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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.

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.

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.

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.

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.

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.

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.

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.