===========================================
On 3/23/14, 5:27 PM, Dennis Shea wrote:
============================================
At the top of the Taylor Diagram WWW page
http://www.ncl.ucar.edu/Applications/taylor.shtml
A link is provides to Karl Taylor's "Taylor Diagram Primer"
http://www-pcmdi.llnl.gov/about/staff/Taylor/CV/Taylor_diagram_primer.pdf
It is an 'easy read.' This summarizes the important aspects of these
useful plots.
====
The top of the NCL Taylor Diagram WWW page, it states:
"The pertinent statistics are the weighted pattern
correlation (pattern_cor) and the normalized
root-mean-square (RMS) differences."
---
If u have the above quantities, it is simple to plot.
EG: Look at Example 1.
--
xc(nlat,mlon), xo(nlat,mlon)
(1) You need centered pattern correlations. NCL provides
a function for this computation:
http://www.ncl.ucar.edu/Document/Functions/Contributed/pattern_cor.shtml
(a) if the variables are on different grids, you *must* interpolate
to a common grid. Generally, the 'common grid' is
the control grid. Then calculate the pattern correlation.
---
[2] You need the area weighted centered variances. Not necessary but
for consistency, they should use the same grid. The following
assumes that xo and xc are on the same grids. Taylor states:
"... the centered root-mean-square (RMS) difference between
the simulated and observed patterns ..."
(a) Compute the weighted area means for each grid
; areal mean: rectilinear grid
xavgc = wgt_areaave(xc,...,gw,..) ; control
xavgo = wgt_areaave(xo,...,gw,..) ; 'other' dataset
; gw could be cos(lat)
or
; areal mean: curvilinear grid
xavgc = wgt_areaave2(xc,..,w2,..) ; control
xavgo = wgt_areaave2(xo,..,w2,.) ; 'other' dataset
; w2 could be cos(lat2d)
or, more explicitly, for xc and xo on the same grid: wgtc=wgto
wgtc = conform_dims(dimsizes(xc), gw, 0) ; make 2d for gw[*]
xavgc = sum(wgtc*xc)/sum(wgtc) ; control centered mean
xavgo = sum(wgtc*xo)/sum(wgtc)
(b) compute the sum of the centered area weighted variances.
dc2 = sum(wgtc*(xc-xavgc)^2) ; control ; centered about xavgc
do2 = sum(wgtc*(xo-xavgo)^2)
rat = sqrt(do2/dc2)
On 3/30/14, 7:36 PM, louis Vonder wrote:
> Hi Guilherme,
>
> Did you have any news information on the construction of Taylor Diagram?
>
> Regards,
>
> Louis
>
>
> --------------------------------------------
> En date de : Jeu 20.3.14, Guilherme Martins <jgmsantos@gmail.com> a écrit :
>
> Objet: Re: [ncl-talk] Clarification on Taylor diagram
> À: "louis Vonder" <appopson@yahoo.fr>
> Date: Jeudi 20 mars 2014, 18h24
>
> Hi
> Louis,
> My name is
> Guilherme. I have the same question how construct the tayor
> diagram. How is in the website to me is very complicated,
> it's not clear. Did anyone give you any sugestion?
>
>
> Thanks,
> Guilherme.
>
> 2014-03-18 3:58 GMT-03:00
> louis Vonder <appopson@yahoo.fr>:
>
>
> Dear
> members of the NCL community,
>
>
>
> Here a script than I am trying to use to compare
> datasets.
>
> It is a Taylor diagram snipped from ncl example page.
>
>
>
> I want know there is no mistake.
>
> Particularly where I am computing pattern correlation and
> standard deviation normalization.
>
>
>
>
>
> Regards,
>
>
>
>
>
>
>
>
>
> ;**********************************
>
> ; DIAGRAMME DE TAYLOR
>
> ;**********************************
>
> load
> "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"
>
> load
> "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"
>
> load
> "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
>
> load
> "$NCARG_ROOT/lib/ncarg/nclscripts/csm/shea_util.ncl"
>
> load "taylor_diagram.ncl"
>
> ;load "taylor_metrics_table.ncl"
>
> ;**********************************
>
> ;**********************************
>
> begin
>
>
>
> ;************ data reading
> ***************************************
>
>
>
>
>
>
>
> data0 = addfile("reg_cru_pre.nc",
> "r") ; CRU
>
> data1 = addfile("pregpcp1998_2007_inter.daily.nc",
> "r") ;
>
> data2 = addfile("CMAP_precip.mon.mean_inter_1998_2007.nc",
> "r")
>
> data3 = addfile("precip.mon.total.v301_inter_1998_2007.nc",
> "r")
>
> data4 = addfile("precip.mon.total.v6_inter_1998_2007.nc",
> "r")
>
>
>
>
>
> pre0 = data0->pre
>
> pre1 = data1->data
>
> pre2 = data2->precip
>
> pre3 = 10*data3->precip
>
> pre4 = data4->precip
>
>
>
>
>
> copy_VarAtts(data3->precip, pre3)
>
>
>
>
>
> ;******************Pattern correlation
> ************************************
>
> cor1 = dim_avg_n_Wrap(pattern_cor( pre0, pre1,
> 1.0, 0), 0)
>
> cor2 = dim_avg_n_Wrap(pattern_cor( pre0, pre2,
> 1.0, 0), 0)
>
> cor3 = dim_avg_n_Wrap(pattern_cor( pre0, pre3,
> 1.0, 0), 0)
>
> cor4 = dim_avg_n_Wrap(pattern_cor( pre0, pre4,
> 1.0, 0), 0)
>
>
>
> mmd= (/cor1, cor2, cor3, cor4/)
>
> print(mmd)
>
> ;****************** Standard deviation
> **************************
>
>
>
> pre0_Std = dim_avg_n_Wrap( dim_stddev_n_Wrap( pre0,
> (/1,2/)), 0)
>
> pre1_Std = dim_avg_n_Wrap( dim_stddev_n_Wrap( pre1,
> (/1,2/)), 0)
>
> pre2_Std = dim_avg_n_Wrap( dim_stddev_n_Wrap( pre2,
> (/1,2/)), 0)
>
> pre3_Std = dim_avg_n_Wrap( dim_stddev_n_Wrap( pre3,
> (/1,2/)), 0)
>
> pre4_Std = dim_avg_n_Wrap( dim_stddev_n_Wrap( pre4,
> (/1,2/)), 0)
>
>
>
> print(dimsizes(dim_stddev_n_Wrap( pre4, (/1,2/))))
>
>
>
> ;**************** Standard deviations normalisation
> *********************
>
>
>
> std1 = pre1_Std/pre0_Std
>
> std2 = pre2_Std/pre0_Std
>
> std3 = pre3_Std/pre0_Std
>
> std4 = pre4_Std/pre0_Std
>
>
>
> fud = (/std1, std2, std3, std4/)
>
>
>
> ; Cases [Model]
>
> case = (/ "precipitation" /)
>
> nCase = dimsizes(case )
>
> ; variables compared
>
> var = (/ "GPCP", "CMAP",
> "UDEL", "GPCC"/)
>
>
>
> nVar = dimsizes(var)
>
>
>
>
>
> p_rat = (/std1, std2, std3, std4/)
>
>
>
> p_cc = (/cor1, cor2, cor3, cor4/)
>
>
>
>
>
> ;**********************************
>
> ; Put the ratios and pattern correlations into
>
> ; arrays for plotting
>
> ;**********************************
>
>
>
> nDataSets = 1
> ; number of datasets
>
> npts = dimsizes(p_rat)
>
>
>
> ratio = new ((/nCase, nVar/), typeof(p_rat) )
>
> cc = new ((/nCase, nVar/), typeof(p_cc) )
>
>
>
> ratio(0,:) = p_rat
>
> cc(0,:) = p_cc
>
>
>
> ;**********************************************************************************************************************
>
> ;***************************** create
> plot
> ************************************************
>
> ;**********************************************************************************************************************
>
> res = True
> ; diagram mods
> desired
>
> res@tiMainString = "precipitation"
> ; title
>
> res@Colors = (/"blue"/) ;
> ; marker colors
>
> res@Markers = (/14/) ;,9,2,3,8/)
> ; marker styles
>
> res@markerTxYOffset = 0.03
> ; offset btwn marker &
> label
>
> res@gsMarkerSizeF = 0.018
> ; marker size
>
> res@txFontHeightF = 0.018
> ; text
> size**************taille des lettres et chiffres******
>
>
>
> res@stnRad = (/ 0.75, 1.25 /) ;
> additional standard radii
>
> res@ccRays = (/ 0.6, 0.9 /) ;
> correllation rays
>
> ;res@ccRays_color = "LightGray"
> ; default is "black"
>
>
>
> res@varLabels = var
>
> res@caseLabels = case ;
> affiche la liste des variables chargées
>
> res@varLabelsYloc = 1.5 ; Move
> location of variable labels [pour taylor]
>
> ;res@varLabelsYloc = 1.65 ; Move
> location of variable labels [pour taylor2]
>
> ;res@varLabelsYloc = 2.04 ; Move
> location of variable labels [pour taylor1]
>
> res@caseLabelsFontHeightF = 0.10 ; make slight
> larger [default=0.12 ]
>
> res@varLabelsFontHeightF = 0.01 ; make
> slight smaller [default=0.013]
>
>
>
> res@centerDiffRMS = True ; RMS
> 'circles'
>
> res@centerDiffRMS_color = "LightGray" ;
> default is "black"
>
>
>
> wks = gsn_open_wks("eps",
> "pre_tailor_annual")
>
> plot = taylor_diagram(wks, ratio, cc, res)
>
>
>
> ;**********************************************************************************************************************
>
> ;**********************************************************************************************************************
>
> ;**********************************************************************************************************************
>
> end
>
>
>
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> --
> https://sites.google.com/site/jgmsantos/
>
>
>
>
>
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Received on Mon Mar 31 06:38:01 2014
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