# Taylor Diagram SoftwareOpen nc file by using the list of coordinates

Taylor Diagram Software is used in these papers:

## What is Taylor Diagram software tool?

When you want to check the quality of various models vs. observation data, with different statistical tests, you should first compute the statistical analyses between the observation data and future models’ data, then check each of the results and decide that one of the applied models has the better result in compare to the observation data. This process is not easy when you have several models under various scenarios and for multiple parameters simultaneously. In this situation, the best method to achieve the best results in a short time will be the Taylor diagram software tool.

In this regard, Agrimetsoft searched to find software for Taylor diagram and found out there are programming languages as NCL (Ncar Command Language / Taylor diagram NCL), MATLAB (MATLAB Taylor diagram), Python (python Taylor diagram), and various R packages (R Taylor diagram) to draw the Taylor diagram. As you can see, there are different ways to draw Taylor diagram, but if you didn’t work with coding in these software, it would be a tedious challenge! Consequently, Agrimetsoft has developed the Taylor diagram software for facilitating the mentioned process for scholars and scientists.

Taylor diagram software is an appropriate and unique tool with a user-friendly environment that can assist you to depict the Taylor diagram easily and quickly. By running Taylor diagram software, you can draw the Taylor diagram with several models and input variables, save your time, have a high-quality Taylor diagram in many fig’s formats. If you want to use this tool, before order it, see the help file and YouTube video for learning different running steps.

### What is Taylor Diagram?

Taylor diagrams are especially useful in evaluating multiple aspects of complex models or in gauging the relative skill of many different models (e.g., IPCC, 2001). Taylor diagrams can be used to graphically summaries how close a set of patterns (in this case, a collection of models) match observations. This is achieved by plotting the standard deviation of the model values' time series and the correlation between the time series of the model values and the observations. Taylor also noted the geometric connection between correlation, standard deviation, and the central pattern RMS difference, and found that all three could be plotted simultaneously.

Taylor (2001) introduced a single diagram to summarize multiple aspects, including RMSE and correlation coefficient information, in the evaluation of model performance. Taylor diagrams provide a visual framework for comparing a suite of variables from one or more test data sets to one or more reference data sets. Commonly, the test data sets are model experiments, while the reference data set is a control experiment or some reference observations. Generally, the plotted values are derived from climatological monthly, seasonal, or annual means. Because the different variables (e.g., wind, humidity, precipitation, temperature) may have widely varying numerical values, the results are normalized by the reference variables. The ratio of the normalized variances indicates the relative amplitude of the model and observed variations.

Taylor diagram combines various statistical indicators for multiple models on a single quadrant: The correlation coefficient values, the x-axis, the y-axis, delimit arcs with standard deviation values, and the internal semi-circles correspond to the RMSE values. The statistical indicators show the performance of the individual models in comparison with the observations. The correlation coefficient should be as close to one as possible. RMSE should be as small as possible, and the standard deviation of the model results should be as close as possible to the standard deviation of the observations.

The RMSE measures the differences between values predicted by a model or estimator and the values observed. It is a good measure of accuracy. Meanwhile, the correlation coefficient is a measure of the correlation (linear dependence) between two variables. It is widely used as a measure of the strength of the linear relationship (or pattern similarity) between two variables. A further advantage of the Taylor diagram is that different parameters can be plotted together by normalizing (i.e., making dimensionless) their standard deviations. This is the second type of Taylor diagram and as a recommended type.

### How many types of Taylor Diagram are there in general?

Generally, in different papers and software packages, you can see that there are two types of Taylor diagram, namely, the simple type, and the second type is the classic type/modified Taylor diagrams, as we have mentioned in this document as the recommended way (classic type or modified Taylor diagrams) of drawing the Taylor diagram.

### Which kind of Taylor Diagram is available in Taylor Diagram software?

In Taylor diagram software, Agrimetsoft has embedded two types of Taylor diagram, namely the simple type (Taylor, 2001 and 2005), and the classic type (normalized standard deviations) (Elvidge, 2014 and NCL package). ### How can we compute Taylor Diagram?

In general, the Taylor diagram characterizes the statistical relationship between two fields, a "test" field (often representing a field simulated by a model) and a "reference" field (usually representing "truth," based on observations). Taylor (2005) has revealed that the reason that each point in the two-dimensional space of the Taylor diagram can show three different statistics simultaneously (i.e., the centered RMS difference, the correlation, and the standard deviation) is that the following formula relates these statistics, as the simple type of Taylor diagram: where R is the correlation coefficient between the test and reference fields, E'2 is the centered RMS difference between the fields, and σf2 and σr2 are the variances of the test and reference fields, respectively. The construction of the diagram (with the correlation given by the cosine of the azimuthal angle) is based on the similarity of the above equation and the Law of Cosines: There are several minor variations on the diagram that have been found useful for various purposes, so we invite you to read the paper of Taylor (2001).

References:

• IPCC, 2001: Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881 pp. (see this)
• Elvidge, S., Angling, M.J., et al., 2014. On the use of modified Taylor diagrams to compare ionospheric assimilation models, 978-1-4673-5225-3/14/\$31.00, 2014 IEEE.
• Taylor KE. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research 106: 7183–7192.
• Taylor Diagram Primer, 2005, Karl E. Taylor. January 2005

The license of this tool is applicable for one year of using and you can renew it by pay 20% of the price for the new year.

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