Abstract
<span><em>MurCSS</em> </span> (Murphy-Epstein decomposition and Continuous Ranked Probability Skill Score) is a tool for standardized evaluation of decadal hindcast-prediction systems written in Python using CDO [1] and can be downloaded at <a href="https://github.com/illing2005/murcss">https://github.com/illing2005/murcss</a>. It analyzes decadal hindcast experiments in a deterministic and probabilistic way following and extending the framework suggested by Goddard et al. [2]. It was developed as part of the MiKlip (a major project for decadal climate prediction funded by BMBF in Germany) evaluation system to improve the comparability within the project during development stages and interim test phases. It is easily applicable by other modeling groups working on decadal prediction because it complies with international standards.
Highlights
MurCSS (Murphy-Epstein decomposition and Continuous Ranked Probability Skill Score) is a tool for standardized evaluation of decadal hindcast-prediction systems written in Python using CDO [1] and can be downloaded at https://github.com/illing2005/murcss
It analyzes decadal hindcast experiments in a deterministic and probabilistic way following and extending the framework suggested by Goddard et al [2]. It was developed as part of the MiKlip evaluation system to improve the comparability within the project during development stages and interim test phases. It is applicable by other modeling groups working on decadal prediction because it complies with international standards
Model development stages and interim test phases of can be assessed. It simplifies the comparison of decadal prediction systems developed by different modeling groups
Summary
MurCSS (Murphy-Epstein decomposition and Continuous Ranked Probability Skill Score) is a tool for standardized evaluation of decadal hindcast-prediction systems written in Python using CDO [1] and can be downloaded at https://github.com/illing2005/murcss. It analyzes decadal hindcast experiments in a deterministic and probabilistic way following and extending the framework suggested by Goddard et al [2]. It is possible using the simplified file input component (findFilesCustom.py) which
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