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Merge branch 'master' into 'master' 

The templates for jobs and rc-files used by start_ctdas.sh are now in a...

See merge request ctdas/CTDAS!10
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stilt.nc4.sib.co2.Feb.2015.May13.r deleted 100755 → 0
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# CarbonTracker Data Assimilation Shell (CTDAS) Copyright (C) 2017 Wouter Peters.
# Users are recommended to contact the developers (wouter.peters@wur.nl) to receive
# updates of the code. See also: http://www.carbontracker.eu.
#
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# version 3. This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with this
# program. If not, see <http://www.gnu.org/licenses/>.
#------------------------------------------------------------------------------------------------
# CO2 concentration simulation based on WRF-STILT/HYSPLIT-NAM12 footprints and
# SiB3/4 biosphere fluxes, CarbonTracker background fluxes and boundary conditions
#
# created by W.He on Feb 20, 2015
#------------------------------------------------------------------------------------------------
source("/Users/wei/co2simu/Rcode/lpdm/rsource/load.ncdf4.r")
source("/Users/wei/co2simu/Rcode/empirical_boundary/src/id2info.r")
source("/Users/wei/co2simu/Rcode/empirical_boundary/src/julian.r")
source("/Users/wei/co2simu/Rcode/rsource/time.r")
source("/Users/wei/co2simu/Rcode/rsource/ddate.r")
source("/Users/wei/co2simu/Rcode/rsource/datelist.r")
source("/Users/wei/co2simu/Rcode/stilt-cvs-20090417/stiltR/sourceall.r")
source("/Users/wei/co2simu/Rcode/stilt-cvs-20090417/stiltR/Trajecfoot.r")
source("/Users/wei/co2simu/Rcode/stilt-cvs-20090417/stiltR/getgridp.r")
source("/Users/wei/co2simu/Rcode/rsource/assignr.r")
source("/Users/wei/co2simu/Rcode/output.ncdf4.r")
# #inputs and outputs
# sibdir="/Users/weihe/Documents/Concensimulation/sib3_fluxes/day1×1/"
# bgfdir="/Users/weihe/Documents/Concensimulation/bgfluxes/3-hourly/"
# footpdir="/Users/weihe/Documents/Concensimulation/stilt_footprints/" #read arlyn's calculated data
# bounddir="/Users/weihe/Documents/Concensimulation/co2_boundary/co2_total/"
# outdir="/Users/weihe/Documents/Concensimulation/resultsforctdas/"
#
# #scaling factors for 10-days time scale
# samdir="/Users/weihe/Documents/example_ctdas/input/"
stiltfile="stilt.rc"
curdir="/Storage/CO2/wei/ctdas-stilt-proto/exec/da/stilt/"
fn=paste(curdir, stiltfile,sep="")
conf=as.matrix(read.table(fn,header=FALSE))
sibdir = conf[1,3]
bgfdir = conf[2,3]
footpdir= conf[3,3]
bounddir= conf[4,3]
outdir = conf[5,3] #output
samdir = conf[6,3] #input
#----------------------------------------------------------------------------------------------
#Convolve footprints with sib hourly fluxes, for observations from both Aircraft and towers
#----------------------------------------------------------------------------------------------
endtime=240
foottimes=seq(0,endtime,1) #vector of times (backtimes) in hours between which footprint is computed
zbot=0 #lower vertical bound for influence projection, in meters agl
ztop=0 #upper vertical bound for influence projection, in meters agl
#if ztop set to zero, *surface* influence will be calculated
#set up an equivalent domain among footprints,fluxes, but not CO2 boundary
ncol2=66 #number of pixels in x directions in grid
nrow2=40 #number of pixels in y directions in grid
LLLon2=(-129) #lower left corner of grid
LLLat2=22 #lower left corner of grid
ResLon2=1 #resolution in degrees longitude
ResLat2=1 #resolution in degrees latitude
# constants for level-height transfrom
levelhgt=c(34.5,111.9,256.9,490.4,826.4,1274.1,1839.0,2524.0,3329.9,4255.6,5298.5,6453.8,7715.4,9076.6,
+ 10533.3,12108.3,13874.2,15860.1,18093.2,20590.0,24247.3,29859.6,35695.0,42551.5,80000.0)
#data from http://www.esrl.noaa.gov/gmd/ccgg/carbontracker-ch4/documentation_tm5.html
TBegin <- proc.time()
library(ncdf4)
#--------------------------------------------------------------------------------------------------------
#library(futile.logger)
#flog.appender(appender.file("/Users/weihe/Documents/Concensimulation/resultstest/stilt.log"), name='logger.b')
#flog.info("This writes to a logging %s", "file", name='logger.b')
#flog.warn("This statement has higher severity", name='logger.b')
#flog.fatal("This one is really scary", name='logger.b')
#--------------------------------------------------------------------------------------------------------
# site level: read scaling factors for all sites once
# member level: read boundary for all ensemble members once
#nsam= 150
#ndayscycle=10 # one cycle of footprints
#year=2010
#month=1
#day=19
nsam=as.numeric(conf[7,3]) #get from stilt.txt file =>replace with a dictionary style, not id order; also check parameters and report status
ndayscycle=as.numeric(conf[8,3])
startdate=conf[9,3]
year=as.numeric(substring(startdate,1,4))
month=as.numeric(substring(startdate,6,7))
day=as.numeric(substring(startdate,9,10))
#if(ndayscycle!=10)
# flog.warn("Sorry, only support the case nndayscycle equals 10", name='logger.b')
b2 = ISOdatetime(year,month,day,0,0,0,tz="UTC")
b1 = b2 - ndayscycle*24*3600 # for footprint
b3 = b1 + 2*ndayscycle*24*3600 # for flux
tb1 = paste(substring(b1,1,4),substring(b1,6,7),substring(b1,9,10),sep="")
tb2 = paste(substring(b2,1,4),substring(b2,6,7),substring(b2,9,10),sep="")
tb3 = paste(substring(b3,1,4),substring(b3,6,7),substring(b3,9,10),sep="")
scalefacarr1=array(NA, dim=c(ncol2,nrow2,nsam))
scalefacarr2=array(NA, dim=c(ncol2,nrow2,nsam))
# make CTDAS to generate domain for North America, read data partly?
#flog.info("Reading scaling factor files", name='logger.b')
for(i in 0:(nsam-1)) #parameters.000.2010010100_2010011100.nc
{
if (i<10)
ii = paste("00",i,sep="")
if (i<100 && i>=10)
ii = paste("0",i,sep="")
if (i>=100)
ii = i
ncf <- nc_open(paste(samdir,"parameters.",ii,".",tb1,"00","_",tb2,"00",".nc",sep=""))
scalefac <- ncvar_get(ncf,"parametermap",start=c(52,113),count=c(ncol2,nrow2)) #temporary 42 #real52:117,113:152,start=c(52,113),count=c(66,40)
scalefacarr1[,,i+1]=scalefac
ncf <- nc_open(paste(samdir,"parameters.",ii,".",tb2,"00","_",tb3,"00",".nc",sep=""))
scalefac <- ncvar_get(ncf,"parametermap",start=c(52,113),count=c(ncol2,nrow2))
scalefacarr2[,,i+1]=scalefac
}
#---------------------------------------------------------------------------------
# Centering at the state vector especially the ready-optimized cycle, look for data
# from corresponding period for optimzation
#---------------------------------------------------------------------------------
# according to b1, b2, decide which months
m1=substring(b2,6,7)
m2=substring(b3,6,7)
umon=unique(c(m1,m2))
#flog.info("Determing ready-use footprint files", name='logger.b')
fns=NULL
for(mm in 1:length(umon))
{
pfbpath=paste(footpdir,year,"/",umon[mm],"/",sep="") #"stilt2010x01x01x18x59x33.4057Nx081.8334Wx00285.nc"
tmp=list.files(pfbpath,pattern="nc", all=T)
fns=c(fns,tmp)
}
# read needed event ids from sample_coordinates.***.nc files(e.g, sample_coordinates_2010011100_2010012100.nc)
ncf <- nc_open(paste(samdir,"sample_coordinates_",tb2,"00","_",tb3,"00",".nc",sep=""))
eventidarr <- ncvar_get(ncf,"obs_num")
# filter before loop
pfbfns=NULL
fn=NULL
for(mm in 1:length(fns))
{
#select: Uniform time to make it start from the same point
#yr=substring(fns[mm],6,9) #"2010-01-09 09:00:00 UTC"
#mo=substring(fns[mm],11,12)
#dy=substring(fns[mm],14,15)
#bISO = ISOdate(yr,mo,dy)
#diff1=as.numeric(bISO - b2)
#diff2=as.numeric(bISO - b3)
#if (diff1 >= 0 && diff2 <= 0) # in the time span
# pfbfns = c(pfbfns,fns[mm])
eid=as.numeric(substring(fns[mm],48,53))
for(i in 1:length(eventidarr))
{
if(eid==eventidarr[i])
{
pfbfns = c(pfbfns,fns[mm])
break
}
}
}
#log=paste("For state vectors from ",b1,"to",b2,",",length(pfbfns),"observations have been found")
#flog.info(log, name='logger.b')
# for fluxes, the current 10 days using scaling factors
#flog.info("Start convolving for all footprint files", name='logger.b')
newfile=T #output results into a new file
for(mm in 1:length(pfbfns))
{
# Read pfb file
#mm=1
mo=substring(pfbfns[mm],11,12)
fn=paste(footpdir,year,"/",mo,"/",pfbfns[mm],sep="")
footp=load.ncdf(fn)
footnc <- nc_open(fn)
foot <- ncvar_get(footnc,"foot1",start=c(41,12,1),count=c(ncol2,nrow2,-1))
# Read footprint directly
#ft=footp$foot1 #120*70*240
#foot=array(NA, dim=c(40,66,240))
#for (i in 1:240)
# foot[,,i]=t(as.matrix(ft[41:106,12:51,i])) #113:152
# get info form path srings
ident=substring(pfbfns[mm],6,46)
eventid=substring(pfbfns[mm],48,53)
print(ident)
# flog.info("Convolving for %s",ident, name='logger.b')
info=id2info(ident) #interpret info from file names
#--------------------------------------------------------
# STEP 1: Creates footprint for individual particle runs
#--------------------------------------------------------
#foot1=Trajecfoot(ident=ident,part=part, pathname="",foottimes=foottimes,zlim=c(zbot,ztop),fluxweighting=NULL,coarse=1,vegpath=vegpath,
# numpix.x=ncol1,numpix.y=nrow1,lon.ll=LLLon1,lat.ll=LLLat1,lon.res=ResLon1,lat.res=ResLat1)
# for different spatial resolutions between biosphere fluxes and background fluxes, two footprints are planned to be used, and to the end, we picked up the concentration values.
#foot2=Trajecfoot(ident=ident,part=part, pathname="",foottimes=foottimes,zlim=c(zbot,ztop),fluxweighting=NULL,coarse=1,vegpath=vegpath,
# numpix.x=ncol2,numpix.y=nrow2,lon.ll=LLLon2,lat.ll=LLLat2,lon.res=ResLon2,lat.res=ResLat2)
if(length(foot)>100)
{
inityr=as.numeric(substring(ident,1,4))
initmo=as.numeric(substring(ident,6,7))
initdy=as.numeric(substring(ident,9,10))
inithr=as.numeric(substring(ident,12,13))
initmi=as.numeric(substring(ident,15,16)) #by He
inihgt=as.numeric(substring(ident,39,41))
# get the time stamp for each foot step, going back time given by "endtime"
xx=ISOdatetime(inityr,initmo,initdy,inithr,initmi,0,tz="UTC")
yy=xx+(-foottimes*3600)
cyy=as.character(yy)
yrlist=substring(cyy,1,4)
monlist=substring(cyy,6,7)
daylist=substring(cyy,9,10)
hrlist=substring(cyy,12,13)
milist=substring(cyy,15,16)
# get unique months and days
daystring=paste(yrlist,monlist,daylist, sep="")
udaystring=unique(daystring)
yrmonstring=paste(yrlist,monlist, sep="")
uyrmonstring=unique(yrmonstring)
#kk=length(uyrmonstring) #needs July (former month)
#current month
sibyr=substring(uyrmonstring[1],1,4)
sibmon=substring(uyrmonstring[1],5,6)
#----------------------------------------------------------------------------------
# STEP 2: Read boundary conditions & use end points tracing to
# 1) get responding fluxes & convolve with footprints
# 2) get the "actural" concentrations
#----------------------------------------------------------------------------------
# get endpoints
endpts=footp$endpts
endptna=footp$endptsnames
#endpts=ncvar_get(footnc,"endpts")
#endptna=ncvar_get(footnc,"endptsnames")
colnames(endpts)=endptna #=c("time","index","lat","lon","agl","grdht","temp","pres")
endptsdate=footp$endptsdate #2010-08-04 20:18:00 UTC
#endptsdate=ncvar_get(footnc,"endptsdate")
latarr=endpts[,3]
lonarr=endpts[,4]
hgtarr=endpts[,5] + endpts[,6] #agl+grdht
# analyze the dates to recognize how many data to read
dd=as.character(endptsdate)
yrlist=substring(dd,1,4)
monlist=substring(dd,6,7)
daylist=substring(dd,9,10)
hrlist=substring(dd,12,13)
milist=substring(dd,15,16)
# get unique months and days
daystring=paste(yrlist,monlist,daylist, sep="")
udaystring=unique(daystring)
yrmonstring=paste(yrlist,monlist, sep="")
uyrmonstring=unique(yrmonstring)
#--------------------------------------------------------------
# 2-1: Read fluxes and boundary data
#--------------------------------------------------------------
ndays=length(udaystring)
bouarr=array(0,dim=c(ndays,120,90,25,8)) #boundary : lon,lat,hgt,time
#biospheric fluxes
nrow_flux1=181
ncol_flux1=361 #288
gpp=array(NA, dim=c(ndays,ncol_flux1,nrow_flux1,24))
rec=array(NA, dim=c(ndays,ncol_flux1,nrow_flux1,24))
pla=array(NA, dim=c(ndays,ncol_flux1,nrow_flux1,24))
soi=array(NA, dim=c(ndays,ncol_flux1,nrow_flux1,24))
#other fluxes #(360,180,8)
nrow_flux2=180
ncol_flux2=360
ocn=array(NA, dim=c(ndays,ncol_flux2,nrow_flux2,8))
fos=array(NA, dim=c(ndays,ncol_flux2,nrow_flux2,8))
fir=array(NA, dim=c(ndays,ncol_flux2,nrow_flux2,8))
ntimes1=ndays*24
ntimes2=ndays*8
for(d in 1:ndays)
{
datestr=udaystring[d]
yr=substr(datestr,1,4)
mn=substr(datestr,5,6)
dy=substr(datestr,7,8)
#bou=load.ncdf(paste(bounddir,"CT2013B.molefrac_nam1x1_",yr,"-",mn,"-",dy,".nc",sep=""))
bou=load.ncdf(paste(bounddir,"CT2013B.molefrac_glb3x2_",yr,"-",mn,"-",dy,".nc",sep=""))
# co2(date, level, lat, lon) / ocn_flux_opt(date, lat, lon)
biof=load.ncdf(paste(sibdir,"SiB3.hourly.flux1x1.global.",yr,mn,dy,".nc",sep=""))
bgf=load.ncdf(paste(bgfdir,"CT2013B.flux1x1.",yr,mn,dy,".nc",sep=""))
bouarr[d,,,,]=bou$co2 # boundary CO2
gpp[d,,,]=biof$gpp
rec[d,,,]=biof$rtotal
pla[d,,,]=biof$ocs.gpp
soi[d,,,]=biof$ocs.soil
ocn[d,,,]=bgf$ocn.flux.opt # here we can read less, so change the sizes of ocnarr
fos[d,,,]=bgf$fossil.flux.imp
fir[d,,,]=bgf$fire.flux.imp
remove(list=c("bou","biof","bgf"))
}
#--------------------------------------------------------------
# 2-2: prepare data for calculations
#--------------------------------------------------------------
dateall1=rep(ISOdatetime(0,0,0,0,0,0,tz="UTC"), ntimes1)
dateall2=rep(ISOdatetime(0,0,0,0,0,0,tz="UTC"), ntimes2)
gppflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
recflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
plaflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
soiflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
neeflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
neeflux1=array(NA, dim=c(ncol2,nrow2,ntimes1))
#----------------------------------------------------
neefluxarr=array(NA, dim=c(ncol2,nrow2,ntimes1,nsam))
neeoutarr=array(NA, dim=c(nsam))
fsimuarr=array(NA, dim=c(nsam))
#----------------------------------------------------
ocnflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
fosflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
firflux=array(NA, dim=c(ncol2,nrow2,ntimes1))
yr=rep(sibyr,ntimes1)
mon=rep(sibmon,ntimes1)
hrs=seq(1,ntimes1,1)
dy=ceiling(hrs/24)
hr=(hrs-(dy-1)*24)*1-1
time1=ISOdatetime(yr,mon,dy,hr,0,0,tz="UTC")
yr=rep(sibyr,ntimes2)
mon=rep(sibmon,ntimes2)
hrs=seq(1,ntimes2,1)
dy=ceiling(hrs/8)
hr=(hrs-(dy-1)*8)*3-1
time2=ISOdatetime(yr,mon,dy,hr,0,0,tz="UTC")
for(hh in ntimes1:1)
{
dateall1[ntimes1-hh+1]=time1[hh]
inxd=ceiling(hh/24)
inxh=hh-(inxd-1)*24
gppflux[,,ntimes1-hh+1]=gpp[inxd,52:117,113:152,inxh] #transform matrix, 41:93; delete this on May 4,2015
recflux[,,ntimes1-hh+1]=rec[inxd,52:117,113:152,inxh]
plaflux[,,ntimes1-hh+1]=pla[inxd,52:117,113:152,inxh]
soiflux[,,ntimes1-hh+1]=soi[inxd,52:117,113:152,inxh]
}
neeflux[,,]=recflux[,,]-gppflux[,,]
for(hh in ntimes2:1)
{
dateall2[ntimes2-hh+1]=time2[hh]
inxd=ceiling(hh/8)
inxh=hh-(inxd-1)*8
ocnflux[,,ntimes2-hh+1]=ocn[inxd,52:117,113:152,inxh] #transform matrix, delete this
fosflux[,,ntimes2-hh+1]=fos[inxd,52:117,113:152,inxh]
firflux[,,ntimes2-hh+1]=fir[inxd,52:117,113:152,inxh]
}
#--------------------------------------------------------------
# 2-3: Convolving footprints with fluxes to get delta co2
#--------------------------------------------------------------
# sib3 flux, 1-hourly
datevalid=dateall1[1]+0.5*3600-(0:(ntimes1-1))*3600 #need to change, ntimes=1488
ixflux=(1:ntimes1)[(datevalid<=xx)][1:endtime] #time backward
#dateall[ixflux]
#datevalid from dateall1, from time1, from SiB flux; xx from from footprint
#need large amount of memories
ocstmp=foot*plaflux[,,ixflux]
soiltmp=foot*soiflux[,,ixflux]
gpptmp=foot*gppflux[,,ixflux]
recotmp=foot*recflux[,,ixflux] #dim(neetmp): 40 53 240
#################################################
# determine which fluxes need scaling factors
#################################################
# involve scaling factors to neeflux[,,ixflux], partly, for example as follow
# xx = "2010-01-24 18:10:00 UTC" || b1 = "2010-01-19 12:00:00 UTC", b3 = "2010-01-29 12:00:00 UTC"
xxleft=xx-ndays*24*3600
for (i in 1:nsam)
{
if(xxleft<b2) #flux with scaling factors
{
diff=as.numeric(difftime(b2,xxleft,units='hours')) # needs scaling for first half
diff=ceiling(diff)
for (hh in 1:(diff))
neeflux1[,,hh] = neeflux[,,hh]*(scalefacarr1[,,i]) #delte "t" transform
for (hh in (diff+1):ntimes1)
neeflux1[,,hh] = neeflux[,,hh]*(scalefacarr2[,,i])
neefluxarr[,,,i] = neeflux1[,,]
}
else
{
for (hh in 1:ntimes1)
neeflux1[,,hh] = neeflux[,,hh]*(scalefacarr2[,,i])
neefluxarr[,,,i] = neeflux1[,,]
}
}
# REMEMBER: the spatial resolutios of the scaling factors and fluxes are different, whiched needs to be uniformed afterwards
# delta co2 on NEE for all ensemble members
for(i in 1:nsam)
{
neetmp=foot*neefluxarr[,,ixflux,i]
neeout=sum(neetmp,na.rm=TRUE)
neeoutarr[i]=neeout
}
#------------------------------------------------
ocsout=sum(ocstmp,na.rm=TRUE)
soilout=sum(soiltmp,na.rm=TRUE)
gppout=sum(gpptmp,na.rm=TRUE)
recoout=sum(recotmp,na.rm=TRUE)
#remove(list=c("gppflux","recflux","soiflux","plaflux","gpptmp","recotmp","soiltmp","ocstmp","dateall1"))
gc()
# bg fluxes, 3-hourly
datevalid=dateall2[1]+1.5*3600-(0:(ntimes2-1))*3*3600
ixflux=(1:ntimes2)[(datevalid<=xx)][1:endtime]
#need large amount of memories
ocntmp=foot*ocnflux[,,ixflux]*1e6
fostmp=foot*fosflux[,,ixflux]*1e6
firtmp=foot*firflux[,,ixflux]*1e6
ocnout=sum(ocntmp,na.rm=TRUE)
fosout=sum(fostmp,na.rm=TRUE)
firout=sum(firtmp,na.rm=TRUE)
#remove(list=c("ocnflux","fosflux","firflux","ocntmp","fostmp","firtmp","foot","dateall2"))
gc()
#--------------------------------------------------------------
# 2-4: Calculate boundary
#--------------------------------------------------------------
# boundary domain : lat 22.5~61.5, lon -128.5~-63.5
latmin=-90 #22.5
latmax=90 #61.5
lonmin=-180 #-128.5
lonmax=180 #-63.5
npts=dim(endpts)[1]
# calulate concentrations
pbou=0
nval=0
dmin=min(as.integer(substr(udaystring,7,8)))
for(p in 1:npts)
{
#if(lonarr[p]>=lonmin && lonarr[p]<=lonmax && latarr[p]>=latmin && latarr[p]<=latmax)
#{
#dy=as.integer(daylist[p])-dmin+1 #bug, who know if the dates are continuous or not
dy=0
goal=as.integer(daylist[p])
for(uu in 1:ndays)
{
dstr=udaystring[uu]
ddy=substr(dstr,7,8)
if(ddy<=goal)
dy=dy+1
}
#i=ceiling((lonarr[p]-lonmin)/1.0+0.5) #check if the position is right
i=ceiling((lonarr[p]-lonmin)/3.0)
#nval=nval+1 #notice!!! this (i,j) is for NA
#notice: we maybe should use the data besides North America domain, not only nam1x1, but glb3x2...
#j=ceiling((latarr[p]-latmin)/1.0+0.5)
j=ceiling((latarr[p]-latmin)/2.0)
# height matching
k0=hgtarr[p] #go upward stair stategy, one by one
k=1
for(l in 1:25)
{
if(k0 > levelhgt[l] && k0 <= levelhgt[25])
k=l+1
if(k0 > levelhgt[25])
k=25
}
# 3-hourly matching /agt/alt
hpos=ceiling((as.integer(hrlist[p])+0.000001+as.integer(milist[p])/60.0 )/3.0)
tt=bouarr[dy,i,j,k,hpos]
if(length(tt)==1) #why sometimes we get a unnormal array?
{
pbou=pbou+tt #sum
nval=nval+1
}
#}
}
#print(nval)
fbou=pbou/nval
#deltaco2=(recoout-gppout)+ocnout+fosout+firout #contributions from the biosphere and background fluxes
#fsimu=fbou+deltaco2
#################################################
# final results and output to files
#################################################
fnameout1=paste(outdir,"/",tb2,"/","samples_simulated.",tb2,"00","_",tb3,"00",".txt",sep="")
fn=fnameout1
outlist=NULL #c(inityr, initmo, initdy, inithr, initmi, inihgt, fbou)
for(i in 1:nsam)
{
deltaco2=neeoutarr[i]+ocnout+fosout+firout
fsimuarr[i]=fbou+deltaco2
outlist<-c(outlist, fsimuarr[i])
}
out1<-c(ident,eventid,round(outlist,4))
if(newfile)
write.table(t(out1), file=fnameout1, append=F, col.names=F, row.names=F, quote=F)
if(!newfile)
write.table(t(out1), file=fnameout1, append=T, col.names=F, row.names=F, quote=F)
newfile=F
}# end if foot NA
}#end loop mm
#-----------------------------------------------------------------------
# ouput rsults as *.nc files
#-----------------------------------------------------------------------
#source("/Users/weihe/Documents/Concensimulation/output.ncdf4.r")
#fn= "/Users/weihe/Documents/Concensimulation/resultstest/stilt_sib_ct_co2_2010-01-19_2010-01-29_simulated.txt"
fin=paste(fn,sep="") #already include eventid
fout=paste(substring(fn,1,nchar(fn)-4),".nc",sep="")
data=as.matrix(read.table(fin,header=FALSE))
nobs=dim(data)[1]
nmem=dim(data)[2]-2
vals=data[,2:(nsam+2)] #151 colums
write.results.netcdf(vals,nobs,nmem,fout) #write simulated results once
#-----------------------------------------------------------------------
#create stilt.ok file
file.create("stilt.ok")
# Time marker
TEnd=proc.time()
Tot<-TEnd-TBegin
print(Tot)
template.jb → templates/template.jb
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template.py → templates/template.py
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......@@ -27,7 +27,7 @@ sys.path.append(os.getcwd())
from da.tools.initexit import start_logger, validate_opts_args, parse_options, CycleControl
from da.tools.pipeline import ensemble_smoother_pipeline, header, footer
from da.platform.maunaloa import MaunaloaPlatform
from da.platform.capegrim import CapeGrimPlatform
from da.baseclasses.dasystem import DaSystem
from da.baseclasses.statevector import StateVector
from da.carbondioxide.obspack_globalviewplus2 import ObsPackObservations
......
template.rc → templates/template.rc
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