Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

By Douglas Maraun. 2013

322 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/d4e9a6428bc768c481d64aad56cdb6f552e7515e

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

In Global and Planetary Change

In Oceanography

Citations in the corpus

Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes (2016, unknown): “In this study, we focus specifically on the performance of bias correction and not issues related to downscaling/upscaling (Maraun, 2013).”
Cross-validation of bias-corrected climate simulations is misleading (2018, unknown): “For reviews of bias correction see Teutschbein and Seibert (2012), Maraun (2016) and the book by Maraun and Widmann (2018). The performance of a bias correction is typically evaluated against independent observational data, which have not entered the calibration of the correction function. For insta”
A new high‐resolution Meteorological Reanalysis Italian Dataset: MERIDA (2019, methodology): “To work around this problem, the prosecution of the activity will focus on the implementation of different statistical methods, such as the Empirical Quantile Mapping (Maraun, 2013) or the Analog Ensemble (Keller et al.”
Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes (2014, methodology): “2010), if used for downscaling below gridbox scale may consequently produce wrong variability and trends (Maraun 2013).”
Multi-variable error correction of regional climate models (2013, background): “Maraun (2013) demonstrated that spatial and temporal variability show considerable deficiencies after applying QM compared to observations.”
Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0) (2019, unknown): “These issues can be overcome by spatially multivariate bias adjustment or, as suggested by Maraun (2013), using a statistical downscaling method which is able to add the spatiotemporal variability that is missing at the simulation data resolution.”
Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, unknown): “Consistent with results from the synthetic example in section 4 and Maraun (2013) and Maurer and Pierce (2014), we ﬁnd that the largest QM outliers tend to occur at grid cells where the raw GCM underestimates, relative to observations, the variance of the ﬁtted GEV distribution in the historical…”
The effect of univariate bias adjustment on multivariate hazard estimates (2019, methodology): “Although the effectiveness of bias adjustment is typically evaluated outside the calibration period (Maraun, 2013), here we focus our analysis completely on the selected 15-year period.”