Preprints
https://doi.org/10.5194/gmd-2022-298
https://doi.org/10.5194/gmd-2022-298
Submitted as: model description paper
 | 
17 May 2023
Submitted as: model description paper |  | 17 May 2023
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping

Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty

Abstract. Snow cover plays a crucial role in regional climate systems worldwide. It is a key variable in the context of climate change because of its direct feedback to the climate system, while at the same time being very sensitive to climate change. Accurate long-term spatial data on snow cover are scarce, due to the lack of satellite data or forcing data to run land surface models back in time. This study presents an R package, SnowQM, designed to correct for the bias in long-term spatial snow water equivalent data, using more accurate data for calibrating the correction. The correction is based on the widely applied quantile mapping approach. A new method of spatial and temporal clustering of the data points is used to calculate the quantile distributions. The main functions of the package are written in C++ to achieve high performance and to allow parallel computing. In a case study over Switzerland, where a 60-year snow water equivalent climatology is produced at a resolution of 1 day and 1 km, SnowQM reduces the bias in snow water equivalent from −9 mm to −2 mm in winter and from −41 mm to −2 mm in spring. It is also significantly faster than pure R implementations. The limitations of the quantile mapping approach for snow, such as snow creation, are discussed. The proposed spatial clustering improves the correction in homogeneous terrain, which opens the way for further use with other variables.

Adrien Michel et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-298', Michael Matiu, 27 May 2023
  • RC2: 'Comment on gmd-2022-298', Marianne Cowherd, 22 Jul 2023
  • AC1: 'Comment on gmd-2022-298', Adrien Michel, 05 Dec 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-298', Michael Matiu, 27 May 2023
  • RC2: 'Comment on gmd-2022-298', Marianne Cowherd, 22 Jul 2023
  • AC1: 'Comment on gmd-2022-298', Adrien Michel, 05 Dec 2023

Adrien Michel et al.

Data sets

SnowQM data Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty https://zenodo.org/record/7886773

SnowQM data Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty https://zenodo.org/record/7886773

Model code and software

SnowQM source code Adrien Michel https://zenodo.org/record/7886675

SnowQM source code Adrien Michel https://zenodo.org/records/10257951

Adrien Michel et al.

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Latest update: 12 Jan 2024
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Short summary
We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better quality maps. The correction can then be extended backwards and forwards in time for periods when better quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the last 60 years at a resolution of one day and one kilometre. This is the first time that such a dataset has been produced.