Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 16, issue 9 | Copyright
Hydrol. Earth Syst. Sci., 16, 3383-3390, 2012
https://doi.org/10.5194/hess-16-3383-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Technical note 21 Sep 2012

Technical note | 21 Sep 2012

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

L. Gudmundsson1,*, J. B. Bremnes1, J. E. Haugen1, and T. Engen-Skaugen1 L. Gudmundsson et al.
  • 1The Norwegian Meteorological Institute, Oslo, Norway
  • *now at: Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland

Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for post-processing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation.

Publications Copernicus
Download
Citation
Share