Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-629-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-629-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2026
Research article |
| 26 Jan 2026
| 26 Jan 2026
Numerical simulation of a severe blowing snow event over the Prydz Bay Region
Jinfeng Ding
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
Yuan Shang
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
Yulong Shan
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
Jingkai Ma
National Marine Environmental Forecasting Center, Beijing, China
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
Xichuan Liu
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
Xiaoqiao Wang
CORRESPONDING AUTHOR
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
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Cited articles
Amory, C.: Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East Antarctica, The Cryosphere, 14, 1713–1725, https://doi.org/10.5194/tc-14-1713-2020, 2020.
Amory, C., Trouvilliez, A., Gallée, H., Favier, V., Naaim-Bouvet, F., Genthon, C., Agosta, C., Piard, L., and Bellot, H.: Comparison between observed and simulated aeolian snow mass fluxes in Adélie Land, East Antarctica, The Cryosphere, 9, 1373–1383, https://doi.org/10.5194/tc-9-1373-2015, 2015.
Amory, C., Kittel, C., Le Toumelin, L., Agosta, C., Delhasse, A., Favier, V., and Fettweis, X.: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica, Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, 2021.
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002.
Clifton, A., Rüedi, J.-D., and Lehning, M.: Snow saltation threshold measurements in a drifting-snow wind tunnel, J. Glaciol., 52, 585–596, 2006.
Cierco, F.-X., Naaim-Bouvet, F., and Bellot, H.: Acoustic sensors for snowdrift measurements: How should they be used for research purposes?, Cold Reg. Sci. Technol., 49, 74–87, https://doi.org/10.1016/j.coldregions.2007.01.002, 2007.
Déry, S. J. and Yau, M. K.: A bulk blowing snow model, Bound.-Lay. Meteorol., 93, 237–251, https://doi.org/10.1023/A:1002065615856, 1999.
Déry, S. J. and Yau, M. K.: Large-scale mass balance effects of blowing snow and surface sublimation, J. Geophys. Res.-Atmos., 107, ACL 8-1–ACL 8-17, https://doi.org/10.1029/2001JD001251, 2002.
Essery, R., Li, L., and Pomeroy, J.: A distributed model of blowing snow over complex terrain, Hydrol. Process., 13, 2423–2438, 1999.
Gerber, F., Sharma, V., and Lehning, M.: CRYOWRF – Model evaluation and the effect of blowing snow on the Antarctic surface mass balance, J. Geophys. Res.-Atmos., 128, e2022JD037744, https://doi.org/10.1029/2022JD037744, 2023.
Gallée, H., Trouvilliez, A., Agosta, C., Genthon, C., Favier, V., and Naaim-Bouvet, F.: Transport of snow by the wind: A comparison between observations in Adélie Land, Antarctica, and simulations made with the regional climate model MAR, Bound.-Lay. Meteorol., 146, 133–147, https://doi.org/10.1007/s10546-012-9764-z, 2013.
Gallée, H., Guyomarc'h, G., and Brun, E.: Impact of snow drift on the Antarctic ice sheet surface mass balance: possible sensitivity to snow-surface properties, Bound.-Lay. Meteorol., 99, 1–19, https://doi.org/10.1023/A:1018776422809, 2001.
Gossart, A., Souverijns, N., Gorodetskaya, I. V., Lhermitte, S., Lenaerts, J. T. M., Schween, J. H., Mangold, A., Laffineur, Q., and van Lipzig, N. P. M.: Blowing snow detection from ground-based ceilometers: application to East Antarctica, The Cryosphere, 11, 2755–2772, https://doi.org/10.5194/tc-11-2755-2017, 2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. R. Meteorolog. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Kain, J. S.: The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kodama, Y., Wendler, G., and Gosink, J.: The effect of blowing snow on katabatic winds in Antarctica, Ann. Glaciol., 6, 59–62, 1985.
Lilly, D. K.: A Severe Downslope Windstorm and Aircraft Turbulence Event Induced by a Mountain Wave, J. Atmos. Sci., 35, 59–77, https://doi.org/10.1175/1520-0469(1978)035<0059:ASDWAA>2.0.CO;2, 1978.
Lehning, M., Löwe, H., Ryser, M., and Raderschall, N.: Inhomogeneous precipitation distribution and snow transport in steep terrain, Water Resour. Res., 44, W07404, https://doi.org/10.1029/2007WR006545, 2008.
Lehning, M., Doorschot, J., and Bartelt, P.: A snowdrift index based on SNOWPACK model calculations, Ann. Glaciol., 31, 382–386, https://doi.org/10.3189/172756400781819770, 2000.
Lenaerts, J. T. M., van den Broeke, M. R., van Angelen, J. H., van Meijgaard, E., and Déry, S. J.: Drifting snow climate of the Greenland ice sheet: a study with a regional climate model, The Cryosphere, 6, 891–899, https://doi.org/10.5194/tc-6-891-2012, 2012.
Lenaerts, J. T. M. and van den Broeke, M. R.: Modeling drifting snow in Antarctica with a regional climate model: 2. Results, J. Geophys. Res.-Atmos., 117, D05109, https://doi.org/10.1029/2010JD015419, 2012.
Ligtenberg, S. R. M., Helsen, M. M., and van den Broeke, M. R.: An improved semi-empirical model for the densification of Antarctic firn, The Cryosphere, 5, 809–819, https://doi.org/10.5194/tc-5-809-2011, 2011.
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description, J. Atmos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1, 2005.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification, Bound.-Lay. Meteorol., 112, 1–31, https://doi.org/10.1023/B:BOUN.0000020164.04146.98, 2004.
Parish, T. R. and Bromwich, D. H.: The surface windfield over the Antarctic ice sheets, Nature, 328, 51–54, 1987.
Parish, T. R. and Cassano, J. J.: Diagnosis of the katabatic wind influence on the wintertime Antarctic surface wind field from numerical simulations, Mon. Weather Rev., 131, 1128–1139, https://doi.org/10.1175/1520-0493(2003)131<1128:DOTKWI>2.0.CO;2, 2003.
Palm, S. P., Yang, Y., Spinhirne, J. D., and Marshak, A.: Satellite remote sensing of blowing snow properties over Antarctica, J. Geophys. Res.-Atmos., 116, D16123, https://doi.org/10.1029/2011JD015828, 2011.
Palm, S. P., Kayetha, V., Yang, Y., and Pauly, R.: Blowing snow sublimation and transport over Antarctica from 11 years of CALIPSO observations, The Cryosphere, 11, 2555–2569, https://doi.org/10.5194/tc-11-2555-2017, 2017.
Pomeroy, J. W., Gray, D. M., and Landine, P. G.: The prairie blowing snow model – Characteristics, validation, operation, J. Hydrol., 144, 165–192, https://doi.org/10.1016/0022-1694(93)90171-5, 1993.
Priestley, M. D., Ackerley, D., Catto, J. L., Hodges, K. I., McDonald, R. E., and Lee, R. W.: An overview of the extratropical storm tracks in CMIP6 historical simulations, J. Climate, 33, 6315–6343, https://doi.org/10.1175/JCLI-D-19-0928.1, 2020.
Scarchilli, C., Frezzotti, M., Grigioni, P., De Silvestri, L., Agnoletto, L., and Dolci, S.: Extraordinary blowing snow transport events in East Antarctica, Clim. Dynam., 34, 1195–1206, https://doi.org/10.1007/s00382-009-0601-0, 2010.
Stoll, P. J.: A global climatology of polar lows investigated for local differences and wind-shear environments, Weather Clim. Dynam., 3, 483–504, https://doi.org/10.5194/wcd-3-483-2022, 2022.
Souverijns, N., Gossart, A., Gorodetskaya, I. V., Lhermitte, S., Mangold, A., Laffineur, Q., Delcloo, A., and van Lipzig, N. P. M.: How does the ice sheet surface mass balance relate to snowfall? Insights from a ground-based precipitation radar in East Antarctica, The Cryosphere, 12, 1987–2003, https://doi.org/10.5194/tc-12-1987-2018, 2018.
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, 2023.
Sato, T., Kimura, T., Ishimaru, T., and Maruyama, T.: Field test of a new snow-particle counter (SPC) system, Ann. Glaciol., 18, 149–154, https://doi.org/10.3189/S0260305500011411, 1993.
Turner, J., Chenoli, S. N., Abu Samah, A., Marshall, G. J., Phillips, T., and Orr, A.: Strong wind events in the Antarctic, J. Geophys. Res.-Atmos., 114, D18103, https://doi.org/10.1029/2008JD011642, 2009.
Trouvilliez, A., Naaim-Bouvet, F., Bellot, H., Genthon, C., and Gallée, H.: Evaluation of the FlowCapt Acoustic Sensor for the Aeolian Transport of Snow, J. Atmos. Ocean. Tech., 32, 1630–1641, https://doi.org/10.1175/JTECH-D-14-00104.1, 2015.
van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.
Vignon, É., Picard, G., Durán-Alarcón, C., Alexander, S. P., Gallée, H., and Berne, A.: Gravity wave excitation during the coastal transition of an extreme katabatic flow in Antarctica, J. Atmos. Sci., 77, 1295–1312, https://doi.org/10.1175/JAS-D-19-0264.1, 2020.
Wang, X.: Numerical Simulation of a Severe Blowing Snow Event over the Prydz Bay Region, Zenodo [data set] https://doi.org/10.5281/zenodo.18213206, 2026.
Ye, J., Liu, L., Ding, J., Liu, X., Xie, H., and Chen, Y.: First blowing snow measurement at Zhongshan Station in Antarctica using ceilometer, Adv. Atmos. Sci., 42, 1–11, https://doi.org/10.1007/s00376-024-4172-0, 2025.
Short summary
This study employed the numerical model to simulate an intense blowing snow event near Zhongshan Station, East Antarctica, from 15–17 July 2022. While primarily driven by a mid-latitude cyclone, the event’s evolution was strongly modulated by complex local topography, which influenced airflow and enhanced snow transport. Terrain-induced uplift intensified snowfall and prolonged blizzard conditions, underscoring the importance of high-resolution modeling for Antarctic weather research.
This study employed the numerical model to simulate an intense blowing snow event near Zhongshan...
