COMPARING BIAS CORRECTION METHODS TO IMPROVE MODELLED PRECIPITATION EXTREMES

Yeli Sarvina, Thomas Pluntke, Christian Bernhofer

Abstract


This study aims to analyze and improve modelled extreme precipitation. It was conducted in the German Federal State of Saxony using the WEREX V data set. WEREX V is a model that statistically downscales Global Circulation Model (GCM) data. Inputs for the WEREX V model included GCMs ECHAM 5, HadCM3C and HadGEM2 (sometimes downscaled with Regional Climate Models RCMs REMO, RACMO and CCLM), SRES scenarios A1B and E1, and different model runs. The output of analysis was shown by a boxplot since the WEREX V data set has 120 future projections of precipitation. The model results were verified against observed data obtained from representative meteorological stations, and systematical deviations or biases were identified. To improve the model results, two bias correction methods were applied with special emphasis given to the reproduction of precipitation extremes. Empirical quantile mapping and gamma quantile mapping methods were applied. The ability of the WEREX V ensemble to capture extreme precipitation values varied; this was described in terms of biases. All of the identified correction methods were capable of reducing the bias related to the intensity of extreme precipitation occurrence during the calibration period. The performance of empirical quantile mapping is better than gamma quantile mapping to reduce biases (median value) and uncertainty (inter quartile range value )

Keywords


Bias correction methods, extreme precipitation, statistical downscaling, WEREX V data set.

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DOI: http://dx.doi.org/10.31172/jmg.v19i2.449

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