2018 Fiscal Year Final Research Report
Development of estimation methods of physical quantities of new snow considering cloud microphysical processes using numerical meteorological model
| Project/Area Number |
16K01340
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Natural disaster / Disaster prevention science
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| Research Institution | National Research Institute for Earth Science and Disaster Prevention |
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Principal Investigator |
Nakai Sento 国立研究開発法人防災科学技術研究所, 雪氷防災研究部門, 総括主任研究員 (20360365)
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| Co-Investigator(Kenkyū-buntansha) |
橋本 明弘 気象庁気象研究所, 予報研究部, 主任研究官 (20462525)
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| Research Collaborator |
YAMAGUCHI Satoru
MOTOYOSHI Hiroki
YAMASHITA Katsuya
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | 降雪 / 新積雪 / SSA / 雲物理 / レーダー / メソ気象学 / 雪氷学 |
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| Outline of Final Research Achievements |
The specific surface area (SSA) of newly fallen snow (NS) was measured and the data of 102 cases were compiled and analyzed. The SSA of NS varied depending on the types of falling snow particles, local meteorological and synoptic conditions, and types of snow cloud deduced from radar observations. The SSA values of NS from low-pressure systems were smaller than that of NS from winter monsoon clouds. A comparison was made between observationally derived SSA of NS using empirical functions and cloud-particle contribution ratio derived from numerical experiments. Both parameters showed similar variation in daily-mean basis. It means that the estimation of the SSA using numerical meteorological models would be possible.
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| Free Research Field |
メソ気象学
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| Academic Significance and Societal Importance of the Research Achievements |
新雪のSSAという新しい指標を使うことで、今まで記述できなかった、降雪粒子が持つ履歴情報を積雪変質モデルに導入できる可能性を示すことができた。これは、社会的には、降雪起源の弱層になりやすい降雪となりにくい降雪を区別した予測の可能性を示唆したものである。
さらに、数値気象モデルそのものについても、本研究の雲物理過程寄与率の診断手法は、将来の雲物理スキーム改良による新積雪物理量の予報の可能性を示した。これは例えば2014年の関東甲信の大雪の時に多発したような、降雪種起源の表層雪崩の予測にも役立つと期待される。
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