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http://dx.doi.org/10.3741/JKWRA.2021.54.12.1317

Development of daily spatio-temporal downscaling model with conditional Copula based bias-correction of GloSea5 monthly ensemble forecasts  

Kim, Yong-Tak (Department of Civil and Environmental Engineering, Sejong University)
Kim, Min Ji (Hydrometeorological and Meteorological Drought Team, Climate Science Bureau, Korea Meteorological Administration)
Kwon, Hyun-Han (Department of Civil and Environmental Engineering, Sejong University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.12, 2021 , pp. 1317-1328 More about this Journal
Abstract
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.
Keywords
GloSea5; Spatio-temporal downscaling; Conditional copula; Bias-correction; Ensemble;
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