- 1Korea University, Department of Civil, Environmental and Architectural Engineering, Seoul, Korea, Republic of (syku01@korea.ac.kr)
- 2Korea University, Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Seoul, Korea, Republic of (jongjin.baek@gmail.com)
- 3Korea University, Department of Civil, Environmental and Architectural Engineering, Seoul, Korea, Republic of (jbyun41@korea.ac.kr)
- 4Wuhan University, State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan, PR China (jongsuk@whu.edu.cn)
- 5Korea University, School of Civil, Environmental and Architectural Engineering, Seoul, Korea, Republic of (cjun@korea.ac.kr)
Abstract
Typhoons, accompanied by strong winds and heavy rainfall, are among the most devastating natural disasters, causing significant loss of life and property damage. To prepare for disaster situations caused by typhoons, predicting Typhoon-induced Accumulated Rainfall (TAR) is crucial. Many previous studies have attempted to predict TAR by evaluating the similarity between typhoon tracks and rainfall patterns using various methods. In this study, we utilized time series data evaluating methodologies (e.g. Dynamic Time Warping, Cosine Similarity, etc.), for assessing similarity of typhoon tracks. Using the best typhoon track data from the Regional Specialized Meteorology Center, Tokyo from 1979 to 2024 (1,157 in the Western North Pacific and the South China Sea), and National Hurricane Center (727 in Atlantic and 816 in Northeast and North Central Pacific), and precipitation data from the National Oceanic and Atmospheric Administrations Climate Prediction Center. The similarity of typhoon tracks was evaluated based on the latitude and longitude of the typhoon center and various meteorological properties such as pressure, translation speed. Typhoons with highly similar tracks were clustered, and the average TAR of the clustered typhoons was used to predict the TAR. To optimize the number of typhoons included within a single cluster, we determined the Optimal Ensemble Number (OEN) based on the root mean square error between observed TAR and predicted TAR. In this process, each typhoon’s trajectory and region are considered. Using OEN, we predicted TAR and validated the performance of our method by selecting typhoons which have different tracks and rainfall characteristics. The results demonstrated that the proposed methodology achieved performance comparable to that of previous studies. These findings suggest that methodologies for evaluating the similarity of time series data can comprehensively account for not only typhoon tracks but also unique meteorological attributes, contributing to improved TAR prediction.
Acknowledgement
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2024-00334564).
How to cite: Ku, S., Baik, J., Byun, J., Kim, J.-S., and Jun, C.: Optimizing typhoon-induced accumulated rainfall prediction through track similarity and meteorological properties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5147, https://doi.org/10.5194/egusphere-egu25-5147, 2025.
