[논문]조건부 Copula 함수 기반의 월단위 GloSea5 앙상블 예측정보 편의보정 기법과 연계한 일단위 시공간적 상세화 모델 개발

김용탁; 김민지; 권현한

doi:10.3741/jkwra.2021.54.12.1317

[국내논문] 조건부 Copula 함수 기반의 월단위 GloSea5 앙상블 예측정보 편의보정 기법과 연계한 일단위 시공간적 상세화 모델 개발
Development of daily spatio-temporal downscaling model with conditional Copula based bias-correction of GloSea5 monthly ensemble forecasts 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12, 2021년, pp.1317 - 1328

김용탁 (세종대학교 건설환경공학과) , 김민지 (기상청 기후과학국 수문기상팀) , 권현한 (세종대학교 건설환경공학과)

초록
AI-Helper

본 연구에서는 예측 모델의 정확성이 비교적 높은 월단위의 GloSea5 자료를 기반으로 예측강수량을 편의보정 및 시공간적으로 상세화하여 연속된 일단위 강우량을 모의하고자 하였다. 이를 위하여 GloSea5를 입력자료로 조건부 Copula와 MNHMM 모형을 적용하여 일단위 시계열 강우량 예측정보를 생산할 수 있는 모델링 체계를 제시하였다. 모의결과 동기간의 자료라도 매주 생산되는 결과가 큰 차이를 나타내는 예측강수량의 변동성이 유의하게 개선되었다. 모형 검증에서 모의된 일강수량, 연속강우확률, 연속무강우확률 및 강우일수가 관측자료와 유사한 값으로 모의되는 등 수문모형의 입력자료로써 활용성이 클 것으로 판단된다. 유역 단위에서의 모의된 강수량 계열간의 상관성 차이가 최소 -0.02에서 최대 0.10로 유역의 강우관측소간 상호종속성을 효과적으로 복원되는 등 수문모형의 입력자료로 활용 시 유역의 수문기상학적 반응을 보다 현실적으로 모의가 가능할 것으로 기대된다.

Abstract ▼ AI-Helper

This study aims to provide a predictive model based on climate models for simulating continuous daily rainfall sequences by combining bias-correction and spatio-temporal downscaling approaches. For these purposes, this study proposes a combined modeling system by applying conditional Copula and Multisite Non-stationary Hidden Markov Model (MNHMM). The GloSea5 system releases the monthly rainfall prediction on the same day every week, however, there are noticeable differences in the updated prediction. It was confirmed that the monthly rainfall forecasts are effectively updated with the use of the Copula-based bias-correction approach. More specifically, the proposed bias-correction approach was validated for the period from 1991 to 2010 under the LOOCV scheme. Several rainfall statistics, such as rainfall amounts, consecutive rainfall frequency, consecutive zero rainfall frequency, and wet days, are well reproduced, which is expected to be highly effective as input data of the hydrological model. The difference in spatial coherence between the observed and simulated rainfall sequences over the entire weather stations was estimated in the range of -0.02~0.10, and the interdependence between rainfall stations in the watershed was effectively reproduced. Therefore, it is expected that the hydrological response of the watershed will be more realistically simulated when used as input data for the hydrological model.

주제어

표/그림 (13)

그림 Fig. 1. Flowchart of research
그림 Fig. 2. ASOS weather stations (dots) used in this study
표 Table 1. Archimedean Copula Generator
그림 Fig. 3. A schematic diagram of MNHMM
그림 Fig. 4. Cross-validated results of conditional Copula model ((a) observed and simulated monthly rainfall time series, (b) Observed and hindcast original monthly rainfall time series, (c),(d) Observed, simulated and hindcast original monthly rainfall in 2008~2010 year, Seoul (No.108))
그림 Fig. 5. Distribution of bias-corrected forecasts through conditional Copula model (Seoul (No.108))
표 Table 2. Cross-validated results of conditional Copula model (1991~2010, Seoul (No.108))
표 Table 3. Comparison of observation and bias-corrected forecast through conditional Copula model (2018~2020, Seoul (No.108))
그림 Fig. 6. Validation result of MNHMM model (1991~2010, Han-river basin)
그림 Fig. 7. Validation result of MNHMM model (1991~2010, Han-river basin)
표 Table 4. Validation result of MNHMM model (1991~2010, Han-river basin)
그림 Fig. 8. Correlation Matrix between observed and simulated daily rainfall sequences obtained from MNHMM (Han-river basin)
그림 Fig. 9. Comparison between observed (black) and simulated (blue) rainfall sequences for Seoul station during the period of 2018-2020

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