• Wind and Structures
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• v.39 no.3
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• pp.207-222
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• 2024

This paper presents a numerical simulation method for snow drift that takes into account the cohesion effect of snow particles. The critical state of free collapse accumulation of idealized snow particles is used to indirectly infer the effect of interparticle interactions on snow transport and re-accumulation. With the help of the Hertz-Mindlin with JKR cohesion contact model, the particle angle of repose is calibrated with a number of contact parameters through numerical experiment. The surface energy for a given property of snow particles is determined using the observed snow angle of repose, and a continuous-discrete snow drift two-way coupled numerical model incorporating these optimized contact parameters is developed. The snow redistribution pattern on a stepped flat roof structure is simulated, and the results are found to be consistent with those of the field measured in terms of phenomena and general laws, verifying the achievability and effectiveness of the presented method. To eliminate the influence of environmental conditions, wind tunnel tests are also conducted, and it is found that the reconstructed depth and reaccumulated angle of snowdrift resulting from the numerical simulation are in closer agreement with the experimental results, further confirming the enhancement achieved by introducing the contact effect.

• Wind and Structures
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• v.3 no.2
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• pp.73-81
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• 2000

Snowdrifts around buildings can cause serious problems when formed on undesirable places. The formation of snowdrifts is highly connected to the wind pattern around the building, and the wind pattern is again dependent on the building design. The shear stress on the surface and snowdrifting around different buildings are investigated through CFD analysis and compared to measurements. The computations of shear stress shows local minima in the same areas as snowdrifts are formed. The snowdrifting computations utilises a drift-flux model where a fluid with snow properties is allowed to drift through a fluid with air properties. An apparent dynamic viscosity of the snow/air mixture is defined and used as a threshold criterion for snowdrifting. The results from the snowdrifting computations show increased snow density where snowdrifts are expected, and are in agreement with previous large-scale snowdrift measurements. The results show that computational fluid dynamics can be a tool for planning building design in snowdrifting areas.

• Wind and Structures
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• v.37 no.1
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• pp.25-38
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• 2023

Due to the complexity and difficulty in meeting the multiphase flow complexity, similarity, and multiscale characteristics, the mechanism of snow drift is so complicated that the snow deposition prediction is still inaccurate and needs to be far improved. Meanwhile, the validation of prediction methods is also limited due to a lack of field-measured data about snow deposition. To this end, a field measurement activity about snow deposition around a cube with time was carried out, and the snow accumulation process was measured under blowing snow conditions in northwest China. The maximum snow depth, snow profile, and variation in snow depth around the cube were discussed and analyzed. The measured results indicated three stages of snow accumulation around the cube. First, snow is deposited in windward, lateral and leeward regions, and then the snow depth in windward and lateral regions increases. Secondly, when the snow in the windward region reaches its maximum, the downwash flow erodes the snow against the front wall. Meanwhile, snow range and depth in lateral regions have a significant increase. Thirdly, a narrow road in the leeward region is formed with the increase in snow range and depth, which results in higher wind speed and reforming snow deposition there. The field measurement study in this paper not only furthers understanding of the snow accumulation process instead of final deposition under complex conditions but also provides an important benchmark for validating prediction methods.

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Journal of the Korean housing association
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• v.25 no.2
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• pp.11-18
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• 2014

This study looked at designing, building and operating temporary camp, the first structures that South Korea built in the Antarctica. While there may be differences in accordance with the topography, ground surfaces in the Antarctica are covered broken stones, glaciers and snow. Hence, such topographical characteristics should be taken into account when conducting any construction work. To ensure successful assembly construction in the Antarctica using modules, prior trial assembly work should be done in Korea to identify any possible trouble in the actual construction process. Assuming that the workers will have to spend at least one winter in the temporary camp, the work will be more severely affected by adverse weather conditions and snow drift, resulting in the need to clear snow. This can be by designing roofs with curved surfaces. Also, quantitative effects will need to be verified through simulation and actual measurement. It will also be necessary to assess the camp's thermal environment and examine its air-conditioning methods. To identify the temporary camp's thermal system, the temperatures and humidities were measured, and the heating system was designed not to offer automatic control or desired value selection functions.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.507-520
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• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.