2021 Fiscal Year Final Research Report

Development of the methods estimating physical properties of new snow based on the radar classification of snowstorms and cloud microphysical process modeling.

Research Project

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Project/Area Number	19K04978
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Review Section	Basic Section 25030:Disaster prevention engineering-related
Research Institution	National Research Institute for Earth Science and Disaster Prevention
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)
Co-Investigator(Kenkyū-buntansha)	橋本明弘気象庁気象研究所, 気象予報研究部, 主任研究官 (20462525)
Project Period (FY)	2019-04-01 – 2022-03-31
Keywords	降雪 / 雲物理 / 数値気象モデル / レーダー / 機械学習 / 新積雪 / 比表面積 / SSA
Outline of Final Research Achievements	Based on the simultaneous observations of the Snow and Ice Research Center radar and Specific Surface Area of the new snow at the Falling Snow Observatory (FSO), a precipitation intensity estimation equation for aggregates of unrimed cold-type snow crystals was formulated. A meteorology-based preprocessing was suggested to be necessary for snowstorm classification using deep learning technique. It was observationally clarified that SSA was small (large) for unrimed particles of low-pressure systems (heavily rimed snow aggregates and graupel during winter monsoon). New schemes were developed in which the ratio of aggregation in the cloud microphysical process of the JMA-NHM was given as a function of temperature based on surface hydrometeor observations. Thus, radar analyses and cloud microphysical modeling progressed based on the observations of physical properties of new snow.
Free Research Field	メソ気象学
Academic Significance and Societal Importance of the Research Achievements	降雪起源の弱層形成につながる雪についてレーダー降雪強度が過小評価となりうる点を指摘できたことは、レーダー気象学上，及び雪氷防災上の意義が大きい。地上降雪粒子観測データをもとにJMA-NHMの雲物理過程を最適化し雪粒子の凝集率を温度の関数として新たに与えたことは、気象学的に、また予報業務への効果が大きい。この改良が衛星リモートセンシングシミュレータの出力も改善できた点は、当初想定より進展したと言える。機械学習を用いた降水分布分類については、深層学習に関するWebサイトの開設により、気象学、雪氷学分野における深層学習の導入による研究加速の手段のひとつを提供できた。