A Study on Relationship Between Precipitation Intensity of XRAIN and Snow-depth on the Ground

Date and Time: Tuesday, May 9, 2023: 10:00 AM - 12:00 PM
Location: Keck 100

 

Lead Presenter:  Kotaro Yamashiro, Master’s student
Affiliation:  Master of Engineering, Student, Hokkaido University
Social Media Handle:  None

Lead Presenter Biography

Kotaro Yamashiro,
Master of Engineering, Student, Hokkaido University
Email: ym16kt@eis.hokudai.ac.jp

Kotaro Yamashiro is a Master's student at Graduate School of Engineering, Hokkaido University.
As a graduation thesis, He studied “Relationship Between Precipitation Intensity of XRAIN and Snow-depth on the Ground”. In his master's program, he would like to analyze the impact of snowfall observed by XRAIN on traffic flow. 

Co-Authors

Sho Takahashi,
Ph.D., Associate Professor, Hokkaido University
Email: stakahashi@eng.hokudai.ac.jp
Phone Number: +81-11-706-6215

Toru Hagiwara,
Ph.D., Professor, Hokkaido University
Email: hagiwara@eng.hokudai.ac.jp
Phone Number: +81-11-706-6214

Presentation Description 

Short Abstract Summary
The eXtended Radar Information Network (XRAIN) is a system for observing precipitation established by Ministry of Land, Infrastructure, Transport and Tourism (MLIT) in Japan. The present study examined relationship between XRAIN precipitation intensity and multi-sensor snow depth change under various conditions. As a result, we found that correlation coefficients between XRAIN precipitation intensity and multi-sensor snow depth change were strongly depended on weather conditions. When the air temperature was below 0°C and the wind velocity was below 6.0 m/s, there was a highly positive correlation coefficient between the two 　(greater than 0.8). The correlation coefficients decreased after precipitation intensity over peak. In addition, correlation coefficients between the two became low when the wind velocity was above 6.0 m/s and the temperature was above 0 °C.
These results indicate that there are many weather conditions in which snow depth change on the ground could be estimated using XRAIN precipitation intensity. However, it was also found that the relationship between XRAIN precipitation intensity and multi-sensor snow depth change did not match in some weather conditions, indicating that advection should be considered on the XRAIN side and that the snow depth on the ground complicated changes by compaction or snow-blowing. From the results of this paper, the realization of snowfall observation and prediction under the environment of temperature below 0 °C is expected. Therefore, the related method of a novel monitoring method based on XRAIN for supporting winter road management is the future work of this study.

Extended Summary

Extended Abstract

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Presentation Video