| Title |
Evaluation of Performances of Radar-Based Rainfall Nowcasting Models Based on Input Domain Size |
| Authors |
서호철(Seo, Hocheol);김희철(Kim, Heechul);최수연(Choi, Suyeon);김연주(Kim, Yeonjoo) |
| DOI |
https://doi.org/10.12652/Ksce.2025.45.3.0339 |
| Keywords |
ConvLSTM, pySTEPS, 강우 예측, 레이더 ConvLSTM, PySTEPS, Rainfall prediction, Radar |
| Abstract |
The frequency and intensity of extreme weather events are increasing due to climate change, and precipitation patterns are becoming
more irregular. These changes have led to a rise in flood damage in urban areas, highlighting the growing importance of short-term
rainfall forecasting technologies that can accurately predict rainfall intensity and location within a limited time frame. This study
evaluates the rainfall prediction performance of ConvLSTM and pySTEPS models using radar-based rainfall data in the Andong Dam
watershed (128 km×128 km) in South Korea. This study specifically analyses how different input domain sizes (128 km×128 km,
256 km×256 km, and 384 km×384 km) affect the performance of precipitation prediction over the same spatial domain, the Andong
Dam basin. The results show that the prediction performance of both models improved as the input domain size increased. In
particular, the pySTEPS model using a 384 km×384 km input domain exhibited relatively superior rainfall prediction performance for
lead times exceeding 80 minutes. Therefore, this study suggests that the selection of an appropriate input domain for radar-based rainfall
prediction models significantly impacts prediction performance, providing important guidelines for improving forecasting accuracy in
future applications. |