Kazuya Takami, Rimpei Kamamoto, Kenji Suzuki, Kosei Yamaguchi, Eiichi Nakakita
Received 2022/07/11, Accepted 2022/10/17, Published 2022/12/01

Kazuya Takami¹⁾, Rimpei Kamamoto¹⁾, Kenji Suzuki²⁾, Kosei Yamaguchi³⁾, Eiichi Nakakita³⁾

1) Meteorological Disaster Prevention, Railway Technical Research Institute, Japan
2) Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan
3) Disaster Prevention Research Institute, Kyoto University, Japan

Direct and steady observation of newly fallen snow density is difficult because of the effect of snow compaction. We aimed to evaluate a method for estimation of newly fallen snow density using particle size and fall velocity distribution obtained from disdrometer (Parsivel²) for snowfall cases at temperatures below 0°C. As disdrometer observations cannot easily manage cases of mixed hydrometeor such as graupel and aggregate, we considered only the averaged riming degree of snowfall particles as an index without classifying the hydrometeor types. We observed newly fallen snow density using a snow board for 157 cases of snowfall in the winters of 2020–2021 and 2021–2022 in Niigata Prefecture, Japan. Furthermore, we calculated the riming degree for each case using a fraction of squared fall speed with respect to the unrimed aggregate. The results revealed that the averaged riming degree was correlated with density of newly fallen snow. Based on its relationship with the averaged riming degree investigated herein, the newly fallen snow density can be estimated from the particle size and fall speed distribution, which can be automatically observed using a disdrometer without any manual observations via a snowboard.

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