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2025 Vol.58, Issue 2

Research Article

Advancing deep learning-based precipitation nowcasting model with radar and ground observation

레이더 및 지상 관측 강우 자료를 통한 딥러닝 기반 강우 예측 모델 개선

Suyeon Choia
,
Yeonjoo Kimb*
최 수연a
,
김 연주b*
aPh.D Candidate, Department of Civil & Environmental Engineering, Yonsei University, Seoul, Korea
bProfessor, Department of Civil & Environmental Engineering, Yonsei University, Seoul, Korea
a연세대학교 건설환경공학과 박사수료
b연세대학교 건설환경공학과 교수
*Corresponding Author
28 February 2025. pp. 91-103
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Abstract

To forecast accurate predictions of flooding in a basin, it is essential to have accurate precipitation forecasts. Recently, there have been a number of studies that utilize machine learning techniques to improve the performance of precipitation prediction models. Several radar-based precipitation prediction models have been developed and shown their capabilities, mainly for spatial precipitation prediction over basins. However, since the model is developed based on radar data, which has limitations in accurate rainfall estimation, it is challenging to accurately predict the actual precipitation that falls in a basin. In this study, we proposed a strategy to improve the prediction performance of radar-based models using machine learning by incorporating observations from gauge stations during the training process. To develop a model that produces predicted rainfall up to 6 hours for the Yeongsangang River basin, a conditional generative adversarial neural network (cGAN) was applied. Along with the radar data, the model was trained using the rainfall data from gauge stations (Automated Synoptic Observing System, ASOS) within the domain of the Yeongsangang River basin. Through the results, we showed that the model that considers point rainfall data as an additional input performs better in predicting the spatial pattern of rainfall than the model that only uses radar data.

Keywords
Precipitation nowcasting
Radar
Gauge station
cGAN

유역의 정확한 홍수량 예측을 위해서는 정확한 강우 예측이 필수적이다. 최근 머신러닝 기법을 활용하여 강우 예측 모델의 성능을 개선하는 연구가 활발히 진행되고 있다. 주로 유역에 대한 공간 강우량을 예측하기 위해 레이더 기반 강우 예측 모델이 개발되어 그 성능을 입증하였으나, 정확한 강우량 추정에 한계가 있는 레이더 자료를 기반으로 개발되는 모델 특성상, 실제 유역에 떨어지는 강우량을 정확하게 예측하는 데에는 한계가 있다. 본 연구에서는 기존 머신러닝을 활용한 레이더 기반 모델의 예측 성능 개선을 위해 지점강우를 훈련과정에서 사용하는 기법을 제안하였다. 영산강 유역에 대해 최대 6시간까지의 예측 강우를 생산하는 모델 개발을 위해 조건부 생성적 적대 신경망(conditional Generative Adversarial Network, cGAN)을 활용하였으며, 모델 훈련을 위해 기상레이더 자료와 함께, 영산강 유역의 도메인 내에 종관기상관측(Automated Synoptic Observing System, ASOS) 강우자료를 사용하였다. 결과를 통해, 레이더 자료만 사용한 모델에 비해 지점강우자료를 추가입력자료로 고려한 모델이 유역의 공간 강우량을 예측하는데 더 우수한 성능을 보이는 것을 확인하였다.

키워드
강우예측
레이더
지점강우
조건부 생성적 적대 신경망
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 58
  • No :2
  • Pages :91-103
  • Received Date : 2024-11-09
  • Revised Date : 2024-12-04
  • Accepted Date : 2024-12-09
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.2.91
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