Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes
By M. Switanek, Peter Troch, C. Castro, A. Leuprecht, Hsin-I Chang, R. Mukherjee, E. Demaria. 2016
219 citations recorded by the source corpus.
Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.
Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/978da03887f62f828e737b9c77406998bee95e74
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In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin (2017, unknown): “Correction schemes for rainfall estimates are developed for climate models (Maraun, 2016; Grillakis et al., 2017; Switanek et al., 2017), for radar approaches (Cecinati et al.”
- Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0) (2019, unknown): “The parametric quantile mapping method used in ISIMIP3 is inspired by the scaled distribution mapping method introduced by Switanek et al. (2017), which in addition to biases in quantiles also adjusts biases in the likelihood of individual events.”
- The effect of univariate bias adjustment on multivariate hazard estimates (2019, background): “Furthermore, cross-validation might not help to diagnose whether a bias-adjustment approach is effective (Switanek et al., 2017; Maraun and Widmann, 2018).”