Development of highly versatile humidity environmental index estimation method and evaluation of its spatio-temporal distribution.

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K04757
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 22060:Environmental systems for civil engineering-related
• Research Institution: Kochi University of Technology
• Principal Investigator: Akatsuka Shin 高知工科大学, システム工学群, 准教授 (80548743)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 相対湿度 / 可降水量

Outline of Final Research Achievements

A relative humidity estimation model was constructed by machine learning method, using the amount of precipitable water, meteorological observation data and landuse ratio at AMeDAS stations as input data. As a result, the hourly relative humidity was estimated with higher accuracy than the accuracy of the relative humidity forecast value from the numerical prediction. A method was also developed to produce a precipitable water distribution map with a spatial resolution of 1 km and a temporal resolution of 1 hour using numerical prediction data with a spatial resolution of 5 km and a temporal resolution of 3 hours. A method for estimating relative humidity using a precipitable water distribution map was investigated, and it was shown that the 90 m resolution relative humidity distribution for the whole of Shikoku could be estimated with the same accuracy as the 5 km resolution numerical prediction data by using the accumulated water vapour from the ground to the 900 hPa pressure level.

Free Research Field

空間情報工学

Academic Significance and Societal Importance of the Research Achievements

本研究では，高時空間分解能の可降水量分布図作成手法を開発し，可降水量分布から相対湿度分布を推定できる可能性を示した．高時空間分解能の可降水量分布図から相対湿度分布を高精度で推定できるようになれば，熱中症リスクの評価，圃場における遅霜や病虫害などの発生予察，コンクリート構造物の劣化予測などへの活用が期待できる．また，高時空間分解能の可降水量分布図からこれまでの可降水量の分布傾向を把握することができ，豪雨発生の事前予測への貢献も期待できる．