Preprints
https://doi.org/10.5194/egusphere-2022-282
https://doi.org/10.5194/egusphere-2022-282
 
06 May 2022
06 May 2022
Status: this preprint is open for discussion.

CCdownscaling: an open-source Python package for multivariable statistical climate model downscaling V1.0 

Andrew D. Polasky1, Jenni L. Evans1,2, and Jose D. Fuentes1 Andrew D. Polasky et al.
  • 1Department of Meteorology and Atmospheric Science, The Pennsylvania State University
  • 2Institute for Computational and Data Sciences, The Pennsylvania State University, University Park

Abstract. Statistical downscaling methods provide an essential bridge between low resolution global climate models and localized information needed by decision makers. As the demand for localized climate information continues to grow to make projections for a wide variety of applications, the need for software that can provide this sort of downscaled data grows with it. The CCdownscaling package described in the article provides a number of downscaling methods, including Self Organizing Maps, as well as a number of evaluation metrics for assessing downscale model skill. In this article, we describe the features of the CCdownscaling package, and show an example use case for downscaling temperature and precipitation. It is open-source and freely available for use in generating downscaled projections.

Andrew D. Polasky et al.

Status: open (until 01 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Andrew D. Polasky et al.

Data sets

CCdownscaling example use case data - O'Hare airport Andrew Polasky https://zenodo.org/record/6506677

Model code and software

CCdownscaling v1.0 Andrew Polasky https://zenodo.org/record/6506660

Andrew D. Polasky et al.

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Short summary
Statistical downscaling provides methods to bridge the gap between the global climate models and the scale of information needed to understand the impacts of climate change. This paper describes a new software package that provides a number of statistical downscaling approaches, as well as evaluation metrics for these methods. The goal of this work is to provide a new tool for researchers carrying out downscaling studies, and enable the easy use and comparison of different downscaling methods.