Construction of a scientific system for disaster prevention of heavy rain and heavy snow using a small vertical radar network

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K05037
• 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 Institute of Technology, Toyama College
• Principal Investigator: SHIINA Toru 富山高等専門学校, その他部局等, 教授 (80196344)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 鉛直レーダ / 豪雨・豪雪 / 防災科学

Outline of Final Research Achievements

Three small vertical Doppler radars were deployed at fixed distance intervals to measure precipitation phenomena in three-dimensional space. In addition, the shape, water content, and velocity of rain and snow particles falling on the ground were measured by two disdrometers. A method was developed to estimate the backscattering cross section of rain and snow particles between the backscattering cross section by radar and the particle size and fall velocity distributions using a disdrometer. We attempted to classify precipitation types using sufficient statistics of the size and fall velocity distributions obtained by an optical disdrometer. Furthermore, a convolutional neural network model system was constructed to classify precipitation types using radar reflection spectra of a small Doppler radar.

Free Research Field

防災科学

Academic Significance and Societal Importance of the Research Achievements

本研究手法により、光学式ディスドロメータにて得られた粒径－落下速度分布ならびにドップラーレーダのレーダ反射スペクトルから降水種判別を行うことが可能である。更に、得られた降水種ごとに、ドップラースペクトルから降雪粒子の粒径－落下速度分布を推定することにより、降水種の密度を考慮した高精度な降雨・降雪強度推定への適用が期待される。