the snowdrift formed on the windward side of the two-dimensional and three-dimensional fences, although the snowdrift also formed along the split streaks on the leeward side of the three-dimensional fence. Our results suggested that the fence should be as long as possible to avoid snowdrifts on roads. [ABSTRACT FROM AUTHOR]

Tenuous atmospheric layers, such as high, thin cirrus clouds and blowing snow over Antarctica, play an important role in the climate system, affecting energy fluxes between the Earth and the atmosphere. Our knowledge of the structure of the atmosphere is largely derived from atmospheric satellite measurements. Yet tenuous layers can be hard to detect in satellite lidar data, especially in daylight data characterized by high solar background. In this study, we introduce an approach to detect tenuous atmospheric layers in the atmospheric lidar measurements of NASA's ICESat‐2. The density‐dimension algorithm for ICESat‐2 atmospheric data (DDA‐atmos) identifies atmospheric layers while automatically adapting to different background conditions of night, twilight, and daylight data. This capability, demonstrated for tenuous clouds and blowing snow, offers a data‐based solution to an important climate modeling problem. Atmospheric layer boundaries, detection confidence, and density fields resultant from the DDA‐atmos are reported in ICESat‐2 atmospheric data product ATL09. Plain Language Summary: Tenuous atmospheric layers, such as high, thin cirrus clouds and blowing snow over Antarctica, play an important role in the climate system, because they affect energy fluxes between the Earth and the atmosphere. Our knowledge of the structure of the atmosphere is largely derived from atmospheric satellite measurements. Yet tenuous layers can be hard to detect in satellite lidar data, especially in daylight data characterized by high solar background, which results in high noise. In this study, we introduce an approach to detect tenuous atmospheric layers in the atmospheric lidar measurements of NASA's Ice, Cloud and land Elevation Satellite ICESat‐2. To this end, we develop a mathematical algorithm, the density‐dimension algorithm for ICESat‐2 atmospheric data, that identifies atmospheric layers while automatically adapting to different background conditions of night, twilight, and daylight data. The resultant capability to detect thin clouds and blowing snow in ICESat‐2 data offers a data‐based solution to an important climate modeling problem. The results are publicly available on the ICESat‐2 atmospheric data product ATL09. Key Points: The atmospheric channel of ICESat‐2 measures tenuous clouds and blowing snowThe density‐dimension algorithm for atmospheric data detects tenuous layers while automatically adapting to night and daylight conditionsThis capability offers a data‐based solution to including tenuous clouds in atmospheric climate models [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Japanese Society of Snow & Ice / Seppyo is the property of Japanese Society of Snow & Ice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the Japanese Society of Snow & Ice / Seppyo is the property of Japanese Society of Snow & Ice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the Japanese Society of Snow & Ice / Seppyo is the property of Japanese Society of Snow & Ice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the Japanese Society of Snow & Ice / Seppyo is the property of Japanese Society of Snow & Ice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the Japanese Society of Snow & Ice / Seppyo is the property of Japanese Society of Snow & Ice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blowing snow (BLSN) is an impactful process in cold climates, affecting regional thermodynamics, radiation properties, and the surface mass balance of snow. Though it has significant climatic impacts, the process is still poorly understood and not widely included in weather and climate models. In 2016, the AWARE Field Campaign saw the deployment of a large suite of in situ and remote sensing instruments to McMurdo Station, Antarctica allowing for investigation of BLSN. A ceilometer‐based BLSN detection algorithm used elsewhere in Antarctica is applied to data from AWARE, yielding a BLSN frequency of 14.1% compared to 8.2% as detected by human observers. To increase confidence in detections, the algorithm is updated to have shorter temporal averaging and to include a variety of meteorological thresholds to limit false detections due to fog. Efforts to incorporate a laser disdrometer into the algorithm were unsuccessful. An unphysical dependence of particle size distributions on wind speed is found suggesting observations are problematic at wind speeds greater than 10 m s−1. The revised algorithm detected a BLSN frequency of 7.4%, increasing agreement with human observations and confidence that the process is actively occurring at the observation site. These observations are put into context of a climatology of human observations of BLSN at McMurdo station from 2002–2018. An annual average of 8.0%–14.0% is estimated, with a total annual range of 3.4%–21.3%. Regardless of whether BLSN is observed by humans or instrument, the majority of cases at this location are associated with ongoing precipitation. Plain Language Summary: This study investigated the occurrence of blowing snow (BLSN) at McMurdo Station in Antarctica. Understanding this process is important for reasons ranging from immediate travel impacts (e.g., whiteout conditions) to long‐term changes in ice sheet surface mass balance. We used a number of meteorological instruments and human observations to estimate how often BLSN occurs. We provide a range in BLSN frequencies because we don't know what ground truth is. The instrument we hoped would provide this information struggles during periods of strong winds. Because BLSN is associated with strong winds, this is problematic. Key Points: The frequency of blowing snow at McMurdo Station, Antarctica is analyzed based on human observations and ground‐based instrumentationAlterations to the algorithm developed by Gossart et al. (2017) are presented to increase confidence in detection of blowing snowParsivel2 laser disdrometer observations have an unrealistic dependence on wind speed during blowing snow events [ABSTRACT FROM AUTHOR]

Abstract As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.

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.

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes. [ABSTRACT FROM AUTHOR]

Continuous measurements of blowing snow are scarce, both in time and space. Satellites now provide the opportunity to derive blowing snow occurrences, transport and sublimation rates over Antarctica. These products are extremely valuable and offer a continental-wide assessment of blowing snow, which is an important but unknown component of the surface mass balance of the Antarctic ice sheet. However, little ground truth is available to validate these retrievals. The recent application of ceilometers for detection of blowing snow frequencies provides an opportunity to validate the satellite retrievals of blowing snow. A routine to detect blowing snow occurrence from ground-based remote sensing ceilometers has been developed at two coastal locations in East Antarctica for the 2011–2016 time period. Thanks to their ground-based location, ceilometers are able to detect blowing snow events in the presence of clouds and precipitation, which can be missed by the satellite, since optically thick clouds impede the penetration of the signal. In coastal areas, more than 90% of blowing snow occurs under cloudy conditions and represent 30 to 56% of all cloudy conditions at Princess Elisabeth and Neumayer III (Neumayer hereafter) stations, respectively. For cloud-free conditions, 8% of the measurements at Princess Elisabeth (and none at Neumayer) are identified as blowing snow by the satellite but not by the ceilometer, likely due to differences in sensors, limitation of the surface identification by the satellite, or the spatial inhomogeneity of the blowing snow event. While the satellite blowing snow retrieval is a useful product, further investigation is needed to reduce the uncertainties on blowing snow frequencies associated with clouds.

The number of days with blizzard, the intensity, duration, periods of occurrence, and dynamics of blizzards in the mountain regions of Georgia are investigated using observational data from 20 weather stations. The features of wind and temperature regimes of blizzards are revealed. [ABSTRACT FROM AUTHOR]

• We used measurements to explore energy exchanges in an agro-forested snowpack. • Differences in energy fluxes between open, forest, and gap areas were significant. • Longwave radiation had a greater influence on energy balance during accumulation. • Latent heat fluxes and solar radiation absorption dominated snowpack melting. • Blowing snow influenced the energy budget through negative feedback effects. A lack of field studies measuring snow mass and energy balance in open and forest patches hinders the holistic understanding of snowpack dynamics and makes it difficult to validate modeling efforts in agro-forested environments. In such context, this study explores the energy exchanges within snowpacks in an agro-forested environment in eastern Canada, with a focus on measuring energy fluxes and assessing temporal variability and meteorological controls on the snowpack. The results showed that there are considerable differences in energy fluxes between open, forest, and gap areas, with net radiation dominating the snow surface energy balance. During the accumulation period, longwave radiation had a greater influence on the variability of the energy balance, while during the ablation period, latent heat fluxes and solar radiation absorption dominated the variability of the energy balance and snowpack melting. Blowing snow also influenced the energy budget in the open area through negative feedback effects. Despite the negative feedback effects, results showed that the decreased air stability in response to the reduced temperature gradient between the atmosphere and snowpack counterbalanced the reduced vapor pressure gradient and resulted in slightly increased latent heat (sublimation) losses from the snowpack during blowing snow. Furthermore, our analysis showed reduced influxes of sensible heat and longwave radiation to the snowpack in response to the blowing snow cooling feedback on the atmosphere. These results emphasize the significant role of blowing snow for the energy exchanges in large wind-exposed open areas in humid continental agro-forested landscapes. Furthermore, the different snowpack and energy balance conditions between the open and forested patches of agro-forested landscapes highlighted in this study could have important implications for snowmelt infiltration patterns and resulting catchment-scale hydrology. [ABSTRACT FROM AUTHOR]

Snow cover in mountainous terrain plays an important role in regional and global water and energy balances, climate change, and ecosystems. Blowing snow is a frequent and important weather phenomenon over the Tibetan Plateau (TP); however, this process is neglected in most current land surface models, despite the consequential role it plays in the land surface and atmospheric water and energy budgets. In this paper, we present a blowing snow model PIEKTUK coupled with the Community Land Model (CLM4.5) to provide a better estimate of the snow dynamics for the consideration of snow redistribution induced by wind. Two simulations with a 0.065° spatial resolution were performed in 2010 over the TP, namely, a sensitivity experiment with the inclusion of blowing snow effects (CLM_BS) and a control run with the original model (CLM). A specific objective of this study was to evaluate the improvements in the simulations of snow dynamics and other key variables in surface energy partitioning provided by the coupled model, such as the surface albedo and land surface temperature (LST). Compared with a variety of remote‐sensing observations, the results show that the surface snow cover, snow depth, and surface albedo can be better reproduced in most of the TP region by CLM_BS than by the original CLM, particularly in the Kunlun Mountains, Hoh Xil area, and the southwestern TP. In areas with reduced bias, variations in the monthly mean snow cover fraction can be reflected particularly well by CLM_BS. For LST, however, a significant decrease in the nighttime LST bias was detected in CLM_BS, while the bias in the daytime LST increases. The results show considerable potential for the inclusion of the blowing snow process to promote the modeling of snow dynamics and land‐atmosphere interactions on the TP. Key Points: The PIEKTUK blowing snow model was coupled to the Community Land Model to better estimate the snow dynamics over the Tibetan PlateauSnow cover, snow depth, and surface albedo can be better represented by the coupled model with the inclusion of blowing snow effectsA remarkable decrease of bias in the nighttime land surface temperature is detected in the coupled model simulation [ABSTRACT FROM AUTHOR]

Abstract The process of blowing snow is important for secondary distribution of water resources in seasonal grassland snow areas because it affects the regeneration, growth, and nutrient circulation of vegetation. This study conducted field observations of blowing snow and snow accumulation in a grassland area typical of northern China. The results showed that in the near-surface area (0 to 100 cm), snow drifting intensity increased exponentially with a mean wind speed of 2 m/s, and that the maximum snow drifting intensity, which was found within 30 cm of the surface, accounted for 89.61% of the total amount of snow drifting within the near-surface area. The snow drifting intensity was strong under conditions of an abundant snow source and sparse vegetation, with an average value of 2.203 g/cm/min. The snow drifting intensity was lower under conditions of an insufficient snow source and higher vegetation coverage, with an average value of 0.545 g/cm/min. In typical grassland areas with a bare surface and sparse vegetation, snow cover is generally thin; the mean snow depth in such areas during the observation period of this study was 4.03 cm, and this constituted the source of the drifting snow. Snow cover was thicker in areas with taller grass and greater vegetation coverage; the mean value of snow depth in such areas during the observation period was 16.56 cm, and this constituted the main snow cover area. The observation results provide basic data to support the estimation of snow cover amount in grassland ecosystems, optimization of the utilization of snow water resources, regularization of snow cover, restoration of vegetation, and prevention and control of snowstorms. Highlights • The process of blowing snow and snow cover were observed. • The relationship between vegetation and snow cover were surveyed. • Snow areas with a bare surface and sparse vegetation are the primary source areas of blowing snow. • Greater vegetation coverage and height are areas of snow accumulation. [ABSTRACT FROM AUTHOR]

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Direct measurement of precipitation in the Antarctic using ground-based instruments is important to validate the results from climate models, reanalyses and satellite observations. Quantifying precipitation in Antarctica faces many unique challenges such as wind and other technical difficulties due to the harsh environment. This study compares a variety of precipitation measurements in Antarctica, including satellite data and reanalysis fields atRothera Station, Antarctica Peninsula. The tipping bucket gauges (TBGs) were less sensitive than laser-based sensors (LBSs). The most sensitive LBS (Visibility and Present Weather Sensor, VPF-730) registered 276 precipitation days, while the most sensitive TBG (Universal Precipitation Gauge, UPG-1000) detected 152 precipitation days. Case studies of the precipitation and seasonal accumulation results show the VPF-730 to be the most reliable precipitation sensor of the evaluated instruments. The precipitation amounts given by the reanalyses were positively correlated with wind speed. The precipitation from the Japanese 55-year Reanalysis was most affected by wind speed. Case studies also show that during low wind periods, precipitation measurements from the instruments were very close to the precipitation measurement given by the Global Precipitation Climatology Project (GPCP) 1-degree-daily (1DD) data. During strong wind events, the GPCP 1DD did not fully capture the effect of wind, accounting for the relatively small precipitation amount. The Laser Precipitation Monitor (LPM) and Campbell Scientific-700 (CS700H) experienced instrumental errors during the study, which caused the precipitation readings to become exceedingly high and low, respectively. Installing multiple LBSs in different locations (in close proximity) can help identify inconsistency in the readings.

Horizontal and altitudinal redistribution of snow by wind transport and avalanches can be important controls on small- and large-scale snow accumulation patterns that control meltwater supply in alpine environments. Redistribution processes control the spatial variability of snow accumulation, which not only controls meltwater supply, but also regulates snowmelt timing, duration, and rates, as well as snow-covered area depletion and the variable contributing area for meltwater runoff generation. However, most hydrological models and land surface schemes do not consider snow redistribution processes, and those that do are difficult to verify without spatially distributed snow depth measurements. These are rarely available in both high resolution and covering large scales. As an increased number of hydrological models include snow redistribution processes there is a need for additional snowcover metrics to verify snow redistribution schemes over large areas using readily available data. This study develops novel high-resolution (20 m), snowcover indices from remotely sensed imagery (Landsat-8 and Sentinel-2) to evaluate snow redistribution models over alpine areas without in-situ or airborne snow observations. A snowcover absence (SA) index, calculated from snow-free areas during the winter, identifies areas of wind erosion or avalanche source areas. A snowcover persistence (SP) index, calculated from snow-covered areas during the summer, identifies snow deposition in drifts and avalanche deposits. The snowcover indices captured the relative differences in surface observations of snow presence and absence between exposed and sheltered sites on an intensely instrumented ridge in the Canadian Rockies Hydrological Observatory. Within the Tuolumne River Basin in central California (1100 km 2 ), the SP index captured roughly half of the spatial variability (R 2  = 0.49 to 0.56) in peak SWE as estimated from airborne LiDAR-derived snow depths. At the individual mountain ridge scale (~800 m), variability in both ablation and snow redistribution controlled the SP patterns over 7979 ridges. Differences in shortwave irradiance explained 76% of the SP variance across ridges, but could not explain smaller-scale (~100 m) SP peaks that are associated with snowdrifts and avalanche deposits. The snowcover indices can be used to evaluate snow redistribution models of the finer scale impacts of snow redistribution by wind and gravity as long as the larger scale influences of spatially variable solar irradiance effects are also simulated. [ABSTRACT FROM AUTHOR]

The object under study is the blowing snow, i.e., the transport of snow lifted from the snow surface. The method is described for predicting the blowing snow initiation using the output data of the WRF-ARW numerical atmospheric model. The skill scores are presented for the forecasts for January 2013 calculated from data of 10 stations of the Canadian weather observation network. [ABSTRACT FROM AUTHOR]

While turbulent bursts are considered critical for blowing-snow transport and initiation, the interaction of the airflow with the snow surface is not fully understood. To better characterize the coupling of turbulent structures and blowing-snow transport, observations collected in natural environments at the necessary high-resolution time scales are needed. To address this, high-frequency measurements of turbulence, blowing-snow density and particle velocity were made in the Canadian Rockies. During blowing-snow storms, modified variable-interval time averaging enabled identification of periods of near-surface blowing-snow coupling with shear-stress-producing motions in the lowest 2 m of the atmospheric surface layer. The identification of those turbulent motions responsible for blowing snow yields a better understanding of the event-driven mechanics of initiation and sustained transport. The type of coherent structures generating the Reynolds stress are just as important as the magnitude of the Reynolds stress in initiating and sustaining near-surface blowing snow. Our results suggest that blowing-snow models driven by merely the time-averaged shear stress lack physical realism in the near-surface region. The next phase of the development of blowing-snow models should incorporate parametrizations of coherent turbulent structures. [ABSTRACT FROM AUTHOR]

In alpine terrain, blowing snow events strongly affect the local evolution of the avalanche danger and must be taken into account by avalanche hazard forecasters. This study presents the implementation and the evaluation of the blowing snow scheme Sytron into the operational chain for avalanche hazard forecasting (named S2M) used in the main French mountain ranges. S2M-Sytron provides information on blowing snow occurrence and intensity per 300-m elevation bands and aspects for several regions of the French mountains. The wind forcing is provided by the meteorological analysis system SAFRAN. S2M-Sytron was evaluated for winter 2015/16 at 11 automatic stations measuring wind speed and blowing snow fluxes in the French Alps. The system detects 55% of blowing snow days with less than 10% of false alarms. S2M-Sytron captures the occurrence of blowing snow events with and without concurrent snowfall. Improvements are obtained when considering an updated parameterization for the properties of falling snow which reduces the threshold velocity for freshly fallen snow. Using observed wind speed instead of SAFRAN wind speed to drive Sytron shows further improvements at stations where SAFRAN wind speed differs from the observations due to local topographic features. Overall, S2M-Sytron provides a regional blowing snow assessment but cannot fully reproduce the local intensity of blowing snow events. [ABSTRACT FROM AUTHOR]

Snow accumulation in alpine terrain is controlled by three main processes that act at different spatial scales: (i) orographic snowfall, (ii) preferential deposition of snowfall, and (iii) wind-induced snow transport of deposited snow. The relative importance of these processes largely remains uncertain at small scale (10-100 m). This study presents how high-resolution coupled snowpack/atmosphere simulations help quantifying the effects of these processes. The simulation system consists of the detailed snowpack model Crocus and the atmospheric model Meso-NH used in Large Eddy Simulation mode. Dedicated routines allow the coupled system to explicitly simulate wind-induced snow transport. Our case study is a snowfall event that occurred in February 2011 in the French Alps. Three nested domains at 450, 150 and 50 m grid spacing allow the model to simulate the complex 3D precipitation and wind fields down to fine scale. We firstly assess the ability of the coupled model to reproduce meteorological conditions during the event (wind speed and direction, snowfall amount, and blowing snow fluxes). The spatial variability of snowfall and snow accumulation is then considered. At 50 m grid spacing, snowfall presents local maxima associated with the formation of rimed snow aggregates and graupel in regions of sustained updrafts. Variograms show that the resultant spatial variability of snowfall is lower than the variability of snow accumulation when considering snow transport. Despite an overestimation of simulated blowing fluxes, our results suggest that wind-induced snow transport is the main source of spatial variability of snow accumulation in our case study. [ABSTRACT FROM AUTHOR]

Snow distribution has a profound impact on natural processes such as the hydrological cycle, the climate system, and ecological evolution. Many studies suggest that elevation, temperature, and precipitation are the three major factors controlling snow distribution. Our study explores the influence of wind on the snow distribution and finds that wind is another important factor controlling the snow distribution in the northeastern Tibet Plateau. We select the Qilian Mountains in the northeastern Tibetan Plateau as the study area, and the data include the moderate-resolution imaging spectroradiometer snow area product and the atmosphere dataset generated by the Weather Research and Forecasting model. The results indicate that there is a threshold elevation for the correlation between the fractional snow cover (FSC) area and the wind speed in the study area. At elevations above 3900 m, the FSC and wind speed exhibit a significant negative correlation, and at elevations below 3900 m, they exhibit a significant positive correlation. Our analyses indicate that the probability for the occurrence of snowdrifts is higher in regions above 3900 m and that the wind transports snow from regions above 3900 m to lower elevations. [ABSTRACT FROM PUBLISHER]

An equation was proposed to model the height of blowing snow accumulation downwind of an obstacle such as vegetation, a snow fence, a building, or a topographic feature. The equation does not require aerodynamic flow condition parameters such as wind speed, allowing for the spatial distribution of snow to be determined at locations where meteorological data is not available. However, snow particle diffusion, drift, and erosion coefficients must be estimated for application of the equation. These coefficients can be used to provide insight into the relative magnitude of blowing snow processes at a field location. Further research is required to determine efficient methods for coefficient estimation. The equation could be used with other models of wind-transported snow to predict snow accumulation downwind of an obstacle without the need for wind speed adjustments or correction equations. Applications for this equation include the design of snow fences, and the use of this equation with other hydrological models to predict snow distribution, climate change, drought, flooding, and avalanches. [ABSTRACT FROM AUTHOR]