| Title |
Development of a Hazardous Weather Classification Algorithm Based on Radar Rainfall Data Using Morphological Features |
| DOI |
https://doi.org/10.12652/Ksce.2025.45.6.0659 |
| Keywords |
위험기상; 선형강수대; 집중호우; 형태적 특징; 레이더 Hazardous weather; Line-shaped rainband; Heavy rainfall; Morphological features; Radar |
| Abstract |
Due to changes in rainfall patterns driven by climate change, the frequency and intensity of hazardous weather events such as localized heavy rain and line-shaped rainband systems that produce heavy rainfall over short periods are rapidly increasing. Early identification and prediction of the occurrence and intensity of such hazardous weather events are crucial for flood disaster response. This study adapted an algorithm to classify hazardous rainfall types (linear, stationary, and linear-stationary, others) using radar rainfall data and morphological characteristics of heavy rainfall and linear rainfall systems associated with extreme precipitation. Morphological features and stationary characteristics were distinguished based on aspect ratio, accumulated rainfall, rainfall area overlap ratio, and maximum rainfall. Using radar-based accumulated rainfall data at 3 km and 5 km spatial resolution, domestic rainfall cases from May to October 2021-2024 were analyzed to identify linear rainfall systems and heavy rainfall systems, and their occurrence locations and characteristics were examined. The 3 km resolution data more accurately classified linear-stationary rainfall patterns for typical linear rainfall system cases compared to 5 km resolution, it was confirmed that lower spatial resolution sometimes led to oversimplification of detailed structures of localized rainfall, resulting in misinterpretation of rainfall systems. Furthermore, for three major flood disaster cases, the morphological classification algorithm identified that linear-stationary rainfall types. This study confirmed the usefulness of the morphological hazardous weather classification algorithm for classifying heavy rainfall in Korea. |