A Taylor Diagram is a graphical representation used to evaluate how well models reproduce the observed data. It displays three statistical measures: correlation coefficient, root mean square deviation (RMSD), and the standard deviation of both observed and model data. Here’s a basic guide on how to create and interpret a Taylor Diagram.
This repository contains the code for creating Taylor Diagrams to visualize the performance of various models based on multiple evaluation metrics. The Taylor Diagrams are generated from a CSV file containing the observed values and model predictions.
Taylor Diagrams provide a concise way of graphically summarizing how well patterns match each other in terms of their correlation, their centered root mean square difference, and the amplitude of their variations (represented by their standard deviations). This makes it easier to compare the relative performance of different models.
For more details, visit the GitHub repository.