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Special Topics: Taylor Diagrams

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 (eg, ECMWF Reanalyses). Generally, the plotted values are derived from climatological monthly, seasonal or annual means. Because the different variables (eg: 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.

For the classic Taylor Diagram (Karl, 2005), the pertinent statistics are the weighted centered pattern correlation(s) (pattern_cor) and the ratio(s) of the normalized root-mean-square (RMS) differences between 'test' dataset(s) and 'reference' dataset(s). An additional bias statistic, developed by R. Neale (NCAR), may added to the classic Taylor Diagram. See taylor_7b below.

NCL 6.5.0 will contain a function, taylor_stats, which will create the statistics needed for the taylor diagram: pattern_correlation, ratio and bias. Optionally, additional statistics can be returned. Prior to the release of NCL 6.5.0, the taylor_stats function may be downloaded from here.


Taylor, K.E. (2005):  Taylor Diagram Primer:  
A brief 4-page overview which summarizes the important aspects of these useful plots. 

Taylor, K.E. (2001): 
Summarizing multiple aspects of model performance in a single diagram
JGR, vol 106, no. D7, 7183-7192, April 16, 2001. 

Gleckler, P. J., K. E. Taylor, and C. Doutriaux (2008): 
Performance metrics for climate models 
J. Geophys. Res., 113, D06104

Baker, N.C., Taylor, P.C (2016): 
A Framework for Evaluating Climate Model Performance Metrics 
Journal of Climate, 2016, 29, 5, 1773.

The following examples use taylor_diagram.ncl or taylor_diagram_cam.ncl to generate the background upon which the normalized statistics are plotted. The advantage of using the normalized version of the Taylor diagram is that variables with widely varying variances can be viewed on one figure.

The classic taylor_diagram function is prototyped as follows:

    function taylor_diagram ( wks:graphic\    ; pre-created workstation
                   , RATIO[*][*]:numeric \    ; ratios
                   , CC[*][*]:numeric    \    ; pattern correlations: range 0-1
                   , rOpts:logical)           ; scalar to which attributes are assigned
The version that includes the bias is prototyped as:

    function taylor_diagram_cam ( wks:graphic\; pre-created workstation
                   , RATIO[*][*]:numeric \    ; ratios
                   , CC[*][*]:numeric    \    ; pattern correlations: range 0-1
                   , BIAS[*][*]:numeric  \    ; rlative bias (%)
                   , rOpts:logical)           ; scalar to which attributes are assigned

The arguments are:

  • RATIO[*][*]: ratio of the standardized variances
  • CC[*][*]: pattern correlations: range 0-to-1
  • BIAS[*][*]: relative bias (%): See taylor_8
  • rOpts: options

The RATIO, CC and BIAS arguments are two dimensional. The left dimension refers to the number of test data sets used (eg: model experiments and/or observational). The right dimension holds the actual values to be plotted. If only one comparison dataset is used, the ratio, cc and bias are scalars. to plot, the user must create a two dimensional array prior to calling the function. EG:

    CC    = conform_dims( (/1,1/), cc)
    RATIO = conform_dims( (/1,1/), ratio)
    BIAS  = conform_dims( (/1,1/), bias)

The procedure taylor_metrics_table.ncl can be used to create a table containing a specified statistic. See taylor_{7,8}.

    procedure taylor_metrics_table (mfname[1]:string \ ; plot name
                   ,varNames[*]:string      \   ; variable names
                   ,cases[*]:string         \   ; case (model) names
                   ,seasons[*]:string       \   ; season names
                   ,values[*][*][*]:numeric \   ; 3d array w values
                   ,topt:logical            )   ; table options

By default, the taylor diagram functions can handle up to 10 variable comparisons. Different colors and markers are used to differentiate between different models/cases. The 10 default colors and markers are:

      Markers = (/ 4, 6, 8,  0, 9, 12, 7, 2, 11, 16/) ; Marker Indices 
      Colors  = (/ "red"      , "blue" , "green" , "cyan"  , "orange" \   
                 , "turquoise", "brown", "yellow", "purple", "black" /)  
The markers and colors can readily be changed by the user.

The taylor_diagram plot options are activated by setting the option argument, (say) "opt", to True and setting various attributes. The user specified attribute options include:

      opt   = True                      ; taylor diagram with options 
      opt@tiMainString  = "......"      ; title 
      opt@Markers       = (/  ...  /)   ; markers   
      opt@Colors        = (/  ...  /)   ; colors
      opt@caseLabels    = (/  ...  /)   ; case Labels 
      opt@varLabels     = (/  ...  /)   ; variable Labels 

      opt@caseLabelsFontHeightF =       ; caseLabels size [default=0.12  ]   
      opt@varLabelsFontHeightF  =       ; varLabels  size [default=0.013 ]
      opt@varLabelsYloc =               ; Move location of variable labels 
                                        ; [default=0.45]
      opt@gsMarkerSizeF =               ; marker size     [default=0.0085]

                                        ; BACKGROUND options
      opt@stnRad        = (/  ...  /)   ; additional standard radii
      opt@ccRays        = (/  ...  /)   ; correlation rays 
      opt@centerDiffRMS = True          ; RMS 'circles'                                            
      opt@ccRays_color  = "LightGray"          ; default is black
      opt@centerDiffRMS_color  = "LightGray"   ; default is black

                                        ; OTHER recognized options
      opt@taylorFrame   = False         ; do not advance frame [default is True]
The following examples illustrate the most commonly used options.

The taylor_metrics_table options are activated by setting the option argument, (say) opt=True and setting various attributes. The user specified attribute options include:

      opt   = True                      ; taylor metric table with options 
      opt@tiMainString  = "......"      ; title [default="CAM METRICS"]
                                        ; make roughly the same length 
taylor_1.ncl: Default classic Taylor diagram.
taylor_2.ncl: Set background options.
taylor_3.ncl: Add labeling to the default Taylor background.
taylor_4.ncl: Create a taylor plot and a table displaying the CAM metrics.
taylor_5.ncl: Create sample tables displaying the CAM metrics.
taylor_6.ncl: Requires NCL 4.2.0.a034 or newer: This demonstration script illustrates paneling multiple Taylor plots. Obviously, paneling figures can make the features small. To make the figures less 'busy' the opt@varLabels and opt@caseLabels could be deleted after the first plot.

The panel's component figures need not have the same number of variables or cases. Each figure is independent of the others.
taylor_7.ncl: A simple processing script that compares a single test model versus a reference (control) case. The 'classic' Taylor diagram quantities (pattern correlations and ratios) were calculated using taylor_stats. The taylor_diagram.ncl plotting function is used. For convenience, The annual [ANN] and seasonal [eg: JJA, DJF] quantities are derived from the monthly climatologies.
taylor_7b.ncl: Similar to the taylor_7 example except the relative bias is plotted. The pertinent statistics are calculated using taylor_stats. This requires using the taylor_diagram_cam.ncl plotting function.
taylor_8.ncl: The following example uses taylor_diagram_cam.ncl to plot the percent bias of each case for each variable (nv). Further, it plots negative correlations and standard deviations > 1.65 as text at the bottom of the figure.

If mean_case represents the weighted areal mean for each case (nc) and each variable (nv) and mean_ref(nv) is the areal mean of the reference case variable, then the percent bias is calculated via

   bias = new(nCase,nVar), "double", 1d20)
   . . .
   bias(nc,nv)  = mean_case(nc,nv) - mean_ref(nv)              
   if (mean_ref(nv).ne.0) then
       bias(nc,nv) = (bias(nc,nv)/mean_ref(nv))*100    ; bias [%]
   end if
The percent bias is indicated by different symbols. The taylor_diagram_cam.ncl also handles negative pattern correlation coefficients by adding text information.

Erik_taylor_panel_Example_1.ncl and Erik_taylor_panel_Example_2.ncl: The following two taylor diagram panel examples were contributed by Erik Noble of NASA. This has no support from the NCL developers.

They are mostly identical, except the second one adds additional labels to the diagram using gsn_add_text.

They differ from the above examples in that they do not use labels and show how one could use the plot for many models. Erik is using it to look at 64 model results at once.

These scripts require that you load taylor_diagram_enoble.ncl.

The following was contributed by Jatin.Kala.JK *AT* This has no support from the NCL developers.

"The following is a slightly modified taylor_diagram function, with less hard-coding of axis limits etc. Also, I have passed the very many variables to local (would be nice if someone double-checked). The drawing of RMS "semi-circles" is automated provided there is enough space for a "full semi-circle". I need to figure out a nice way to make the RMS circles end nicely on the "outer arc" where is there not enough space, but I have no time, and maybe someone else can automate that? There are ample comments of my changes at the start of the script."

For more info, and any improvements or bugs, please contact Jatin Kala (Jatin.Kala.JK *AT*

The script is taylor_diagram_less_hardcoded.ncl.