Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

By Patrick Laux, Stefanie Vogl, W. Qiu, H. Knoche, Harald Kunstmann. 2011

130 citations recorded by the source corpus.

Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d8680ad14ec7d14dc6cdb44b71c4e522fb42314a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. G. Mao
2. Harald Kunstmann
3. Patrick Laux
4. Lihua Xiong
5. Stefanie Vogl
6. Chong-yu Xu
7. Shenglian Guo

In Global and Planetary Change

1. Chong-yu Xu
2. Shenglian Guo
3. Lihua Xiong
4. Pinggen Zhou
5. G. Mao
6. W. Qiu
7. Zhiyong Liu
8. Harald Kunstmann

Citations in the corpus

• Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields (2012, methodology): “RecentlyLaux et al.(2011) showed that daily precipitation data incorporate autocorrelation and heteroskedasticity and therefore are not “iid”.”
• A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, background): “Copulas can join multiple random variables with diverse correlations and dependence structures, regardless of their margins [Laux et al., 2011].”
• Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data (2014, unknown): “This study is an extension of the two former studies of Laux et al. (2011) and Vogl et al. (2012) by applying the Copula-based bias correction technique to high-resolution RCM precipitation output and a gridded observation product.”
• Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula (2015, methodology): “, 2007) has been widely applied (De Michele and Salvadori, 2003; Favre et al., 2004; Salvadori and De Michele, 2004a; Grimaldi and Serinaldi, 2006; Zhang and Singh, 2006; Zhang and Singh, 2007; Evin and Favre, 2008; Kao and Govindaraju, 2008; Bárdossy and Pegram, 2009; Serinaldi et al., 2009; Wang e”