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2025 Vol.58, Issue 7

Research Article

Development of grid-based rainfall scenarios using rain gauge data in Seoul: focusing on convective rainfall

지상 우량계 자료를 활용한 격자 형태의 강우 시나리오 생성 방법 개발: 서울특별시 내 대류성 강우를 중심으로

Hoyoung Chaa
,
Changhyun Junb
,
Jongjin Baikc
,
Hyeon-Joon Kimd*
차 호영a
,
전 창현b
,
백 종진c
,
김 현준d*
aPh.D. Student, Department of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul, Korea
bAssociate Professor, School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul, Korea
cResearch Professor, Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University, Seoul, Korea
dAssociate Researcher, Center of Oceanic and Meteorological Information, Pukyong National University, Busan, Korea
a고려대학교 일반대학원 건축사회환경공학과 박사과정
b고려대학교 공과대학 건축사회환경공학부 부교수
c고려대학교 미래건설환경융합연구소 연구교수
d부경대학교 해양기상정보센터 전임연구원
*Corresponding Author
31 July 2025. pp. 521-535
PDF XML Citation
Abstract

This study proposes a machine learning model to develop grid-based rainfall scenarios reflecting characteristics of convective rainfall, using rain gauge and radar data from Seoul, Republic of Korea, during 2019-2022. Rainfall data were separated into independent rainstorm events based on specific conditions (IETD: 2 hours, threshold: 0.5 mm) to identify convective rainfall as defined in prior studies. Radar data were denoised using Gaussian filtering, and the spatial extent of convective rainfall was defined by selecting the 36 grid points nearest to the rain gauge. For training, input data included rain gauge, their latitude and longitude, the distance between rain gauge and radar grid, and temporal variables. Output data comprised 36 nearest radar grids to the rain gauge location in Seoul. Machine learning models employed were Gradient Boosting, Random Forest, Extra Trees, and Multi-Layer Perceptron. The models achieved satisfactory performance, with Pearson correlation coefficients generally exceeding 0.7 when compared to rainfall intensity observed by radar highlighting their positive correlation with observed rainfall and the relatively lower MAE and RMSE of the Multi-Layer Perceptron compared to other models. The rainfall scenarios tended to underestimate rainfall intensity and showed spatial similarity to radar data, with normalized cross-correlation between 0.2 and 0.4. The methodology was deemed applicable as a spatial interpolation method for convective rainfall in urban watersheds, particularly in data-sparse environments with information about rain gauge.

Keywords
Convective rainfall
Spatial interpolation method
Radar composite field
Machine learning
Independent rainstorm event

본 연구에서는 2019년부터 2022년까지 서울특별시 내 위치한 우량계와 레이더를 통해 관측된 강우 자료를 활용하여 대류성 강우의 특징을 반영한 격자 형태의 강우 시나리오를 제작할 수 있는 머신러닝 모형을 제안하였다. 우량계에서 관측한 강우 자료는 특정 조건(IETD: 2시간, 절단값: 0.5 mm)에 따라 독립 호우사상으로 분리하였다. 독립 호우사상은 대류성 강우가 발생한 시기를 구분하기 위해 활용된다. 레이더 합성장은 가우시안 필터링을 활용하여 잡음을 제거한 이후, 우량계 중심으로 가까운 36개의 격자 자료를 대류성 강우의 공간적 크기로 정의하여 구분하였다. 이후, 머신러닝 구축을 위해 활용된 학습 자료는 입력자료로 우량계에서 관측된 강우 자료, 우량계의 위도와 경도, 우량계와 레이더 중심과의 거리 차이, 시간 관련 변수를, 출력자료로 우량계 주변의 36개 강우 격자 자료로 구성하였다. 구축된 자료는 회귀모형의 머신러닝에 적용하였으며, 활용된 머신러닝은 그래디언트 부스팅, 랜덤 포레스트, 엑스트라 트리, 다층 퍼셉트론이다. 분석 결과, 4개의 머신러닝은 레이더 합성장에서 관측된 강우강도와 서로 비교하면 평가 지표인 피어슨 상관계수가 전반적으로 0.7 이상이지만, 다층 퍼셉트론은 다른 모델에 비해 MAE와 RMSE가 작다는 점을 통해 준수한 성능을 가지고 있었다. 강우강도와의 유사성을 비교하면, 제작된 강우 시나리오는 우량계와 레이더보다 과소 추정하는 결과를 보여주었다. 마지막으로 강우 시나리오와 레이더 자료와의 공간분포를 비교한 결과를 보면, 정규화된 상관계수는 0.2에서 0.4로 산출되었다. 본 연구에서 제안한 방법론은 도시에 특화된 소규모 유역의 대류성 강우에 대한 강우 자료의 공간 보간 방법으로 활용 가능할 것으로 판단된다. 또한, 본 방법론은 우량계 중심의 정보만 있다면, 우량계 주변 격자 형태의 시나리오를 추정할 수 있다.

키워드
대류성 강우
강우 공간 보간
레이더 합성장
머신러닝
독립 호우사상
References
1

Baik, J., Park, J., Ryu, D., and Choi, M. (2016). “Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite‐based and ground‐based data.” Hydrological Processes, Vol. 30, No. 16, pp. 2789-2803.

10.1002/hyp.10786
2

Baik, J., and Choi, M. (2015). “Spatio-temporal variability of remotely sensed precipitation data from COMS and TRMM: case study of Korean peninsula in East Asia.” Advances in Space Research, Vol. 56, No. 6, pp. 1125-1138.

10.1016/j.asr.2015.06.015
3

Barnolas, M., Rigo, T., and Llasat, M.C. (2010). “Characteristics of 2-D convective structures in Catalonia (NE Spain): An analysis using radar data and GIS.” Hydrology and Earth System Sciences, Vol. 14, No. 1, pp. 129-139.

10.5194/hess-14-129-2010
4

Bezak, N., Šraj, M., and Mikoš, M. (2016). “Copula-based IDF curves and empirical rainfall thresholds for flash floods and rainfall- induced landslides.” Journal of Hydrology, Vol. 541, pp. 272- 284.

10.1016/j.jhydrol.2016.02.058
5

Cha, H., Baik, J., Lee, J., Na, W., Bateni, S.M., and Jun, C. (2024). “Generation of rainfall scenarios based on rainfall transition probability to determine temporal distribution of independent rainstorms.” Stochastic Environmental Research and Risk Assessment, Vol. 38, pp. 4959-4977.

10.1007/s00477-024-02844-7
6

Chen, F.W., and Liu, C.W. (2012). “Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan.” Paddy and Water Environment, Vol. 10, pp. 209-222.

10.1007/s10333-012-0319-1
7

Chen, M., Xie, P., Janowiak, J.E., and Arkin, P.A. (2002). “Global land precipitation: A 50-yr monthly analysis based on gauge observations.” Journal of Hydrometeorology, Vol. 3, No. 3, pp. 249-266.

10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2
8

Cho, H., and Jeong, J. (2006). “Application of spatial interpolation to rainfall data.” Spatial Information Research, Vol. 14, No. 1, pp. 29-41.

9

D’Haeyer, J.P. (1989). “Gaussian filtering of images: A regularization approach.” Signal Processing, Vol. 18, No. 2, pp. 169-181.

10.1016/0165-1684(89)90048-0
10

Daly, C. (2006). “Guidelines for assessing the suitability of spatial climate data sets.” International Journal of Climatology: A Journal of the Royal Meteorological Society, Vol. 26, No. 6, pp. 707-721.

10.1002/joc.1322
11

Danielsson, P.E. (1980). “Euclidean distance mapping.” Computer Graphics and Image Processing, Vol. 14, No. 3, pp. 227-248.

10.1016/0146-664X(80)90054-4
12

Das, A., and Siddique, L.A. (2012). “A case study of heavy downpour over NE India.” Mausam, Vol. 63, No. 3, pp. 503-507.

10.54302/mausam.v63i3.1294
13

Foehn, A., Hernández, J.G., Schaefli, B., and De Cesare, G. (2018). “Spatial interpolation of precipitation from multiple rain gauge networks and weather radar data for operational applications in Alpine catchments.” Journal of Hydrology, Vol. 563, pp. 1092-1110.

10.1016/j.jhydrol.2018.05.027
14

Gatidis, C., Schleiss, M., and Unal, C. (2024). “A new power-law model for μ-Λ relationships in convective and stratiform rainfall.” Atmospheric Measurement Techniques, Vol. 17, No. 1, pp. 235-245.

10.5194/amt-17-235-2024
15

Gentile, M., Courbin, F., and Meylan, G. (2013). “Interpolating point spread function anisotropy.” Astronomy & Astrophysics, Vol. 549, pp. 1-20.

10.1051/0004-6361/201219739
16

Gower, J.C. (1985). “Properties of euclidean and non-euclidean distance matrices.” Linear Algebra and its APplications, Vol. 67, pp. 81-97.

10.1016/0024-3795(85)90187-9
17

Harrison, D.L., Scovell, R.W., and Kitchen, M. (2009). “High- resolution precipitation estimates for hydrological uses.” In Proceedings of the Institution of civil Engineers-water Management, Vol. 162, No. 2, pp. 125-135.

10.1680/wama.2009.162.2.125
18

Hofstra, N., and New, M. (2009). “Spatial variability in correlation decay distance and influence on angular‐distance weighting interpolation of daily precipitation over Europe.” International Journal of Climatology: A Journal of the Royal Meteorological Society, Vol. 29, No. 12, pp. 1872-1880.

10.1002/joc.1819
19

Hofstra, N., Haylock, M., New, M., Jones, P., and Frei, C. (2008). “Comparison of six methods for the interpolation of daily, European climate data.” Journal of Geophysical Research: Atmospheres, Vol. 113, No. D21, pp. 1-19.

10.1029/2008JD010100
20

Jung, I., Choi, S., Jung, D., Woo, J., Sim, S., and Han, K.S. (2024). “Accuracy assessment of precipitation products from GPM IMERG and CAPPI ground radar over South Korea.” Korean Journal of Remote Sensing, Vol. 40. No. 3, pp. 269-274.

21

Kim, B.S., Hong, J.B., Kim, H.S., and Yoon, S.Y. (2007). “Combining radar and rain gauge rainfall estimates for flood forecasting using conditional merging method.” World Environmental and Water Resources Congress 2007: Restoring Our Natural Habitat, Tampa, FL, U.S., pp. 1-16.

10.1061/40927(243)414
22

Kim, H.A., Ho, J., Zhang, G., Ha, K.J., Hong, S.Y., and Ho, C.H. (2024a). “Polarimetric radar signatures in various lightning activities during Seoul (Korea) flood on August 8, 2022.” Asia-Pacific Journal of Atmospheric Sciences, Vol. 60, No. 4, pp. 401-415.

10.1007/s13143-023-00346-0
23

Kim, J., and Kang, J. (2023). “Development of hazard capacity factor design model for net-zero: Evaluation of the flood adaptation effects considering green-gray infrastructure interaction.” Sustainable Cities and Society, Vol. 96, 104625.

10.1016/j.scs.2023.104625
24

Kim, S., Kim, Y., Ghorbani, M.A., and Kim, D. (2024b). “Analysis on the temporal scaling behavior of extreme rainfall in Korean Peninsula based on high-resolution radar-based precipitation data.” Journal of Hydrology: Regional Studies, Vol. 55, 101915.

10.1016/j.ejrh.2024.101915
25

Kitchen, M., and Blackall, R.M. (1992). “Representativeness errors in comparisons between radar and gauge measurements of rainfall.” Journal of Hydrology, Vol. 134, No. 1-4, pp. 13-33.

10.1016/0022-1694(92)90026-R
26

Korea Meteorological Administration (KMA) (2024). Korea, accessed 1 November 2024, <http://data.kma.go.kr>.

27

Kyznarová, H., and Novák, P. (2009). “CELLTRACK - Convective cell tracking algorithm and its use for deriving life cycle characteristics.” Atmospheric Research, Vol. 93, No. 1-3, pp. 317-327.

10.1016/j.atmosres.2008.09.019
28

Liu, X., Zheng, T., Wan, Q., and Xin, Y. (2018). “Spatiotemporal distribution characteristics and variation trends of hierarchical precipitation in Guangdong province over the past 50 years.” Journal of Tropical Meteorology, 구Vol. 24, No. 1, pp. 82-91.

29

Ochoa-Rodriguez, S., Wang, L.P., Willems, P., and Onof, C. (2019). “A review of radar‐rain gauge data merging methods and their potential for urban hydrological applications.” Water Resources Research, Vol. 55, No. 8, pp. 6356-6391.

10.1029/2018WR023332
30

Peleg, N., Marra, F., Fatichi, S., Molnar, P., Morin, E., Sharma, A., and Burlando, P. (2018). “Intensification of convective rain cells at warmer temperatures observed from high-resolution weather radar data.” Journal of Hydrometeorology, Vol. 19, No. 4, pp. 715-726.

10.1175/JHM-D-17-0158.1
31

Penide, G., Protat, A., Kumar, V.V., and May, P.T. (2013). “Comparison of two convective/stratiform precipitation classification techniques: Radar reflectivity texture versus drop size distribution-based approach.” Journal of Atmospheric and Oceanic Technology, Vol. 30, No. 12, pp. 2788-2797.

10.1175/JTECH-D-13-00019.1
32

Restrepo-Posada, P.J., and Eagleson, P.S. (1982). “Identification of independent rainstorms.” Journal of Hydrology, Vol. 55, No. 1-4, pp. 303-319.

10.1016/0022-1694(82)90136-6
33

Rigo, T., and Llasat, M.C. (2004). “A methodology for the classification of convective structures using meteorological radar: Application to heavy rainfall events on the Mediterranean coast of the Iberian Peninsula.” Natural Hazards and Earth System Sciences, Vol. 4, No. 1, pp. 59-68.

10.5194/nhess-4-59-2004
34

Schleiss, M., Olsson, J., Berg, P., Niemi, T., Kokkonen, T., Thorndahl, S., Nielsen, R., Nielsen, J. E., Bozhinova, D., and Pulkkinen, S. (2020). “The accuracy of weather radar in heavy rain: A comparative study for Denmark, the Netherlands, Finland and Sweden.” Hydrology and Earth System Sciences, Vol. 24, No. 6, pp. 3157-3188.

10.5194/hess-24-3157-2020
35

Shin, D.H., Park, R.H., Yang, S., and Jung, J.H. (2005). “Block-based noise estimation using adaptive Gaussian filtering.” IEEE Transactions on Consumer Electronics, Vol. 51, No. 1, pp. 218-226.

10.1109/TCE.2005.1405723
36

Song, H.J., Choi, W., and Wang, Z. (2024). “Abnormal climate in 2022 Summer in Korea and Asia.” Asia-Pacific Journal of Atmospheric Sciences, Vol. 60, No. 4, pp. 385-386.

10.1007/s13143-024-00381-5
37

Todini, E. (2001). “A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements.” Hydrology and Earth System Sciences, Vol. 5, No. 2, pp. 187-199.

10.5194/hess-5-187-2001
38

Vörösmarty, C.J., Bravo de Guenni, L., Wollheim, W.M., Pellerin, B., Bjerklie, D., Cardoso, M., D'Almeida, C., Green, P., and Colon, L. (2013). “Extreme rainfall, vulnerability and risk: A continental-scale assessment for South America.” Philosophical TRansactions of the Royal Society a: Mathematical, Physical and Engineering Sciences, Vol. 371, No. 2002, 20120408.

10.1098/rsta.2012.0408
39

Wackernagel, H. (2003). Multivariate geostatistics: An introduction with applications. Springer Science & Business Media, Germany.

10.1007/978-3-662-05294-5_1
40

Wei, L., Hu, K.H., and Hu, X.D. (2018). “Rainfall occurrence and its relation to flood damage in China from 2000 to 2015.” Journal of Mountain Science, Vol. 15, No. 11, pp. 2492-2504.

10.1007/s11629-018-4931-4
41

Xu, W., Zou, Y., Zhang, G., and Linderman, M. (2015). “A comparison among spatial interpolation techniques for daily rainfall data in Sichuan Province, China.” International Journal of Climatology, Vol. 35, No. 10, pp. 2898-2907.

10.1002/joc.4180
42

Yoo, C., Yoon, J., Kim, J., and Ro, Y. (2016). “Evaluation of the gap filler radar as an implementation of the 1.5 km CAPPI data in Korea.” Meteorological Applications, Vol. 23, No. 1, pp. 76-88.

10.1002/met.1531
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 :7
  • Pages :521-535
  • Received Date : 2025-01-27
  • Revised Date : 2025-04-22
  • Accepted Date : 2025-04-22
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.7.521
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