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WTs_ICTP_SPEEDY_localNC.m
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WTs_ICTP_SPEEDY_localNC.m
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% Atmospheric circulation states based on K-means analysis of atm circulations
% AG Munoz (IRI Columbia U; CMC Universidad del Zulia) - agmunoz@iri.columbia.edu
% Project: IRAP
% First edition: Feb 4, 2013
% Last edition: Nov 24, 2014
%Key questions here:
%(1) filter the annual cycle? No need to filter it for the present study
%EOF-filtering is applied.
%
% Details:
% The code uses NNRP data from a particular domain (sdomain) to define
% clusters representing circulation states, usually in terms of geopotential heigth
% at 500 mb, but this is modifiable. Composites of moisture fluxes, rainfall,
% and lightning are computed for each cluster.
%
% Maps correspond to a bigger domain (bdomain) or could be customizable (pdomain).
%
% Data will be read via DAP and written to a _ICTP_SPEEDY.mat file. If the domains/variables are
% not changed, the user doesn't need to read the data again from the DAP
% server.
%
% Note (only if using chi): year 2009 has a lot of missing values for chi as for Nov '14.
%An easy way to solve the problem is just to make yeare=2008.
%%%%%START OF USER-MODIFIABLE SECTION%%%%%%%%%%%%
disp('Start...');
% set working directory
clear all
% set working directory
cd /Users/agms/Documents/Angel/GFDL/WTs/Weather_within_climate
%addpath /usr/local/bin
addpath /Users/agms/Documents/MATLAB/m_map
%parpool('local')
%Read data via OpenDAP?
down=1; %1=yes; 0=no (this assumes the data is available in _ICTP_SPEEDY.mat format; *not* NetCDF!)
%Select variable for clustering:
var=1; %options available are
% 1= z500 (geopotential height at 500mb)
% 2= chi (velocity potential)
%Define temporal parameters:
seasons='Dec'; %start
midmon ='Jan'; %middle month
seasone='Feb'; %end
yeari=1981; %first year (MUST BE >=1995 AND <2005!)
yeare=2010; %last year (MUST BE >2006!) %Note: for chi there're (or used to be) missing values in 2009!!!!
%Define spatial parameters:
%sdomain (for clusters)
% slonmin=263.75;
% slonmax=306.25;
% slatmin=5;
% slatmax=30;
slonmin=255;%-105;
slonmax=291; %-69;
slatmin=30;
slatmax=50;
%bdomain (for plotting)
blonmin=0;
blonmax=360;
blatmin=0;
blatmax=90;
%pdomain (custom plotting)
pdlatmin=slatmin;
pdlatmax=slatmax;
pdlonmin=slonmin;
pdlonmax=slonmax;
%Define cluster parameters:
minclust=5; %min num of clust, typically 2
maxclust=5; %typically 10
varfract=0.95; %total required variance explained by the EOF pre-filtering in the k-means algorithm
nclust=5; % choice of k (use classifiability index and physics to determine this value)
ncfile='/Users/agms/Documents/Angel/SPEEDY/exp_pacemaker_ENSO.M1/exp_pacemaker_ENSO_M1.nc';
%%%END OF USER-MODIFIABLE SECTION (DO NOT MODIFY ANYTHING BELOW THIS LINE!!)%%%%%
%%
%%Read NC
Xp=double(ncread(ncfile,'lon'));
Yp=double(ncread(ncfile,'lat'));
Tp = ncread(ncfile, 'time')/24 + datenum(1979,1,1);
dv = datevec(Tp);
ilon = find(Xp > slonmin & Xp < slonmax); %find longitudes
ilat = find(Yp > slatmin & Yp < slatmax); %find latitudes
ilon2 = find(Xp > blonmin & Xp < blonmax); %find longitudes
ilat2 = find(Yp > blatmin & Yp < blatmax); %find latitudes
%%%THIS WORKS FOR DJF -- change for other seasons
itim = (dv(:,1) >= yeari+1 & dv(:,1) <= yeare) & (dv(:,2) >11 | dv(:,2) <=2) | (dv(:,1) == yeari & dv(:,1) == 12) ; %find times
iridl=ncfile;
Xp=Xp(ilon2);
Yp=Yp(ilat2);
%% Rainfall
% Due to the way IRIDL has divided the dataset (retro + present), we need
% to read them in two pieces and add a bridge between them.
% Future version: use appendstream at DL;
% sdomain is a subset of bdomain, so manage that in Matlab
%% Rainfal YEARI-2005 CPC Unified Precipitation RETRO + DJF 2006-YEARE CPC Unified Precipitation Realtime
if down==1
month={'Jan' 'Feb' 'Mar' 'Apr' 'May' 'Jun' 'Jul' 'Aug' 'Sep' 'Oct' 'Nov' 'Dec'};
%iridl=['http://iridl.ldeo.columbia.edu/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.prec/lon/' num2str(slonmin) '/' num2str(slonmax) '/RANGEEDGES/lat/' num2str(slatmin) '/' num2str(slatmax) '/RANGEEDGES/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/dods'];
pr1 = double(ncread(ncfile,'prec'));
pr1 = pr1(ilon,ilat,itim);
[nlonp nlatp ndat2]=size(squeeze(pr1)); %get dims
pr1 = squeeze(pr1);
pr1 = permute(pr1,[2 1 3]); %we want lat first
% This is just to plot it if necessary (plot section comes later; this is a
% test)
% figure(1); clf
% Xmat=repmat(Xp',length(Yp),1); Ymat=repmat(Yp,1,length(Xp));
% m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
% m_coast('patch',[.7 .7 .7],'edgecolor','none');
% m_grid;
% hold on
% [cs,h]=m_contour(Xmat,Ymat,squeeze(pr1(:,:,1000)));
pr_SESA=reshape(pr1,nlatp*nlonp,ndat2)';
%iridl=['http://iridl.ldeo.columbia.edu/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.prec/lon/' num2str(blonmin) '/' num2str(blonmax) '/RANGEEDGES/lat/' num2str(blatmin) '/' num2str(blatmax) '/RANGEEDGES/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/dods'];
pr1 = double(ncread(ncfile,'prec'));
pr1 = pr1(ilon2,ilat2,itim);
[nlonp nlatp ndat2]=size(squeeze(pr1)); %get dims
pr1 = squeeze(pr1);
pr1 = permute(pr1,[2 1 3]); %we want lat first
pr=reshape(pr1,nlatp*nlonp,ndat2)';
clear pr1
save -v7.3 precip_ICTP_SPEEDY.mat pr pr_SESA Xp Yp
else
load precip_ICTP_SPEEDY.mat pr pr_SESA Xp Yp
end
disp('Rainfall (SPEEDY) has been read and storaged ');
%% CIRCULATION VARIABLE
%This is sdmain (for computing clusters):
if down==1
if var==1
% z500
%iridl=['http://iridl.ldeo.columbia.edu/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.gh_500/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/lon/' num2str(slonmin) '/' num2str(slonmax) '/RANGEEDGES/lat/' num2str(slatmin) '/' num2str(slatmax) '/RANGEEDGES/dods'];
phi = double(ncread(ncfile,'gh_500'));
phi = phi(ilon,ilat,itim);
else
disp('Unkown option for cluster variable');
end
[nlon2 nlat2 ndat2]=size(squeeze(phi));
phi = squeeze(phi);
phi = permute(phi,[2 1 3]);
iridl=ncfile;
%T=double(ncread(iridl,'time'));
X=double(ncread(iridl,'lon'));
Y=double(ncread(iridl,'lat'));
X=X(ilon2);
Y=Y(ilat2);
phi=reshape(phi,nlat2*nlon2,ndat2)';
save -v7.3 var4clust_ICTP_SPEEDY.mat phi X Y ndat2
else
load var4clust_ICTP_SPEEDY.mat X Y phi ndat2
end
disp('Circulation variable (SPEEDY) has been read and storaged ');
%% SECTION TO COMPUTE CLUSTERS via K-means
rng(1); % initialize random numbers for reproducible results
CI=NaN*ones(maxclust,1); K=NaN*ones(ndat2,maxclust);
for kk=minclust:maxclust
rng(1); % initialize random numbers for reproducible results
[CI(kk),K(:,kk)]=kmeans_ci(phi,'s',varfract,kk,100);
disp(['Calculated k-means for K=' num2str(kk) ' yielding CI of ' num2str(CI(kk))])
end
% %
% % % Plot CI Index
% figure(1); clf
% plot(CI,'k-','Linewidth',1.5)
% hold on
% set(gca,'FontSize',14)
% xlabel('No. of Clusters'); ylabel('Classifiability Index')
% title(['Classifiability Index - ' num2str(yeari) '-' num2str(yeare) ' ' num2str(varfract) ' var'])
% grid on
% axis([minclust maxclust 0.7 1.02])
save -v7.3 CI_95p_z500_obs_ICTP_SPEEDY.mat CI K
indx=K(:,nclust);
%% MORE DATA FOR PLOT SECTION
%%%This subsection is in charge of DAPing data to plot bdomain/pdomain
if down==1
if var==1
% z500
%iridl=['http://iridl.ldeo.columbia.edu/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.gh_500/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/lon/' num2str(blonmin) '/' num2str(blonmax) '/RANGEEDGES/lat/' num2str(blatmin) '/' num2str(blatmax) '/RANGEEDGES/dods'];
phi = double(ncread(ncfile,'gh_500'));
phi = phi(ilon2,ilat2,itim);
else
disp('Unkown option for cluster variable');
end
Xv=double(ncread(iridl,'lon'));
Yv=double(ncread(iridl,'lat'));
Xv=Xv(ilon2);
Yv=Yv(ilat2);
[nlonv nlatv ndat2]=size(squeeze(phi));
phi = squeeze(phi);
phi = permute(phi,[2 1 3]);
phi=reshape(phi,nlatv*nlonv,ndat2)';
%phi=phi(1:double(lseas)*nseas,:);
disp('Circulation variable (SPEEDY) for bdomain has been read and storaged ');
% moisture fluxes
% uq
%iridl=['http://iridl.ldeo.columbia.edu/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.q_850/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/lon/' num2str(blonmin) '/' num2str(blonmax) '/RANGEEDGES/lat/' num2str(blatmin) '/' num2str(blatmax) '/RANGEEDGES/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.u_850/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/lon/' num2str(blonmin) '/' num2str(blonmax) '/RANGEEDGES/lat/' num2str(blatmin) '/' num2str(blatmax) '/RANGEEDGES/mul/dods'];
q850 = double(ncread(ncfile,'q_850'));
q850 = q850(ilon2,ilat2,itim);
u850 = double(ncread(ncfile,'u_850'));
u850 = u850(ilon2,ilat2,itim);
int_dP = u850.*q850;
[nlon2 nlat2 ndat2]=size(squeeze(int_dP));
int_dP = squeeze(int_dP);
uq = permute(int_dP,[2 1 3]);
uq=reshape(uq,nlat2*nlon2,ndat2)';
%T=double(ncread(iridl,'time'));
%X=double(ncread(iridl,'lon'));
%Y=double(ncread(iridl,'lat'));
% vq
%iridl=['http://iridl.ldeo.columbia.edu/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.q_850/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/lon/' num2str(blonmin) '/' num2str(blonmax) '/RANGEEDGES/lat/' num2str(blatmin) '/' num2str(blatmax) '/RANGEEDGES/home/.agmunoz/.SPEEDY/.SPEEDY_T30L8_1979-2015_daily.nc/.v_850/time/%28' seasons '-' seasone '%20' num2str(yeari) '-' num2str(yeare) '%29RANGE/lon/' num2str(blonmin) '/' num2str(blonmax) '/RANGEEDGES/lat/' num2str(blatmin) '/' num2str(blatmax) '/RANGEEDGES/mul/dods'];
clear int_dP
v850 = double(ncread(ncfile,'v_850'));
v850 = v850(ilon2,ilat2,itim);
int_dP = v850.*q850;
[nlon2 nlat2 ndat2]=size(squeeze(int_dP));
int_dP = squeeze(int_dP);
vq = permute(int_dP,[2 1 3]);
vq=reshape(vq,nlat2*nlon2,ndat2)';
disp('Low level moisture fluxes (SPEEDY) have been read and storaged ');
save -v7.3 plotvars_ICTP_SPEEDY.mat phi uq vq nlat2 nlon2 nlatp nlonp nlonv nlatv Xv Yv X Y
else
load plotvars_ICTP_SPEEDY.mat phi uq vq nlat2 nlon2 nlatp nlonp nlonv nlatv Xv Yv X Y
end
disp('Data have been read and storaged ');
disp('The fun is about to start...');
%
%I don't understand why precip is 1 day shorter
nt=2610; %2797; %ndat2-1; %in case a subset is needed
ndat2=nt;
phi=phi(1:nt,:);
pr=pr(1:nt,:);
uq=uq(1:nt,:);
vq=vq(1:nt,:);
indx=K(1:nt,nclust);
%%
%Computing anomalies
clear uqcompa vqcompa z3compa prcompa
for k=1:nclust
clear kk
kk=find(indx==k);
nday(k)=length(kk);
%computing anomaly fields:
uqcompa(k,:)=squeeze(nanmean(uq(kk,:),1) - nanmean(uq,1));
vqcompa(k,:)=squeeze(nanmean(vq(kk,:),1) - nanmean(vq,1));
z3compa(k,:)=squeeze(nanmean(phi(kk,:),1) - nanmean(phi,1));
prcompa(k,:)=squeeze(nanmean(pr(kk,:),1) - nanmean(pr,1));
prcompo(k,:)=squeeze(nanmean(pr(kk,:),1));
end
save -v7.3 h500_NENA_ICTP_SPEEDY.mat z3compa nday Xv Yv Xp Yp prcompa prcompo
uqcompa=reshape(uqcompa,nclust,nlat2,nlon2);
vqcompa=reshape(vqcompa,nclust,nlat2,nlon2);
z3compa=reshape(z3compa,nclust,nlatv,nlonv);
prcompa=reshape(prcompa,nclust,nlatp,nlonp);
prcompo=reshape(prcompo,nclust,nlatp,nlonp);
[dumb,kkplot]=sort(nday,'descend');
prclim = reshape(nanmean(pr,1),nlatp,nlonp);
z3clim = reshape(nanmean(phi,1),nlatv,nlonv); %For Xiaosong's tests
%kkplot = [4 5 2 1 3];
kkplot = [5 3 4 1 2];
%% PLOT SECTION
%%plot composites
disp('Generating plots ');
%Circulation variable and moisture flux anomalies
clear varcompa X2 Y2 Xv2 Yv2
[X2,Y2]=meshgrid(X,Y);
[Xv2,Yv2]=meshgrid(Xv,Yv);
if var==2
%Let's interpolate chi to the standard NNRP grid
for k=1:nclust
varcompa(k,:,:) = interp2(Xv2, Yv2, squeeze(z3compa(k,:,:)),X2,Y2);
end
else
varcompa=z3compa;
end
%clear z3compa
Xmat=repmat(Xv',length(Yv),1); Ymat=repmat(Yv,1,length(Xv));
figure(2); clf
labs={'(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'};
set(gca,'FontSize',14)
%ns=1.; %n-sigmas
pct_th=66; %percentile threshold for plotting arrows
scal=1.5;
colormap((b2r(-10,10)))
pdlatmin=blatmin;
pdlatmax=blatmax;
pdlonmin=blonmin;
pdlonmax=blonmax;
for kplot=1:nclust
map=squeeze(varcompa(kkplot(kplot),:,:));
map(isnan(map))=0;
umap=squeeze(uqcompa(kkplot(kplot),:,:))*scal; %
vmap=squeeze(vqcompa(kkplot(kplot),:,:))*scal; %
um =prctile(reshape(umap,size(umap,1)*size(umap,2),1),pct_th);
vm =prctile(reshape(vmap,size(umap,1)*size(umap,2),1),pct_th);
umap ( umap>-um & umap<um )=NaN;
vmap ( vmap>-vm & vmap<vm )=NaN;
h=subplot(1,nclust,kplot);
position=get(h,'position');
%m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
m_proj('stereographic','longitude',[-90],'latitude',[90],'radius',[80]);
m_coast('patch',[.7 .7 .7],'edgecolor','none');
%m_grid;
m_grid('xticklabels',[]);
hold on
%Add a square to show sdomain
%m_line(-59,-30.5,'marker','square','markersize',65,'color','black','linewidth',1.5); hold on
if var==1 % z850
[cs,h]=m_contour(Xmat,Ymat,map,'linewidth',0.9); %caxis([-15 15]);
hold on
[cs,h]=m_contour(Xmat,Ymat,map,'linewidth',1.05); %caxis([-15 15]);
elseif var==2 %chi
[cs,h]=m_contour(Xmat,Ymat,map,'linewidth',0.9);
else
disp('Unkown option for cluster variable');
end
%clabel(cs,h,'fontsize',12);
hold on
%m_quiver(Xmat,Ymat,umap,vmap,0,'color','black');
%m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
m_proj('stereographic','longitude',[-90],'latitude',[90],'radius',[80]);
%set(gca,'FontSize',12)
title([labs{kplot} ' WT ' num2str(kplot) ' (' num2str(round(nday(kkplot(kplot))/ndat2*100)) '% of days)'])
end
colormap((b2r(-200,200)))
h=colorbar;
set(h, 'Position', [.92 .235 .02 .69])
ha = axes('Position',[0 0 1 1],'Xlim',[0 1],'Ylim',[0
1],'Box','off','Visible','off','Units','normalized', 'clipping' , 'off');
text(0.5, 1,['WTs - z500 - DJF ' num2str(yeari) '-' num2str(yeare) ' ' num2str(varfract) ' var'],'HorizontalAlignment' ,'center','VerticalAlignment', 'top')
%In what follows, 2*sqrt(um*um+vm*vm) was selected to provide ~100 g/kg
%m/s. It may differ in other studies
%For m_vec we have SCALE, LAT, LON, MAG, etc)
%[hpv5, htv5] = m_vec(1, 1, -40, 2*sqrt(um*um+vm*vm)*scal, 0, 'black', 'key', '100 g kg^{-1} m s^{-1}');
%set(htv5,'FontSize',12);
orient landscape
disp('WTs figures saved')
% %Precipitation anomaly
% pdlatmin=slatmin;
% pdlatmax=slatmax;
% pdlonmin=slonmin;
% pdlonmax=slonmax;
% Xmat=repmat(Xp',length(Yp),1); Ymat=repmat(Yp,1,length(Xp));
% figure(3); clf
% labs={'(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'};
% colormap(flipud(b2r(-10,10)))
% for kplot=1:nclust
% map=squeeze(prcompa(kkplot(kplot),:,:));
% map(isnan(map))=0;
% h=subplot(1,nclust,kplot);
% position=get(h,'position');
% m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
% m_coast('patch',[.7 .7 .7],'edgecolor','none');
% %m_grid;
% m_grid('xticklabels',[]);
% %Add a square to show sdomain
% %m_line(-59,-30.5,'marker','square','markersize',65,'color','black','linewidth',1.5);
% hold on
% [cs,h]=m_contour(Xmat,Ymat,map); caxis([-1 1]);
% m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
% title([labs{kplot} ' WT ' num2str(kplot) ' (' num2str(round(nday(kkplot(kplot))/ndat2*100)) '% of days)'])
% end
% h=colorbar;
% set(h, 'Position', [.92 .235 .02 .69])
% %ha = axes('Position',[0 0 1 1],'Xlim',[0 1],'Ylim',[0 1],'Box','off','Visible','off','Units','normalized', 'clipping' , 'off');
% %text(0.5, 1,['Rainfall - WTsz500 - DJF ' num2str(yeari) '-' num2str(yeare) ' ' num2str(varfract) ' var'],'HorizontalAlignment' ,'center','VerticalAlignment', 'top')
%
%%------------------------------------------
%Precipitation anomaly
pdlatmin=slatmin;
pdlatmax=slatmax;
pdlonmin=slonmin;
pdlonmax=slonmax;
Xmat=repmat(Xp',length(Yp),1); Ymat=repmat(Yp,1,length(Xp));
figure(3); clf
labs={'(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'};
%colormap(flipud(b2r(-10,10)))
for kplot=1:nclust
map=squeeze(prcompa(kkplot(kplot),:,:));
map(isnan(map))=0;
h=subplot(1,nclust,kplot);
position=get(h,'position');
m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
%m_grid('xtick',[pdlonmin:5:pdlonmax],'ytick',[pdlatmin:10:pdlatmax]);
m_grid('xticklabels',[]);
m_coast('linewidth',2,'color','black');
hold on
m_plus = map;
m_plus(map<=0) = NaN;
m_neg = map;
m_neg(map>0) = NaN;
[cs,h]=m_contour(Xmat,Ymat,m_neg,[-2:0.5:0],'linewidth',1.5,'color','blue','LineStyle','--'); %caxis([-100 100]); %orig steo 0.2
clabel(cs,h,'fontsize',10,'color','black');
hold on
[cs,h]=m_contour(Xmat,Ymat,m_plus,[0:0.5:2],'linewidth',1.5,'color','red'); %caxis([-100 100]); %orig steo 0.2
clabel(cs,h,'fontsize',10,'color','black');
%following two lines are used to plot stat sig values (**masking**)
% map=squeeze(fsigp(kplot,:,:)).*map;
% map(map==0)=NaN; %masking
% %map(isnan(map))=0;
% [cs,h]=m_contourf(Xmatp,Ymatp,map,[-100:80:20]);
% caxis([-100 20]);
% colormap(gray)
% hold on
%Choose one of the following projections:
%Stereographic (NH)
%m_proj('stereographic','longitude',[-90],'latitude',[90],'radius',[80]);
%m_grid('xticklabels',[],'yticklabels',[]);
m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
title([labs{kplot} ' WT ' num2str(kplot) ' (' num2str(round(nday(kkplot(kplot))/ndat2*100)) '% of days)'])
end
%h=colorbar;
orient landscape
disp('Rainfall figures saved')
Xmat=repmat(X',length(Y),1); Ymat=repmat(Y,1,length(X));
figure(4); clf
labs={'(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'};
set(gca,'FontSize',14)
%ns=1.; %n-sigmas
pct_th=66; %percentile threshold for plotting arrows
scal=0.3;
colormap((b2r(-10,10)))
pdlatmin=15;
pdlatmax=70;
pdlonmin=240;
pdlonmax=300;
for kplot=1:nclust
map=squeeze(varcompa(kkplot(kplot),:,:));
map(isnan(map))=0;
umap=squeeze(uqcompa(kkplot(kplot),:,:))*scal; %
vmap=squeeze(vqcompa(kkplot(kplot),:,:))*scal; %
um =prctile(reshape(umap,size(umap,1)*size(umap,2),1),pct_th);
vm =prctile(reshape(vmap,size(umap,1)*size(umap,2),1),pct_th);
umap ( umap>-um & umap<um )=NaN;
vmap ( vmap>-vm & vmap<vm )=NaN;
h=subplot(1,nclust,kplot);
position=get(h,'position');
m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
m_coast('patch',[.7 .7 .7],'edgecolor','none');
%m_grid;
m_grid('xticklabels',[]);
hold on
%Add a square to show sdomain
%m_line(-59,-30.5,'marker','square','markersize',65,'color','black','linewidth',1.5); hold on
if var==1 % z850
[cs,h]=m_contour(Xmat,Ymat,map,'linewidth',0.9); %caxis([-15 15]);
hold on
[cs,h]=m_contour(Xmat,Ymat,map,'linewidth',1.05); %caxis([-15 15]);
elseif var==2 %chi
[cs,h]=m_contour(Xmat,Ymat,map,'linewidth',0.9);
else
disp('Unkown option for cluster variable');
end
clabel(cs,h,'fontsize',12);
hold on
m_quiver(Xmat,Ymat,umap,vmap,0,'color','black');
m_proj('Equidistant Cylindrical','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
%set(gca,'FontSize',12)
title([labs{kplot} ' WT ' num2str(kplot) ' (' num2str(round(nday(kkplot(kplot))/ndat2*100)) '% of days)'])
end
colormap((b2r(-200,200)))
h=colorbar;
set(h, 'Position', [.92 .235 .02 .69])
%ha = axes('Position',[0 0 1 1],'Xlim',[0 1],'Ylim',[0 1],'Box','off','Visible','off','Units','normalized', 'clipping' , 'off');
%text(0.5, 1,['WTs - z500 - DJF ' num2str(yeari) '-' num2str(yeare) ' ' num2str(varfract) ' var'],'HorizontalAlignment' ,'center','VerticalAlignment', 'top')
%In what follows, 2*sqrt(um*um+vm*vm) was selected to provide ~100 g/kg
%m/s. It may differ in other studies
%For m_vec we have SCALE, LAT, LON, MAG, etc)
%[hpv5, htv5] = m_vec(1, 1, -40, 2*sqrt(um*um+vm*vm)*scal, 0, 'black', 'key', '100 g kg^{-1} m s^{-1}');
%set(htv5,'FontSize',12);
orient landscape
disp('WTs figures saved')
%Precipitation climatology
pdlatmin=slatmin;
pdlatmax=slatmax;
pdlonmin=slonmin;
pdlonmax=slonmax;
Xmat=repmat(Xp',length(Yp),1); Ymat=repmat(Yp,1,length(Xp));
figure(5); clf
colormap(jet)
map=squeeze(prclim);
%map(isnan(map))=0;
m_proj('lambert','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
m_coast('color',[0. 0. 0.],'linewidth',2.5);
m_grid;
%Add a square to show sdomain
%m_line(-59,-30.5,'marker','square','markersize',65,'color','black','linewidth',1.5);
hold on
[cs,h]=m_contourf(Xmat,Ymat,map); %caxis([-1 1]);
m_proj('lambert','longitudes',[pdlonmin pdlonmax],'latitudes',[pdlatmin pdlatmax]);
h=colorbar;
set(gca,'FontSize',14)
title('A) _SPEEDYervations')
%set(h, 'Position', [.92 .195 .02 .69])
%set(h, 'Position', [.92 .235 .02 .69])
%ha = axes('Position',[0 0 1 1],'Xlim',[0 1],'Ylim',[0 1],'Box','off','Visible','off','Units','normalized', 'clipping' , 'off');
save -v7.3 precip_obs_ICTP_SPEEDY.mat prclim Xp Yp
orient landscape
disp('Rainfall clim figures saved')
save -v7.3 precons_obs_ICTP_SPEEDY.mat prcompo Xp Yp
%%
%Others (normally, don't modify this part)
nseas=yeare-yeari; %number of seasons (1980 is Dic1979+JanFeb1980)
lseas=90; %int8(ndat2/nseas); %length of the season (DJF in our case); lseas MUST BE EVEN INTEGER, adjust accordingly!!!! 2790 31
%%
%%WTs stats
%indx=K(1:double(lseas)*nseas,nclust);
indx=K(1:lseas*nseas,nclust);
clear nday ndayperseas
indx=reshape(indx,lseas,nseas);
for iyr=1:nseas
for k=1:nclust
clear kk kk1 kk2
kk=find(indx(:,iyr)==k);
ndayperseas(iyr,k)=length(kk);
end
end
%Re-ordering:
for k1=1:nclust
ndayperseaso(:,k1) = ndayperseas(:,kkplot(k1));
end
figure(6); clf
%plot(ndayperseas,'Linewidth',1.5)
bar(ndayperseaso,'stack')
%bar(ndayperseas)
legend('1','2','3','4','5','6','7','8')
set(gca,'FontSize',14)
xlabel('DJF'); ylabel('Frequency (days)')
xlim([0.5 nseas+.5])
ylim([0 lseas+0.5])
set(gca,'XTick',1:5:nseas)
set(gca,'XTickLabel',{'1981','1986','1991','1996','2001','2006','2011','2016'})
%title('Occupation Frequency by Year')
grid off
cmap=colormap;
%Klee diagram!!!
indx=K(1:lseas*nseas,nclust);
indx=reshape(squeeze(indx(1:nseas*lseas,:)),lseas,nseas);
clear indxo
% %Re-ordering:
for nd=1:lseas
for ns=1:nseas
for k=1:nclust
if indx(nd,ns) == kkplot(k)
indxo(nd,ns)= k;
end
end
end
end
figure(7); clf
colormap((b2r(-5,5)))
%bar3(indx(:,4:5))
imagesc(indxo(:,:))
%axis([1 90 1 2 ])
%set(gca,'XTick',0.5:1:2.5)
%set(gca,'XTickLabel',{'82-83','83-84'})
set(gca,'FontSize',15)
xlabel('DJF'); ylabel('Calendar Day')
set(gca,'XTick',1:5:nseas)
set(gca,'XTickLabel',{'1981','1986','1991','1996','2001','2006','2011','2016'})
%colormap(coll);%colorbar
set(gca,'FontSize',15)
%title('Klee Diagram - Threshold=1 mm ')
%% occ freq by cal day
clear ndayperCalDay F xx Fstd Fo algo upper_limit lower_limit
for iday=1:lseas
for k=1:nclust
clear kk
kk=find(indx(iday,:)==k); %full period
ndayperCalDay(iday,k)=length(kk);
end
end
fn=11;
a = 1;
b = ones(1,fn)/fn;
F = filter(b,a,ndayperCalDay);
F=cat(1,NaN*ones((fn-1)/2,nclust),F(fn:end,:),NaN*ones((fn-1)/2,nclust));
% Fstd=nanstd(F,0,1);
%
% % Approximative
% for k =1:nclust
% upper_limit(:,k)=F(:,k)+0.83*Fstd(k);
% lower_limit(:,k)=F(:,k)-0.83*Fstd(k);
% end
% %F=filter0(ndayperCalDay,[inf 20]);
figure(77); clf
xx=1:90;
%col=colormap('default');
coll=colormap(cmap);
%Re-ordering:
for k1=1:nclust
Fo(:,k1) = F(:,kkplot(k1));
end
colormap((b2r(-5,5)))
bar(Fo,'stack')
legend('1','2','3','4','5','6','7','8')
set(gca,'FontSize',14)
xlabel('Calendar Day'); ylabel('Frequency (years)')
xlim([6 84])
ylim([0 nseas])
%set(gca,'XTick',1:5:nseas)
%set(gca,'XTickLabel',{'1981','1986','1991','1996','2001','2006','2011','2016'})
%title('Occupation Frequency by Year')
grid off
%Transition Probabilities
indx=K(1:ndat2,nclust);
clear nday
for k=1:nclust
clear kk
kk=find(indx==k);
nday(k)=length(kk);
end
%State transition probabilities
%chunk into 2 day sequences
temp1=reshape(K(1:ndat2,nclust),2,ndat2/2); % cols are 2-day seqs
temp2=reshape(K(2:ndat2-1,nclust),2,(ndat2-2)/2); % same, staggered by 1 day
for k1=1:nclust
for k2=1:nclust
tr(k1,k2) = length(find(temp2(1,:)==k1 & temp2(2,:)==k2)); ...
end
end
for k1=1:nclust
for k2=1:nclust
ntran(k1,k2) = length(find(temp1(1,:)==k1 & temp1(2,:)==k2)) ...
+ length(find(temp2(1,:)==k1 & temp2(2,:)==k2)); % k1 to k2
end
end
colsum=sum(ntran,1);
rowsum=sum(ntran,2);
for k1=1:nclust
for k2=1:nclust
ptran(k1,k2) = round(100*ntran(k1,k2)/rowsum(k1));
end
end
%Re-ordering:
for k1=1:nclust
for k2=1:nclust
ptrano(k1,k2) = ptran(kkplot(k1),kkplot(k2));
end
end
coll=colormap;
figure(8); clf
hb=bar3(ptrano)
colormap(jet)
for i = 1:length(hb)
zdata = get(hb(i),'Zdata');
set(hb(i),'Cdata',zdata)
end
axis([0.5 5.5 0.5 5.5 0 90])
set(gca,'FontSize',15)
title('Transition Matrix')
save -v7.3 stats_obs_ICTP_SPEEDY.mat ptrano ndayperseaso indxo Fo
%% SEASONAL SOURCES OF PREDICTABILITY:
%% SST composites for DJF when WT freq exceeds p-th percentile
clear sst sstsig Xsst Ysst sstcomp sstcompa ssty
iridl=['http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.ERSST/.version4/.sst/T/%28' num2str(yeari-1) '-' num2str(yeare) '%29VALUES/T/%28' seasons '-' seasone '%29seasonalAverage/dods'];
ncid = netcdf.open ( iridl );
%[name,xtype,dimids,natts] = netcdf.inqVar(ncid,1);
%name
sst = double(ncread(iridl,'sst'));;
Xsst= double(ncread(iridl,'X'));
Ysst=double(ncread(iridl,'Y'));
%
p=80; %percentile!!
thresh = prctile(ndayperseas,p);
%for k=6:6%nclust
for k=1:nclust
iyears{k} = find(ndayperseas(:,k)>thresh(k));
sstcomp(:,:,k)=mean(sst(:,:,1,iyears{k}),4);
sstcompa(:,:,k)=mean(sst(:,:,1,iyears{k}),4) - mean(sst(:,:,1,:),4);
end
% %%------------------------------------------
% %UNCOMMENT IF T-TEST IS DESIRED
% sig=0.05; %confidence limit
% for k=1:nclust
% iyears{k} = find(ndayperseas(:,k)>thresh(k));
% %Simple t-test for statistical significance of anomalies (comparing to
% %null anomaly)
% ssty=squeeze(squeeze(sst(:,:,1,iyears{k}))); %SST!!!, not anomalies.
% for ilon=1:length(Xsst)
% for ilat=1:length(Ysst)
% sstsig(ilon,ilat,k)=1-ttest(ssty(ilon,ilat,:)-mean(sst(ilon,ilat,1,:),4),0.,'Alpha',sig);
% end
% end
% end
% %%%------------------------------------------
%%%------------------------------------------
%%UNCOMMENT IF HYPERGEOMETRIC TEST IS DESIRED
%Hypergeometric test of statistical significance of anomalies
%Ingredients:
%Zeroth: confidence limit
sig=0.05;
%First: population size
%dime=size(T);
pop=ndat2;%dime(1);
%Second: number of events for each gridbox in POPULATION (event: anom>0,
%anom<0, etc): Kevp_p,n (p for positive, n for negative)
clear Kevp_p Kevp_n
for ilon=1:length(Xsst)
for ilat=1:length(Ysst)
Kevp_p(ilon,ilat)=nansum(squeeze(squeeze(sst(ilon,ilat,1,:)))-mean(sst(ilon,ilat,1,:),4)>0); %counting positive anom in POP
Kevp_n(ilon,ilat)=nansum(squeeze(squeeze(sst(ilon,ilat,1,:)))-mean(sst(ilon,ilat,1,:),4)<0); %counting negative anom in POP
end
end
%Third: sample size ( this is just: length(iyears{k}), k=1..nclust ).
%AND
%Fourth: number of events for each gridbox in SAMPLE (event: anom>0, anom<0,
%etc): : Kevs_p,n (p for positive, n for negative)
clear sample Kevs_p Kevs_n sstsig_p sstsig_n sstsig
for k=1:nclust
iyears{k} = find(ndayperseas(:,k)>thresh(k));
sample(k) = length(iyears{k});
ssty=squeeze(squeeze(sst(:,:,1,iyears{k}))); %SST!!!, not anomalies.
for ilon=1:length(Xsst)
for ilat=1:length(Ysst)
Kevs_p(ilon,ilat,k)=nansum(ssty(ilon,ilat,:)-mean(sst(ilon,ilat,1,:),4)>0); %counting positive anom in SAMP
Kevs_n(ilon,ilat,k)=nansum(ssty(ilon,ilat,:)-mean(sst(ilon,ilat,1,:),4)<0); %counting negative anom in SAMP
%Hypergeometric!
% probability of getting at least Kevs_p,n occurrences of the phenomenon --by
% chance-- out of a set of n events P(X>=Kevs|pop,Kevp,sampl):
sstsig_p(ilon,ilat,k)=1-hygecdf(Kevs_p(ilon,ilat,k)-1,pop,Kevp_p(ilon,ilat),sample(k)); %positives
sstsig_n(ilon,ilat,k)=1-hygecdf(Kevs_n(ilon,ilat,k)-1,pop,Kevp_n(ilon,ilat),sample(k)); %negatives
%Proceed to mask
%positives:
if(sstsig_p(ilon,ilat,k)<=sig)
sstsig_p(ilon,ilat,k)=1;
else
sstsig_p(ilon,ilat,k)=NaN;
end
%negatives:
if(sstsig_n(ilon,ilat,k)<=sig)
sstsig_n(ilon,ilat,k)=1;
else
sstsig_n(ilon,ilat,k)=NaN;
end
%final:
if((sstsig_p(ilon,ilat,k)+sstsig_n(ilon,ilat,k))==2)
sstsig(ilon,ilat,k)=1;
else
sstsig(ilon,ilat,k)=NaN;
end
end
end
end
clear sstsig_n sstsig_p
%%%End of Hypergeom test
%%------------------------------------------
%So sstsig=1 means statistically significant anomalies.
%%%PLOTS
Xmat=repmat(Xsst',length(Ysst),1)'; Ymat=repmat(Ysst,1,length(Xsst))';
figure(10); clf
colormap((b2r(-0.5,0.5)))
for kplot=1:nclust
map=squeeze(sstcompa(:,:,kkplot(kplot)));
%following two lines are used to plot stat sig values
map=squeeze((sstsig(:,:,kkplot(kplot))).*map);
map(map==0)=NaN; %masking
subplot(1,nclust,kplot)
m_proj('Equidistant Cylindrical','longitudes',[120 360],'latitudes',[-30 70]);
m_coast('patch',[.7 .7 .7],'edgecolor','none');
m_grid;
hold on
[cs,h]=m_contourf(Xmat,Ymat,map,[-1:0.1:1]); caxis([-.5 .5]); %colorbar
% clabel(cs,h,'manual');
title(['WT ' num2str(kplot) ' (' num2str(length(iyears{kkplot(kplot)})) ' years)' ])
end
h=colorbar;
set(h, 'Position', [.92 .42 .02 .2])
%%
%%Indices
%ENSO - Niño3.4
another=['http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/.NINO34/T/%28' num2str(yeari-1) '-' num2str(yeare) '%29VALUES/T/%28' seasons '-' seasone '%29seasonalAverage/dods'];
ncid = netcdf.open ( another );
%[name,xtype,dimids,natts] = netcdf.inqVar(ncid,1);
%name
NINO34 = netcdf.getVar(ncid,1);
%n34=reshape(NINO34,3,nseas);
n34m=NINO34';
%n34m=squeeze(mean(n34));
clear ENSOcorr PVAL ENSOcorro
for k=1:nclust
[ENSOcorr(k), PVAL(k)]=corr(n34m',ndayperseas(1:nseas,k));
%[ENSOcorr(k), PVAL(k)]=corr(n34m(end-60:end)',ndayperseas(end-60:end,k))
end
%% bootstrapping - scramble Nino34 timeseries
nsamp=10000; clear signif signifo
for i=1:nsamp
ransample=n34m(randperm(nseas));
for k=1:nclust
ENSOcorrBoot(k,i)=corr(ransample',ndayperseas(1:nseas,k));
end
end
for k=1:nclust
ENSOcorrBoot_2p5(k)=(prctile(ENSOcorrBoot(k,:)',2.5));
ENSOcorrBoot_5(k)=(prctile(ENSOcorrBoot(k,:)',5));
ENSOcorrBoot_95(k)=(prctile(ENSOcorrBoot(k,:)',95));
ENSOcorrBoot_97p5(k)=(prctile(ENSOcorrBoot(k,:)',97.5));
% ENSOcorrBoot_2p5(k)=(prctile(ENSOcorrBoot(k,:)',10));
% ENSOcorrBoot_5(k)=(prctile(ENSOcorrBoot(k,:)',5));
% ENSOcorrBoot_95(k)=(prctile(ENSOcorrBoot(k,:)',95));
% ENSOcorrBoot_97p5(k)=(prctile(ENSOcorrBoot(k,:)',90));
if(ENSOcorr(k)<ENSOcorrBoot_5(k) || ENSOcorr(k)>ENSOcorrBoot_95(k))
signif{k}='*';
else
signif{k}=' ';
end
if(ENSOcorr(k)<ENSOcorrBoot_2p5(k) || ENSOcorr(k)>ENSOcorrBoot_97p5(k))
signif{k}='**';
end
end
figure(11); clf
subplot(1,3,1);
%reordering:
for kk=1:nclust
ENSOcorro(kk)=ENSOcorr(kkplot(kk));
end
bar(ENSOcorro,'FaceColor', [0.5 0.5 0.5])
for kk=1:nclust
text(kk-0.3,0.45,signif{kkplot(kk)},'FontSize',18)
end
set(gca,'FontSize',14)
xlabel('WT'); ylabel('Niño3.4 Correlation' )
axis([0 nclust+1 -.5 .5])
set(gca,'YTick',[-.5:.1:.5])
%title('Correlations between Nino3.4 (NDJ) and WT Freq. ')
%grid on
%orient landscape
%print -dpdf CorrelENSO_OBS.pdf
%
%PNA
another=['http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.CPC_Indices/.NHTI/.PNA/T/%28' num2str(yeari-1) '-' num2str(yeare) '%29VALUES/T/%28' seasons '-' seasone '%29seasonalAverage/dods'];
ncid = netcdf.open ( another );
%[name,xtype,dimids,natts] = netcdf.inqVar(ncid,1);
%name
NINO34 = netcdf.getVar(ncid,1);
%n34=reshape(NINO34,3,nseas);
n34m=NINO34';
%n34m=squeeze(mean(n34));
clear ENSOcorr PVAL ENSOcorro
for k=1:nclust
[ENSOcorr(k), PVAL(k)]=corr(n34m',ndayperseas(1:nseas,k));
%[ENSOcorr(k), PVAL(k)]=corr(n34m(end-60:end)',ndayperseas(end-60:end,k))
end
%% bootstrapping - scramble timeseries
nsamp=10000; clear signif signifo
for i=1:nsamp
ransample=n34m(randperm(nseas));
for k=1:nclust
ENSOcorrBoot(k,i)=corr(ransample',ndayperseas(1:nseas,k));
end
end
for k=1:nclust
ENSOcorrBoot_2p5(k)=(prctile(ENSOcorrBoot(k,:)',2.5));
ENSOcorrBoot_5(k)=(prctile(ENSOcorrBoot(k,:)',5));
ENSOcorrBoot_95(k)=(prctile(ENSOcorrBoot(k,:)',95));
ENSOcorrBoot_97p5(k)=(prctile(ENSOcorrBoot(k,:)',97.5));
% ENSOcorrBoot_2p5(k)=(prctile(ENSOcorrBoot(k,:)',10));
% ENSOcorrBoot_5(k)=(prctile(ENSOcorrBoot(k,:)',5));
% ENSOcorrBoot_95(k)=(prctile(ENSOcorrBoot(k,:)',95));
% ENSOcorrBoot_97p5(k)=(prctile(ENSOcorrBoot(k,:)',90));
if(ENSOcorr(k)<ENSOcorrBoot_5(k) || ENSOcorr(k)>ENSOcorrBoot_95(k))
signif{k}='*';
else
signif{k}=' ';
end
if(ENSOcorr(k)<ENSOcorrBoot_2p5(k) || ENSOcorr(k)>ENSOcorrBoot_97p5(k))
signif{k}='**';
end
end
subplot(1,3,2);
%reordering: