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  • Author or Editor: Moosup Kim x
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Moosup Kim
,
Seon Tae Kim
, and
Yeomin Jeong

Abstract

In this paper, we propose a downscaling method that statistically describes a local-scale climate from large-scale circulations using the case of a Korean basin during boreal winter. Specifically, since the East Asian winter monsoon (EAWM) affects the climate of the Korean Peninsula, we make a weather generator model describing the response of the basin climate to the monsoon strength. Moreover, it operates on the basis of a tercile probabilistic prediction of the EAWM strength to generate diverse scenarios of daily weather sequence during the season, which can be utilized in evaluation of the climate impact. We evaluate the prediction skills of operational hindcasts for several existing EAWM indices by applying a multinomial logistic regression method to choose the most suitable index for the downscaling. In the weather generator model, the precipitation model part is designed to be fully parametric. Its parameter values are allowed to vary according to the monsoon strength so that they can represent the climate variability of precipitation. In the temperature model part, the daily temporal variations of the temperature over the Korean basin are decomposed into several oscillations with different frequencies. Since the slowly varying oscillations significantly respond to the monsoon strength, the proposed downscaling scheme is based on the statistical simulation of oscillations according to the monsoon strength. The proposed downscaling scheme is evaluated in terms of the reproducibility of the climate characteristics for a given EAWM strength and the informativeness for predicting monthly climate characteristics.

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Moosup Kim
,
Yoo-Bin Yhang
, and
Chang-Mook Lim

Abstract

The daily precipitation data generated by dynamical models, including regional climate models, generally suffer from biases in distribution and spatial dependence. These are serious flaws if the data are intended to be applied to hydrometeorological studies. This paper proposes a scheme for correcting the biases in both aspects simultaneously. The proposed scheme consists of two steps: an aggregation step and a disaggregation step. The first one aims to obtain a smoothed precipitation pattern that must be retained in correcting the bias, and the second aims to make up for the deficient spatial variation of the smoothed pattern. In both steps, the Gaussian copula plays important roles since it not only provides a feasible way to correct the spatial correlation of model simulations but also can be extended for large-dimension cases by imposing a covariance function on its correlation structure. The proposed scheme is applied to the daily precipitation data generated by a regional climate model. We can verify that the biases are satisfactorily corrected by examining several statistics of the corrected data.

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