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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<p>thank you.</p>
<p>I have tried both methods and they would archive different EOF
patterns.<br>
</p>
<p>The results from the first method (1) is not desirable as it
doesnt include the signals from all the ensemble members.</p>
<p>The second method (2) is what i tried, but got errors in my code
when combining the time + ensemble dimension-see below. Would
anyone point me in the right direction?</p>
<p><br>
;==============================================================<br>
; Open the file: Read only the user specified period <br>
; =============================================================<br>
latS = 20.<br>
latN = 70. <br>
lonL = -80.<br>
lonR = 15.<br>
<br>
;yrStrt = 2081<br>
;yrLast = 2099<br>
<br>
season = "DJF" ; choose Dec-Jan-Feb seasonal mean<br>
<br>
neof = 3 ; number of EOFs<br>
optEOF = True <br>
optEOF@jopt = 0 ; This is the default; most commonly used; no
need to specify.<br>
optETS = False<br>
<br>
; =============================================================<br>
f = addfile ("anom_rcp45_zo_slr.nc", "r")<br>
<br>
slp=f->rcp45_anom ;[time |
240] x [ens | 100] x [lat | 45] x [lon | 90] <br>
printVarSummary(slp) <br>
printMinMax(slp,0)<br>
<br>
; ==============================================================;<br>
dimx = dimsizes(x)<br>
ntim = dimx(0) ; 240<br>
nens = dimx(1) ; 100<br>
nlat = dimx(2) ; 45<br>
mlon = dimx(3) ; 90<br>
<br>
nmos = 12<br>
nyrs = ntim/nmos ; 20<br>
<br>
; ==============================================================<br>
; compute desired global seasonal mean: month_to_season
(contributed.ncl) <br>
; The first average (DJF=JF) and the last average (NDJ=ND) are
actually two-month averages.<br>
; So make climatology and extract the months needed????<br>
; ==============================================================<br>
SLP = month_to_season (slp, season)<br>
nyrs = dimsizes(SLP&time)<br>
printVarSummary(SLP)<br>
<br>
;==========================================================================<br>
;Estimates and removes the least squares linear trend of the
dimension /time/ from all grid points.The mean is also removed.
Missing values are not allowed, use dtrend_msg_n if missing values
exist. <br>
;==========================================================================<br>
<br>
SLP = dtrend_n (SLP, False,0)<br>
printVarSummary(SLP)<br>
<br>
;
=================================================================<br>
; create weights: sqrt(cos(lat)) [or sqrt(gw) ]<br>
;
=================================================================<br>
rad = 4.*atan(1.)/180.<br>
lat = f->lat<br>
if (typeof(lat).eq."double") then<br>
clat = sqrt( cos(rad*tofloat(lat)) )<br>
else<br>
clat = sqrt( cos(rad*lat) )<br>
end if<br>
copy_VarCoords(lat, clat) ; contributed<br>
printVarSummary(clat) <br>
<br>
;
=================================================================<br>
; weight all observations <br>
;
=================================================================<br>
wSLP = SLP ; type float<br>
wSLP = SLP*conform(SLP, clat, 1)<br>
printVarSummary(wSLP) <br>
</p>
<p>; ===============================================================</p>
<p>; compute EOFs on the “total variance”, in which case you would
want your two dimensions to be space [lat x lon] and [time x ens
member]. If so, then the "time series” would be the concatenation
of the time series of each ensemble member. In order to get that,
you need to combine time and ens member dimensions into one.</p>
<p>; ===============================================================</p>
<p>; work = new((/2000/), "integer")<br>
; work!0 = “time”<br>
; work&time = time</p>
<p>
x = new((/2000/),"integer")<br>
work = new((/dimsizes(x)),nlat,mlon/), "double",
slp@_FillValue)<br>
printVarSummary(work)<br>
<br>
work := reshape(wSLP ,(/nyrs*nens,nlat,mlon/))<br>
printVarSummary(work) ;
(2000,45,90)<br>
<br>
copy_VarCoords(slp(0,0,:,:), work)<br>
printVarSummary(work) </p>
<p>;
=================================================================<br>
; Reorder (lat,lon,time) the *weighted* input data<br>
; Access the area of interest via coordinate subscripting<br>
;
=================================================================<br>
<br>
xw = work({lat|latS:latN},{lon|lonL:lonR},time|:)<br>
x = work(time|:,{lat|latS:latN},{lon|lonL:lonR}) <br>
<br>
; xw = wSLP({lat|latS:latN},{lon|lonL:lonR},time|:)<br>
; x = wSLP(time|:,{lat|latS:latN},{lon|lonL:lonR})<br>
<br>
eof = eofunc_Wrap(xw, neof, optEOF)<br>
eof_ts = eofunc_ts_Wrap (xw, eof, optETS) <br>
<br>
printVarSummary( eof ) ; examine EOF
variables<br>
printVarSummary( eof_ts )<br>
<br>
;
=================================================================<br>
; Normalize time series: Sum spatial weights over the area of used<br>
;
=================================================================<br>
eof_ts = dim_standardize_n( eof_ts, 0, 1) <br>
printVarSummary(eof_ts)<br>
<br>
;====================================================================<br>
;my code: Regress<br>
;======================================================================<br>
<br>
eof_regres=eof ;create an array with meta data<br>
<br>
do ne=0,neof-1<br>
eof_regres(ne,:,:)=(/ regCoef(eof_ts(ne,:), xw) /)<br>
end do<br>
printVarSummary(eof_regres)</p>
<p><br>
The problem:</p>
<p>After reshaping the detrended winter sea surface height, i cannot
perform EOF as the reshaped variable does not have a time
dimension. I have tried to add it without success:</p>
<p> work := reshape(wSLP ,(/nyrs*nens,nlat,mlon/))<br>
printVarSummary(work) ;
(2000,45,90)</p>
<p>Sri</p>
<p><br>
</p>
<p><br>
</p>
<p><br>
</p>
<p><br>
</p>
<div class="moz-cite-prefix">On 22.05.20 10:43, Alessandra Giannini
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:FE24DAF9-76AB-4C1C-BBFF-05ACBBAD7CFE@iri.columbia.edu">
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<div class=""><br class="">
</div>
hi Sri,
<div class=""><br class="">
</div>
<div class="">in general terms, when you have an ensemble, you can
do one of two things to compute EOFs:</div>
<div class=""><br class="">
</div>
<div class="">1) compute EOFs on the ensemble mean, which is what
you do here, since you used “dim_avg_n_Wrap” on your variable.
In this case the two dimensions that the EOF routine sees are
space [lat x lon] and time</div>
<div class="">2) compute EOFs on the “total variance”, in which
case you would want your two dimensions to be space [lat x lon]
and [time x ens member]. If so, then the "time series” would be
the concatenation of the time series of each ensemble member. In
order to get that, you need to combine time and ens member
dimensions into one.</div>
<div class=""><br class="">
</div>
<div class="">You may want to look up papers that discuss
characterizations of total, forced and internal variance.</div>
<div class="">Here (1) gives you a characterization of “forced”
variance, to the extent that the ensemble mean represents it.</div>
<div class="">(2) gives you a characterization of “total”
variance.</div>
<div class="">And if you subtracted the ensemble mean from each
ensemble member, and then proceeded as in (2), you would get the
internal variance.</div>
<div class=""><br class="">
</div>
<div class="">Here are some examples from a google scholar search:</div>
<div class=""><br class="">
</div>
<div class="">
<div tabindex="0" class="gs_citr">Harzallah, A. and Sadourny,
R., 1995. Internal versus SST-forced atmospheric variability
as simulated by an atmospheric general circulation model. <i
class="">Journal of climate</i>, <i class="">8</i>(3),
pp.474-495.</div>
</div>
<div class=""><a
href="https://journals.ametsoc.org/doi/abs/10.1175/1520-0442(1995)008%3C0474:IVSFAV%3E2.0.CO;2"
class="" moz-do-not-send="true">https://journals.ametsoc.org/doi/abs/10.1175/1520-0442(1995)008%3C0474:IVSFAV%3E2.0.CO;2</a></div>
<div class=""><br class="">
</div>
<div class="">
<div tabindex="0" class="gs_citr">Ting, M., Kushnir, Y., Seager,
R. and Li, C., 2009. Forced and internal twentieth-century SST
trends in the North Atlantic. <i class="">Journal of Climate</i>,
<i class="">22</i>(6), pp.1469-1481.</div>
</div>
<div class=""><a
href="https://journals.ametsoc.org/doi/full/10.1175/2008JCLI2561.1"
class="" moz-do-not-send="true">https://journals.ametsoc.org/doi/full/10.1175/2008JCLI2561.1</a></div>
<div class=""><br class="">
</div>
<div class="">
<div tabindex="0" class="gs_citr">Venzke, S., Allen, M.R.,
Sutton, R.T. and Rowell, D.P., 1999. The atmospheric response
over the North Atlantic to decadal changes in sea surface
temperature. <i class="">Journal of Climate</i>, <i class="">12</i>(8),
pp.2562-2584.</div>
</div>
<div class=""><a
href="https://journals.ametsoc.org/doi/full/10.1175/1520-0442%281999%29012%3C2562%3ATAROTN%3E2.0.CO%3B2"
class="" moz-do-not-send="true">https://journals.ametsoc.org/doi/full/10.1175/1520-0442%281999%29012%3C2562%3ATAROTN%3E2.0.CO%3B2</a></div>
<div class=""><br class="">
</div>
<div class="">Reasons why things don’t match could also include
whether you have subtracted the climatology or not</div>
<div class=""><br class="">
</div>
<div class="">Hope this helps!</div>
<div class="">alessandra</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
<div class="">
<div dir="auto" style="caret-color: rgb(0, 0, 0); color:
rgb(0, 0, 0); letter-spacing: normal; text-align: start;
text-indent: 0px; text-transform: none; white-space: normal;
word-spacing: 0px; -webkit-text-stroke-width: 0px;
text-decoration: none; word-wrap: break-word;
-webkit-nbsp-mode: space; line-break: after-white-space;"
class="">
<div dir="auto" style="word-wrap: break-word;
-webkit-nbsp-mode: space; line-break: after-white-space;"
class="">
<div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0,
0); font-family: Helvetica; font-size: 12px; font-style:
normal; font-variant-caps: normal; font-weight: normal;
letter-spacing: normal; text-align: start; text-indent:
0px; text-transform: none; white-space: normal;
word-spacing: 0px; -webkit-text-stroke-width: 0px;
text-decoration: none;">— <br class="">
Alessandra Giannini<br class="">
IRI for Climate and Society - The Earth Institute at
Columbia University<br class="">
P.O. Box 1000, Palisades NY 10964-8000, U.S.A.</div>
<div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0,
0); font-family: Helvetica; font-size: 12px; font-style:
normal; font-variant-caps: normal; font-weight: normal;
letter-spacing: normal; text-align: start; text-indent:
0px; text-transform: none; white-space: normal;
word-spacing: 0px; -webkit-text-stroke-width: 0px;
text-decoration: none;"><br class="">
currently at:<br class="">
LMD - École Normale Supérieure <br class="">
24, Rue Lhomond 75231 PARIS CEDEX 05, France<br class="">
</div>
</div>
</div>
</div>
<div><br class="">
<blockquote type="cite" class="">
<div class="">On May 22, 2020, at 1:01 AM, Sri nandini via
ncl-talk <<a href="mailto:ncl-talk@ucar.edu" class=""
moz-do-not-send="true">ncl-talk@ucar.edu</a>> wrote:</div>
<br class="Apple-interchange-newline">
<div class="">
<meta http-equiv="Content-Type" content="text/html;
charset=UTF-8" class="">
<div class="">
<p class="">Dear all,</p>
<p class="">I tried the below code for performing EOF on
model ensemble data, with plot attached, but it
doesn't match the EOF found in publications since i
believe i averaged the ensemble dimension instead of
including the signal from it. Can someone point me in
the right direction?</p>
<p class=""><br class="">
;==============================================================<br
class="">
; Open the file: Read only the user specified period <br
class="">
;
=============================================================<br
class="">
<br class="">
latS = 20.<br class="">
latN = 80. <br class="">
lonL = -70.<br class="">
lonR = 40.<br class="">
<br class="">
;yrStrt = 1986<br class="">
;yrLast = 2005<br class="">
<br class="">
season = "DJF" ; choose Dec-Jan-Feb seasonal mean<br
class="">
; season = "JJA" <br class="">
<br class="">
neof = 3 ; number of EOFs<br class="">
optEOF = True <br class="">
optEOF@jopt = 0 ; This is the default; most
commonly used; no need to specify.<br class="">
<br class="">
;
=============================================================<br
class="">
f = addfile ("anom_hist_zo_slr.nc", "r")<br
class="">
x=f->hist_anom
;[time | 240] x [ens | 100] x [lat | 45] x [lon | 90]<br
class="">
printVarSummary(x) <br class="">
slp=dim_avg_n_Wrap(x,1) <br
class="">
printVarSummary(slp) <br
class="">
printMinMax(slp,0)<br class="">
<br class="">
;
==============================================================<br
class="">
; compute desired global seasonal mean:
month_to_season (contributed.ncl) <br class="">
; The first average (DJF=JF) and the last average
(NDJ=ND) are actually two-month averages.<br class="">
; So make climatology and extract the months needed.<br
class="">
;
==============================================================<br
class="">
SLP = month_to_season (slp, season)<br class="">
; uClm = clmMonTLLL( u )<br class="">
nyrs = dimsizes(SLP&time)<br class="">
printVarSummary(SLP)<br class="">
<br class="">
;
=================================================================<br
class="">
; create weights: sqrt(cos(lat)) [or sqrt(gw) ]<br
class="">
;
=================================================================<br
class="">
rad = 4.*atan(1.)/180.<br class="">
lat = f->lat<br class="">
if (typeof(lat).eq."double") then<br class="">
clat = sqrt( cos(rad*tofloat(lat)) )<br
class="">
else<br class="">
clat = sqrt( cos(rad*lat) )<br class="">
end if<br class="">
copy_VarCoords(lat, clat) ; contributed<br class="">
printVarSummary(clat) <br class="">
<br class="">
;
=================================================================<br
class="">
; weight all observations <br class="">
;
=================================================================<br
class="">
wSLP = SLP ; type float<br class="">
wSLP = SLP*conform(SLP, clat, 1)<br class="">
printVarSummary(wSLP) <br
class="">
<br class="">
;
=================================================================<br
class="">
; Reorder (lat,lon,time) the *weighted* input data<br
class="">
; Access the area of interest via coordinate
subscripting<br class="">
;
=================================================================<br
class="">
xw =
wSLP({lat|latS:latN},{lon|lonL:lonR},time|:)<br
class="">
x =
wSLP(time|:,{lat|latS:latN},{lon|lonL:lonR}) <br
class="">
<br class="">
xw= dtrend(xw(lat|:,lon|:,time|:),False)<br class="">
printVarSummary(xw)<br class="">
</p>
<p class=""><br class="">
eof = eofunc_Wrap(xw, neof, optEOF) <br
class="">
eof_ts = eofunc_ts_Wrap (xw, eof, optETS)<br
class="">
<br class="">
printVarSummary( eof ) ;
examine EOF variables<br class="">
printVarSummary( eof_ts )<br class="">
;
=================================================================<br
class="">
; Normalize time series: Sum spatial weights over the
area of used<br class="">
;
=================================================================<br
class="">
eof_ts = dim_standardize_n( eof_ts, 0, 1) <br
class="">
printVarSummary(eof_ts)<br class="">
<br class="">
;====================================================================<br
class="">
;my code: Regress<br class="">
;======================================================================<br
class="">
<br class="">
eof_regres=eof ;create an array with meta data<br
class="">
;eof_ts(0,:)=-eof_ts(0,:)<br class="">
do ne=0,neof-1<br class="">
eof_regres(ne,:,:)=(/ regCoef(eof_ts(ne,:), xw) /)<br
class="">
end do<br class="">
printVarSummary(eof_regres)<br class="">
<br class="">
; eof_regres=-eof_regres<br class="">
<br class="">
;=================================================================<br
class="">
yyyymm = cd_calendar(eof_ts&time,-2)/100 <br
class="">
;============================================================<br
class="">
<br class="">
wks = gsn_open_wks("pdf","hist_SSH_EOF2")<br
class="">
<br class="">
plot = new(neof,graphic) ; create
graphic array; only needed if paneling<br class="">
res = True <br class="">
res@mpProjection = "LambertConformal"; choose
projection <br class="">
res@gsnDraw = False ; don't
draw<br class="">
res@gsnFrame = False ; don't
advance frame<br class="">
<br class="">
res@cnFillOn = True ; turn on
color<br class="">
res@cnLinesOn = False ; turn
off the contour lines<br class="">
<br class="">
res@mpDataBaseVersion = "MediumRes"
<br class="">
res@cnLineLabelsOn = False ; turn off
contour line labels<br class="">
<br class="">
res@cnFillDrawOrder = "PreDraw" ; draw
contours before continents<br class="">
res@cnFillPalette = "BlRe"<br class="">
;res@cnLineThicknessF = 2<br class="">
;res@cnLineColor = 0<br class="">
<br class="">
res@mpMinLatF = latS ; zoom in
on map<br class="">
res@mpMaxLatF = latN<br class="">
res@mpMinLonF = lonL<br class="">
res@mpMaxLonF = lonR<br class="">
;res@mpFillOn = False ;
turn on map fill<br class="">
res@mpOutlineOn = True ; turn
the map outline on<br class="">
<br class="">
res@cnLevelSelectionMode = "ManualLevels" ;
manual set levels<br class="">
res@cnMinLevelValF = -0.5<br class="">
res@cnMaxLevelValF = 0.5<br class="">
res@cnLevelSpacingF = .1 ; 20
contour levels <br class="">
<br class="">
res@lbLabelBarOn = False ; turn
off individual lb's<br class="">
res@lbBoxEndCapStyle = "TriangleBothEnds" ;
Added in NCL V6.4.0<br class="">
res@lbLabelAutoStride = True ;
Control labelbar spacing<br class="">
res@gsnMaximize = True ;
large format in landscape<br class="">
res@gsnAddCyclic = False ;
plotted dataa are not cyclic<br class="">
res@gsnMaskLambertConformal = True ; turn
on lc masking<br class="">
<br class="">
;=============================panel plot only
resources<br class="">
resP = True ; modify the
panel plot<br class="">
resP@gsnMaximize = True ; large
format<br class="">
resP@gsnPanelLabelBar = True ; add common
colorbar<br class="">
; now change the size of the label bar labels<br
class="">
resP@lbLabelFontHeightF = 0.017<br class="">
<br class="">
;====================Create (but don't draw) both
plots<br class="">
do n=0, neof -1<br class="">
res@gsnLeftString = "SSH DJF EOF "+(n+1)<br
class="">
res@gsnRightString = sprintf("%5.1f",
eof_regres@pcvar(n)) +"%"<br class="">
plot(n)=gsn_csm_contour_map(wks,eof_regres(n,:,:),res)<br
class="">
end do<br class="">
<br class="">
<br class="">
gsn_panel(wks,plot,(/neof,1/),resP) ; now draw
as one plot<br class="">
</p>
<p class=""><br class="">
</p>
<p class="">The EOF1 should resemble EOF3 pattern
instead, as per previous literature for sea surface
height.<br class="">
</p>
<p class="">Can someone help me with this?</p>
<p class="">Sri</p>
<p class=""><br class="">
</p>
<p class=""><br class="">
</p>
<div class="moz-cite-prefix">On 21.05.20 17:18, Sri
nandini via ncl-talk wrote:<br class="">
</div>
<blockquote type="cite"
cite="mid:cf28d538-08f8-40bf-e712-700c8bcc4710@uni-hamburg.de"
class="">
<meta http-equiv="Content-Type" content="text/html;
charset=UTF-8" class="">
<p class="">Thank you.</p>
<p class="">The idea is that SSH variability is
computed over the ensemble dimension, rather than
the traditional time-dimension- i.e computing EOF
across individual ensemble member at each time step
with looping, in which case the below would not make
sense .i.e averaging the ensemble dimension?</p>
<p class="">Perhaps i should perform the EOF on both
the time and ensemble dimension as variations across
time and ensemble are supposed to be the same after
detrending, the more samples the more robust the EOF
is?<br class="">
</p>
<p class="">Best</p>
<p class="">Sri</p>
<p class=""><br class="">
</p>
<div class="moz-cite-prefix">On 21.05.20 05:09, Dennis
Shea wrote:<br class="">
</div>
<blockquote type="cite"
cite="mid:CAOF1d_4fk_f_AG_in010AJuXPFxwDkVJ_wvhobb+Jhw9tGc0Uw@mail.gmail.com"
class="">
<meta http-equiv="content-type" content="text/html;
charset=UTF-8" class="">
<div dir="ltr" class="">
<div class="">At each time stelp,and lat/lon
location all 100 ensembel members</div>
<div class=""> <br class="">
</div>
<div class=""><br class="">
</div>
<div class="">ssh_ens_mean = <a
href="http://www.ncl.ucar.edu/Document/Functions/Contributed/dim_avg_n_Wrap.shtml"
moz-do-not-send="true" class=""><b class="">dim_avg_n_Wrap</b></a>(ssh,1)
; average <br class="">
</div>
<div class=""> printVarSummary(ssh_ens_mean)</div>
<div class=""> printMinMax(ssh_ens_mean,0)</div>
<div class=""><br class="">
</div>
<div class="">--</div>
<div class="">Input 'ssh_ens_mean' as you would
any other variable to the eof function<br
class="">
</div>
</div>
<br class="">
<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">On Wed, May 20,
2020 at 7:29 AM Sri nandini via ncl-talk <<a
href="mailto:ncl-talk@ucar.edu"
moz-do-not-send="true" class="">ncl-talk@ucar.edu</a>>
wrote:<br class="">
</div>
<blockquote class="gmail_quote" style="margin:0px
0px 0px 0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">Hello dear
fellow ncl users,<br class="">
<br class="">
I have been analyzing and plotting the standard
EOF with success. Now i <br class="">
wish to proceed onto model ensemble EOF but
having problems <br class="">
understanding this coding.<br class="">
<br class="">
My original data is in this format: SSH= [time
| 240] x [ens | 100] x <br class="">
[lat | 45] x [lon | 90]<br class="">
How can i modify the standard EOF script on the
NCL page to perform it <br class="">
on model ensemble of 100 members?e.g through
looping it?<br class="">
<br class="">
Would be grateful for an example.<br class="">
<br class="">
best<br class="">
<br class="">
sri<br class="">
<br class="">
<br class="">
<br class="">
_______________________________________________<br
class="">
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class="">ncl-talk@ucar.edu</a><br class="">
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