Assessment of Snowmaking Conditions Based on Meteorological Reconstruction in the Beijing–Zhangjiakou Mountain Area of North China in 1978–2017

Jing Chen aBeijing Meteorological Information Center, Beijing, China

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Ji Wang bBeijing Municipal Climate Center, Beijing, China

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Runsheng Lin aBeijing Meteorological Information Center, Beijing, China

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Li Lu aBeijing Meteorological Information Center, Beijing, China

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Abstract

The outdoor events of the 2022 Winter Olympics and Paralympics will be held in the mountain areas of Beijing–Zhangjiakou, North China, where there is a complete reliance on artificial snow production owing to the dry and cold weather conditions. To assess how favorable the meteorological conditions are to snowmaking at the mountain venues, we reconstructed the daily wet-bulb temperature by adopting the thin-plate smoothing spline function method, and then we assessed the potential number of snowmaking days at eight weather stations (928–2098 m MSL) from October to the next April (i.e., the ski season) during the period 1978–2017. Results showed that artificial snow production would have been possible on 121 (±14) to 171 (±12) days on average at the stations with the increases of altitude, and the number of days decreased at rates of 4.3–5.1 days decade−1 across four decades of the study period. The cause of the decrease was the warming trend, which affected the number of days in low-altitude sites simultaneously, but the reduction was delayed with increased elevation. At monthly scale, the number of snowmaking days was robust in wintertime but reduced in other months of the ski season, particularly in March in more recent subperiods at high-altitude stations, which was determined by the increase in high values of daily mean wet-bulb temperature. Further improvements in assessing snowmaking conditions require detailed microclimatic studies to reduce the uncertainties caused by meteorological conditions as well as combination with model-based methods to determine potential future changes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ji Wang, wangji_zl@163.com

Abstract

The outdoor events of the 2022 Winter Olympics and Paralympics will be held in the mountain areas of Beijing–Zhangjiakou, North China, where there is a complete reliance on artificial snow production owing to the dry and cold weather conditions. To assess how favorable the meteorological conditions are to snowmaking at the mountain venues, we reconstructed the daily wet-bulb temperature by adopting the thin-plate smoothing spline function method, and then we assessed the potential number of snowmaking days at eight weather stations (928–2098 m MSL) from October to the next April (i.e., the ski season) during the period 1978–2017. Results showed that artificial snow production would have been possible on 121 (±14) to 171 (±12) days on average at the stations with the increases of altitude, and the number of days decreased at rates of 4.3–5.1 days decade−1 across four decades of the study period. The cause of the decrease was the warming trend, which affected the number of days in low-altitude sites simultaneously, but the reduction was delayed with increased elevation. At monthly scale, the number of snowmaking days was robust in wintertime but reduced in other months of the ski season, particularly in March in more recent subperiods at high-altitude stations, which was determined by the increase in high values of daily mean wet-bulb temperature. Further improvements in assessing snowmaking conditions require detailed microclimatic studies to reduce the uncertainties caused by meteorological conditions as well as combination with model-based methods to determine potential future changes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ji Wang, wangji_zl@163.com

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