SkillMetrics 1.1.6

A package for calculating skill of model predictions against observations

This package contains a collection of functions for calculating the skill of model predictions against observations. It includes metrics such as root-mean-square-error (RMSE) difference, centered root-mean-square (RMS) difference, and skill score (SS), as well as a collection of functions for producing target and Taylor diagrams. The more valuable feature of the package are the plotting functions for target and Taylor diagrams and the ability to easily customize the diagrams.

Features

• Statistical metrics such as root-mean-square-error (RMSE) difference, centered root-mean-square (RMS) difference, and skill score (SS)

• Functions to calculate statistical metrics for target & Taylor diagrams

• Target Diagrams

• Taylor Diagrams

• Options to control plot features such as color of labels and lines, width of lines, choice of markers, etc.

• Output of graphics to PNG format.

Installing

To install the package simply use the pip command:

$ pip install SkillMetrics

If you are upgrading the package then include the upgrade option:

$ pip install SkillMetrics --upgrade

Examples

A primer on Taylor diagrams is provided as well as a 6-page description of target and Taylor diagrams as visual tools to aid in the analysis of model predictive skill. The figures used in the latter were generated with the SkillMetrics package. TThere is also an “Examples” folder that contains a collection of example Python scripts showing how to produce target and Taylor diagrams in a variety of formats via the GitHub Wiki at https://github.com/PeterRochford/SkillMetrics/wiki. There are 7 examples for target diagrams and 11 examples for Taylor diagrams that successively progress from very simple to more customized figures. These series of examples provide an easy tutorial on how to use the various options of the target_diagram and taylor_diagram functions. They also provide a quick reference in future for how to produce the diagrams with specific features. The diagrams produced by each script are in Portable Network Graphics (PNG) format and have the same file name as the script with a “png” suffix. Examples of the diagrams produced can be found on the Wiki.

There is also a simple program “all_stats.py” available via the Wiki that provides examples of how to calculate the various skill metrics used or available in the package. All the calculated skill metrics are written to a spreadsheet file for easy viewing and manipulation: Excel for a Windows operating system, Comma Separated Value (CSV) for a Macintosh operating system (MacOS). The Python code is kept to a minimum.

Available Metrics

Here is a list of currently supported metrics. Examples of how to obtain them can be found in the “all_stats.py” program.

Metric	Description
bias	Mean error
BS	Brier score
BSS	Brier skill score
r	Correlation coefficient
CRMSD	centered root-mean-square error deviation
NSE	Nash-Sutcliffe efficiency
RMSD	root-mean-square error deviation
SS	Murphy’s skill score
SDEV	standard deviation