A combined statistical bias correction and stochastic downscaling method for precipitation

Volosciuk, Claudia; Maraun, Douglas; Vrac, Mathieu; Widmann, Martin

doi:https://doi.org/10.5194/hess-21-1693-2017

Articles | Volume 21, issue 3

https://doi.org/10.5194/hess-21-1693-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-21-1693-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 3

Research article

|

22 Mar 2017

Research article |

| 22 Mar 2017

A combined statistical bias correction and stochastic downscaling method for precipitation

Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann

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Final revised paper (published on 22 Mar 2017)
Preprint (discussion started on 15 Sep 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'An excellent paper that could however be improved', Sven Kotlarski, 09 Oct 2016
RC2: 'Innovative separation of bias correction and downscaling', Stefan Hagemann, 14 Oct 2016
RC3: 'A well-explained method that requires more discussion', Anonymous Referee #3, 17 Oct 2016
AC1: 'Responses to reviewer comments - we agree with most suggestions and will implement them in a revised version', Claudia Volosciuk, 09 Dec 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to revisions (further review by Editor and Referees) (09 Jan 2017) by Luis Samaniego

AR by Claudia Volosciuk on behalf of the Authors (20 Feb 2017) Author's response Manuscript

ED: Publish subject to minor revisions (further review by Editor) (21 Feb 2017) by Luis Samaniego

I am satisfied with the detailed revision and further clarifications. In my opinion the manuscript can be published after a minor revision. There are a number of technical corrections:

- Center the legend in Fig. 3, put on the right.
- Fig 4 move legend to the right.
- Fig 5 is too small. Make a single legend for all panels. Add labels (a)... and refer in caption.
- Fig 6. Increase separation between panels. Difficult to read. Too small for me.
- Fig 7, 8 Add Labels (a)..., make a single legend.
- Fig. 9,10 Add Labels (a)..., make a single legend. Increase separations. Indicate the quantiles on red lines 0.1...(at least in one.)
- Fig. 11 too small. legends to the right. it is not usual to have a legend with integers 0, 1, 2, ..., 32 and reals with three decimals! Please change both.
- Fig 12 too small, increase separation between panels. Vertical axis has no legend.
- Fig 13,14 same remarks as in Fig 7
- Fig 15,16 Font too small
- Fig 17 dots are too small . make single legend. add labels (a)... include description in caption.
- Fig 18 far too small. Can't read.

Fonts of all figures are too small in general. I consider that this manuscript has far too many figures. Many are quite similar. I suggest to condense and select the most important ones and put the others in an appendix.

Regarding the method:

In Thober et al (WRR, 2014) 10.1002/2014WR015930, we present a method to downscale RCM/NWP precipitation from monthly to daily values with a temporal cascade and ensuring the crosscorrelation in space. Bárdossy and Pegram, 2009 suggested that copula based methods can be used for the spatial downscaling. The mix of both may the optimal for a spatio-temporal downscaling of precipitation. None of these works have been even mentioned, and many of the references there in. I wonder how thoroughly has been done the literature review in this study. I suggest to put these works in contrast to the approach presented.

LS

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AR by Claudia Volosciuk on behalf of the Authors (03 Mar 2017) Author's response Manuscript

ED: Publish as is (06 Mar 2017) by Luis Samaniego

AR by Claudia Volosciuk on behalf of the Authors (06 Mar 2017)

Short summary

For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.