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2025 Vol.58, Issue 11S-1 Preview Page

Research Article

Real-time snow depth simulation in South Korea using radar-based precipitation and KMA API data

레이더 기반 강수량 및 KMA API 데이터를 활용한 국내 실시간 적설 깊이 모의

Duc Anh Daoab
,
Soohyun Kimbc
,
Dongkyun Kimd
,
Jeongha Parke*
Dao Duc Anhab
,
김 수현bc
,
김 동균d
,
박 정하e*
aLecturer, Faculty of Water Resources Engineering, Thuyloi University, Ha Noi city, Viet Nam
bPostdoctoral Researcher, Department of Civil Engineering, Hongik University, Seoul, Korea
cPostdoctoral Researcher, Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA
dProfessor, Department of Civil and Environmental Engineering, Hongik University, Seoul, Korea
eResearch Professor, Department of Civil Engineering, Hongik University, Seoul, Korea
a투이러이 대학교 수자원공학부 (베트남 하노이) 전임강사
b홍익대학교 토목공학과 박사후연구원
c하와이 대학교 마노아 캠퍼스 토목환경공학과 및 수자원연구센터 박사후연구원
d홍익대학교 건설환경공학과 교수
e홍익대학교 토목공학과 연구교수
*Corresponding Author
30 November 2025. pp. 1181-1193
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Abstract

Real-time and high-resolution snow depth monitoring is essential for early warning systems, transportation safety, and disaster risk management, particularly in complex terrains like South Korea. This study presents a fully automated snow depth simulation system that operates at 15-minute intervals and 500-meter resolution, leveraging radar-based precipitation, ground meteorological data, and a physically interpretable threshold model. All input data are retrieved automatically via the Korea Meteorological Administration (KMA) API, enabling continuous, nationwide snow depth mapping without manual intervention. During the evaluation period from 1 November to 2 December 2024, the model demonstrated strong agreement with observations at 94 stations, achieving an RMSE of 1.03 cm, NSE of 0.52, and R2 of 0.53. The system provides fine temporal resolution and full automation, offering a practical advancement toward real-time snow depth modeling. Although performance slightly declined at higher elevations, it remained robust across diverse topographic conditions. Validation against MODIS-derived NDSI snow cover yielded an accuracy of 0.82 and an F1-score of 0.51, confirming spatial consistency. While the model does not yet simulate internal snowpack processes, it provides a scalable and transparent solution for real-time snow monitoring and has strong potential for deployment in other radar-equipped regions.

Keywords
Snow depth simulation
Radar-based precipitation
Real-time monitoring
Disaster management

대한민국과 같이 복잡한 지형에서는 조기 경보 시스템, 교통 안전, 재난 위험 관리를 위해 실시간 고해상도 적설심 모니터링이 필수적이다. 본 연구는 레이더 기반 강수량, 지상 기상 자료, 그리고 물리적으로 해석 가능한 임계값 모델을 활용하여 15분 간격과 500미터 해상도로 운영되는 완전 자동화된 적설심 모의 시스템을 제시한다. 모든 입력 자료는 기상청(KMA) API를 통해 자동으로 수집되며, 이를 통해 수동 개입 없이 지속적인 전국 단위의 적설심 매핑이 가능하다. 2024년 11월 1일부터 12월 2일까지의 평가 기간 동안, 모델은 94개 관측소의 관측값과 높은 일치도를 보였으며, RMSE 1.03 cm, NSE 0.52, R2 0.53을 달성했다. 이 시스템은 높은 시간 해상도와 완전한 자동화를 제공하여 실시간 적설량 모델링의 실무적인 진전을 이루었다. 고도가 높은 지역에서는 성능이 다소 감소했지만, 다양한 지형 조건 전반에 걸쳐 견고한 성능을 유지했다. MODIS 기반 NDSI 적설 피복 자료와의 검증에서는 정확도 0.82, F1-점수 0.51을 기록하여 공간적 일관성을 확인했다. 비록 모델이 아직 내부 적설층 과정을 모의하지는 않지만, 실시간 적설 감시를 위한 확장 가능하고 투명한 솔루션을 제공하며, 레이더가 설치된 다른 지역에도 적용될 잠재력이 크다.

키워드
적설심 모의
레이더 기반 강수
실시간 모니터링
재난관리
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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 :11
  • Pages :1181-1193
  • Received Date : 2025-09-15
  • Accepted Date : 2025-09-29
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.S-1.1181
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