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Commit 640e5e92 authored by brunner's avatar brunner
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config.rc 0 → 100644
View file @ 640e5e92
! 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/>.
! author: Wouter Peters
!
! This is a blueprint for an rc-file used in CTDAS. Feel free to modify it, and please go to the main webpage for further documentation.
!
! Note that rc-files have the convention that commented lines start with an exclamation mark (!), while special lines start with a hashtag (#).
!
! When running the script start_ctdas.sh, this /.rc file will be copied to your run directory, and some items will be replaced for you.
! The result will be a nearly ready-to-go rc-file for your assimilation job. The entries and their meaning are explained by the comments below.
!
!
! HISTORY:
!
! Created on August 20th, 2013 by Wouter Peters
!
!
! The time for which to start and end the data assimilation experiment in format YYYY-MM-DD HH:MM:SS
! the following 3 lines are for initial start
time.start : 2019-01-01 00:00:00
time.finish : 2019-01-07 23:00:00
time.end : 2019-01-07 23:00:00
abs.time.start : 2019-01-01 00:00:00
! Whether to restart the CTDAS system from a previous cycle, or to start the sequence fresh. Valid entries are T/F/True/False/TRUE/FALSE
time.restart : F
!da.restart.tstamp : 2013-01-08 00:00:00
da.restart.tstamp : 2013-01-01 00:00:00
! The length of a cycle is given in days, such that the integer 7 denotes the typically used weekly cycle. Valid entries are integers > 1
time.cycle : 7
! The number of cycles of lag to use for a smoother version of CTDAS. CarbonTracker CO2 typically uses 5 weeks of lag. Valid entries are integers > 0
time.nlag : 2
! The directory under which the code, input, and output will be stored. This is the base directory for a run. The word
! '/' will be replaced through the start_ctdas.sh script by a user-specified folder name. DO NOT REPLACE
run.name : real
dir.da_run : /scratch/snx3000/parsenov/${run.name}
restartmap.dir : ${dir.da_run}/input
! The resources used to complete the data assimilation experiment. This depends on your computing platform.
! The number of cycles per job denotes how many cycles should be completed before starting a new process or job, this
! allows you to complete many cycles before resubmitting a job to the queue and having to wait again for resources.
! Valid entries are integers > 0
da.resources.ncycles_per_job : 1
! The ntasks specifies the number of threads to use for the MPI part of the code, if relevant. Note that the CTDAS code
! itself is not parallelized and the python code underlying CTDAS does not use multiple processors. The chosen observation
! operator though might use many processors, like TM5. Valid entries are integers > 0
da.resources.ntasks : 1
! This specifies the amount of wall-clock time to request for each job. Its value depends on your computing platform and might take
! any form appropriate for your system. Typically, HPC queueing systems allow you a certain number of hours of usage before
! your job is killed, and you are expected to finalize and submit a next job before that time. Valid entries are strings.
da.resources.ntime : 44:00:00
! The resource settings above will cause the creation of a job file in which 2 cycles will be run, and 30 threads
! are asked for a duration of 4 hours
!
! Info on the DA system used, this depends on your application of CTDAS and might refer to for instance CO2, or CH4 optimizations.
!
da.system : CarbonTracker
! The specific settings for your system are read from a separate rc-file, which points to the data directories, observations, etc
da.system.rc : da/rc/carbontracker_cosmo.rc
! This flag should probably be moved to the da.system.rc file. It denotes which type of filtering to use in the optimizer
da.system.localization : None
!da.system.localization : CT2007
! Info on the observation operator to be used, these keys help to identify the settings for the transport model in this case
da.obsoperator : cosmo
!
! The TM5 transport model is controlled by an rc-file as well. The value below refers to the configuration of the TM5 model to
! be used as observation operator in this experiment.
!
da.obsoperator.home : /store/empa/em05/parsenov/cosmo_processing_chain
da.vprm : /store/empa/em05/parsenov/cosmo_input/online_vprm
da.obsoperator.rc : ${da.obsoperator.home}/tm5-ctdas-ei-zoom.rc
!forward.savestate.exceptsam : TRUE
!
! The number of ensemble members used in the experiment. Valid entries are integers > 2
!
da.optimizer.nmembers : 21
nparameters : 181
! Finally, info on the archive task, if any. Archive tasks are run after each cycle to ensure that the results of each cycle are
! preserved, even if you run on scratch space or a temporary disk. Since an experiment can take multiple weeks to complete, moving
! your results out of the way, or backing them up, is usually a good idea. Note that the tasks are commented and need to be uncommented
! to use this feature.
! The following key identifies that two archive tasks will be executed, one called 'alldata' and the other 'resultsonly'.
!task.rsync : alldata onlyresults
! The specifics for the first task.
! 1> Which source directories to back up. Valid entry is a list of folders separated by spaces
! 2> Which destination directory to use. Valid entries are a folder name, or server and folder name in rsync format as below
! 3> Which flags to add to the rsync command
! The settings below will result in an rsync command that looks like:
!
! rsync -auv -e ssh ${dir.da_run} you@yourserver.com:/yourfolder/
!
!task.rsync.alldata.sourcedirs : ${dir.da_run}
!task.rsync.alldata.destinationdir : you@yourserver.com:/yourfolder/
!task.rsync.alldata.flags g -auv -e ssh
! Repeated for rsync task 2, note that we only back up the analysis and output dirs here
!task.rsync.onlyresults.sourcedirs : ${dir.da_run}/analysis ${dir.da_run}/output
!task.rsync.onlyresults.destinationdir : you@yourserver.com:/yourfolder/
!task.rsync.onlyresults.flags : -auv -e ssh
cosmo.py 0 → 100644
View file @ 640e5e92
"""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/>."""
#!/usr/bin/env python
#################################################################################################
# First order of business is always to make all other python modules accessible through the path
#################################################################################################
import sys
import os
import logging
sys.path.append(os.getcwd())
#################################################################################################
# Next, import the tools needed to initialize a data assimilation cycle
#################################################################################################
from da.tools.initexit import start_logger, validate_opts_args, parse_options, CycleControl
from da.cosmo.pipeline import ensemble_smoother_pipeline, header, footer
from da.platform.maunaloa import MaunaloaPlatform
from da.baseclasses.dasystem import DaSystem
from da.cosmo.covariances import CO2StateVector
from da.cosmo.observations_cosmo import ObsPackObservations
from da.cosmo.optimizer import CO2Optimizer
from da.cosmo.observationoperator_cosmo import ObservationOperator
#from da.cosmo.expand_fluxes import save_weekly_avg_1x1_data, save_weekly_avg_state_data, save_weekly_avg_tc_data, save_weekly_avg_ext_tc_data
#from da.analysis.expand_molefractions import write_mole_fractions
#################################################################################################
# Parse and validate the command line options, start logging
#################################################################################################
start_logger()
#start_logger(level=logging.DEBUG)
opts, args = parse_options()
opts, args = validate_opts_args(opts, args)
#################################################################################################
# Create the Cycle Control object for this job
#################################################################################################
dacycle = CycleControl(opts, args)
platform = MaunaloaPlatform()
dasystem = DaSystem(dacycle['da.system.rc'])
obsoperator = ObservationOperator(dacycle['da.obsoperator.rc'])
#samples = Obs()
samples = ObsPackObservations()
statevector = CO2StateVector()
optimizer = CO2Optimizer()
##########################################################################################
################### ENTER THE PIPELINE WITH THE OBJECTS PASSED BY THE USER ###############
##########################################################################################
logging.info(header + "Entering Pipeline " + footer)
ensemble_smoother_pipeline(dacycle, platform, dasystem, samples, statevector, obsoperator, optimizer)
##########################################################################################
################### All done, extra stuff can be added next, such as analysis
##########################################################################################
logging.info(header + "All done. God bless" + footer)
sys.exit(0)
save_weekly_avg_1x1_data(dacycle, statevector)
save_weekly_avg_state_data(dacycle, statevector)
save_weekly_avg_tc_data(dacycle, statevector)
save_weekly_avg_ext_tc_data(dacycle)
write_mole_fractions(dacycle)
sys.exit(0)
da/cosmo/observationoperator_cosmo.py 0 → 100755
View file @ 640e5e92
#!/usr/bin/env python
# model.py
import logging
import os
import sys
import subprocess
import da.cosmo.io4 as io
import numpy as np
from netCDF4 import Dataset
from datetime import datetime, timedelta
from dateutil import rrule
from cdo import *
from . import site_height
from da.cosmo.icbc4ctdas import ct
from itertools import repeat
from multiprocessing import Pool
from da.tools.general import to_datetime
identifier = 'ObservationOperator'
version = '10'
#cdo = Cdo()
cdo = Cdo(logging=True, logFile='cdo_commands.log')
################### Begin Class ObservationOperator ###################
class ObservationOperator(object):
def __init__(self, dacycle=None):
self.ID = identifier
self.version = version
self.restart_filelist = []
self.output_filelist = []
self.outputdir = None # Needed for opening the samples.nc files created
logging.info('Observation Operator object initialized: %s' % self.ID)
if dacycle != None:
self.dacycle = dacycle
else:
self.dacycle = {}
def get_initial_data(self):
""" This method places all initial data needed by an ObservationOperator in the proper folder for the model """
def setup(self,dacycle):
""" Perform all steps necessary to start the observation operator through a simple Run() call """
self.dacycle = dacycle
self.outputdir = dacycle['dir.output']
def prepare_run(self):
""" Prepare the running of the actual forecast model, for example compile code """
# Define the name of the file that will contain the modeled output of each observation
self.simulated_file = os.path.join(self.outputdir, 'samples_simulated.%s.nc' % self.dacycle['time.sample.stamp'])
self.forecast_nmembers = int(self.dacycle['da.optimizer.nmembers'])
def run(self,lag,dacycle,statevector,advance=False):
members = statevector.ensemble_members[lag]
self.forecast_nmembers = int(self.dacycle['da.optimizer.nmembers'])
self.nparams = int(self.dacycle['nparameters'])
absolute_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
absolute_start_time_ch = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y-%m-%d'))
starth = abs((to_datetime(dacycle['abs.time.start'])-dacycle['time.start']).days)*24
endh = abs((to_datetime(dacycle['abs.time.start'])-dacycle['time.finish']).days)*24
f = io.CT_CDF(self.simulated_file, method='create')
logging.debug('Creating new simulated observation file in ObservationOperator (%s)' % self.simulated_file)
dimid = f.createDimension('obs_num', size=None)
dimid = ('obs_num',)
savedict = io.std_savedict.copy()
savedict['name'] = "obs_num"
savedict['dtype'] = "int"
savedict['long_name'] = "Unique_Dataset_observation_index_number"
savedict['units'] = ""
savedict['dims'] = dimid
savedict['comment'] = "Unique index number within this dataset ranging from 0 to UNLIMITED."
f.add_data(savedict,nsets=0)
dimmember = f.createDimension('nmembers', size=self.forecast_nmembers)
dimmember = ('nmembers',)
savedict = io.std_savedict.copy()
savedict['name'] = "flask"
savedict['dtype'] = "float"
savedict['long_name'] = "mole_fraction_of_trace_gas_in_dry_air"
savedict['units'] = "ppm"
savedict['dims'] = dimid + dimmember
savedict['comment'] = "Simulated model value created by COSMO"
f.add_data(savedict,nsets=0)
# Open file with x,y,z,t of model samples that need to be sampled
f_in = io.ct_read(self.dacycle['ObsOperator.inputfile'],method='read')
# Get simulated values and ID
ids = f_in.get_variable('obs_num')
obs = f_in.get_variable('observed')
mdm = f_in.get_variable('modeldatamismatch')
f_in.close()
model_data = np.empty(shape=(self.forecast_nmembers,obs.size)) # 3x7
self.lambda_file = os.path.join(self.outputdir, 'lambda.%s.nc' % self.dacycle['time.sample.stamp'])
ofile = Dataset(self.lambda_file, mode='w')
opar = ofile.createDimension('nparam', self.nparams)
omem = ofile.createDimension('nensembles', self.forecast_nmembers)#len(members.nmembers))
l = ofile.createVariable('lambda', np.float32, ('nensembles','nparam'),fill_value=-999.99)
co2 = np.empty(shape=(self.forecast_nmembers,self.nparams))
for m in range(0,self.forecast_nmembers):
co2[m,:] = members[m].param_values
# co2[co2<0] = 0.
l[:] = co2
ofile.close()
os.system('cp '+self.lambda_file+' '+dacycle['da.vprm']+'/lambdas.nc')
os.chdir(dacycle['da.obsoperator.home'])
if os.path.exists(dacycle['dir.da_run']+'/'+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
if os.path.exists(dacycle['dir.da_run']+"/non_opt_"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
os.rename(dacycle['dir.da_run']+"/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), dacycle['dir.da_run']+"/old_non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
else:
os.rename(dacycle['dir.da_run']+"/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), dacycle['dir.da_run']+"/non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
os.system('python run_chain.py '+self.dacycle['run.name']+' '+absolute_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc int2lm post_int2lm oae octe online_vprm cosmo')
logging.info('COSMO done!')
os.chdir(dacycle['dir.da_run'])
args = [
(dacycle, starth+168*lag, endh+168*lag-1, n)
for n in range(1,self.forecast_nmembers+1)
]
with Pool(self.forecast_nmembers) as pool:
pool.starmap(self.extract_model_data, args)
for i in range(0,self.forecast_nmembers):
idx = str(i+1).zfill(3)
cosmo_file = os.path.join('/store/empa/em05/parsenov/cosmo_data/model_'+idx+'_%s.nc' % dacycle['time.sample.stamp'])
ifile = Dataset(cosmo_file, mode='r')
model_data[i,:] = (np.squeeze(ifile.variables['CO2'][:])*29./44.01)*1E6 # in ppm
ifile.close()
for j,data in enumerate(zip(ids,obs,mdm)):
f.variables['obs_num'][j] = data[0]
f.variables['flask'][j,:] = model_data[:,j]
f.close()
logging.info('ObservationOperator finished successfully, output file written (%s)' % self.simulated_file)
def run_forecast_model(self, lag, dacycle, statevector, advance):
self.prepare_run()
self.run(lag, dacycle, statevector, advance)
def extract_model_data(self, dacycle, hstart, hstop, ensnum):
self.dacycle = dacycle
time_stamp = dacycle['time.sample.stamp']
abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
cosmo_out = dacycle['dir.da_run']+"/"+abs_start_time+"_"+str(hstart)+"_"+str(hstop+1)+"/cosmo/output/"
hhl_cosmo_out = dacycle['dir.da_run']+"/"+abs_start_time+"_0_168/cosmo/output/"
cosmo_save = "/store/empa/em05/parsenov/cosmo_data/"
hhl_fn = hhl_cosmo_out+'lffd'+abs_start_time+'c.nc'
ens = str(ensnum).zfill(3)
files2cat_albs = []
files2cat_bntg = []
files2cat_brm = []
files2cat_chri = []
files2cat_due1 = []
files2cat_esmo = []
files2cat_frob = []
files2cat_gimm = []
files2cat_hae = []
files2cat_laeg = []
files2cat_magn = []
files2cat_payn = []
files2cat_reck = []
files2cat_rig = []
files2cat_save = []
files2cat_semp = []
files2cat_sott = []
files2cat_ssal = []
files2cat_taen = []
files2cat_zhbr = []
files2cat_zsch = []
files2cat_zue = []
if ens == "001":
cdo.selname("HHL", input = hhl_fn, output = cosmo_out+"hhl.nc")
cdo.remapnn("lon=8.51_lat=47.31,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_albs.nc")
cdo.remapnn("lon=7.53_lat=46.98,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_bntg.nc")
cdo.remapnn("lon=8.18_lat=47.19,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_brm.nc")
cdo.remapnn("lon=7.69_lat=47.57,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_chri.nc")
cdo.remapnn("lon=8.61_lat=47.40,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_due1.nc")
cdo.remapnn("lon=8.57_lat=47.52,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_esmo.nc")
cdo.remapnn("lon=7.90_lat=47.38,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_frob.nc")
cdo.remapnn("lon=7.25_lat=47.05,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_gimm.nc")
cdo.remapnn("lon=7.82_lat=47.31,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_hae.nc")
cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_laeg.nc")
cdo.remapnn("lon=8.93_lat=46.16,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_magn.nc")
cdo.remapnn("lon=6.94_lat=46.81,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_payn.nc")
cdo.remapnn("lon=8.52_lat=47.43,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_reck.nc")
cdo.remapnn("lon=8.46_lat=47.07,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_rig.nc")
cdo.remapnn("lon=7.36_lat=46.24,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_save.nc")
cdo.remapnn("lon=8.21_lat=47.12,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_semp.nc")
cdo.remapnn("lon=6.74_lat=46.66,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_sott.nc")
cdo.remapnn("lon=8.95_lat=45.98,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_ssal.nc")
cdo.remapnn("lon=8.90_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_taen.nc")
cdo.remapnn("lon=8.57_lat=47.38,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_zhbr.nc")
cdo.remapnn("lon=8.52_lat=47.37,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_zsch.nc")
cdo.remapnn("lon=8.53_lat=47.38,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_zue.nc")
for dt in rrule.rrule(rrule.HOURLY, dtstart=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstart), until=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstop)):
dt=dt.strftime('%Y%m%d%H')
if ens == "001":
logging.info('Extracting output for time %s' % (str(dt)))
co2_in_fn = cosmo_out+'lffd'+dt+'.nc'
co2_out_albs = cosmo_out+'CO2_albs_'+ens+'_'+dt+'.nc'
co2_out_bntg = cosmo_out+'CO2_bntg_'+ens+'_'+dt+'.nc'
co2_out_brm = cosmo_out+'CO2_brm_'+ens+'_'+dt+'.nc'
co2_out_chri = cosmo_out+'CO2_chri_'+ens+'_'+dt+'.nc'
co2_out_due1 = cosmo_out+'CO2_due1_'+ens+'_'+dt+'.nc'
co2_out_esmo = cosmo_out+'CO2_esmo_'+ens+'_'+dt+'.nc'
co2_out_frob = cosmo_out+'CO2_frob_'+ens+'_'+dt+'.nc'
co2_out_gimm = cosmo_out+'CO2_gimm_'+ens+'_'+dt+'.nc'
co2_out_hae = cosmo_out+'CO2_hae_'+ens+'_'+dt+'.nc'
co2_out_laeg = cosmo_out+'CO2_laeg_'+ens+'_'+dt+'.nc'
co2_out_magn = cosmo_out+'CO2_magn_'+ens+'_'+dt+'.nc'
co2_out_payn = cosmo_out+'CO2_payn_'+ens+'_'+dt+'.nc'
co2_out_reck = cosmo_out+'CO2_reck_'+ens+'_'+dt+'.nc'
co2_out_rig = cosmo_out+'CO2_rig_'+ens+'_'+dt+'.nc'
co2_out_save = cosmo_out+'CO2_save_'+ens+'_'+dt+'.nc'
co2_out_semp = cosmo_out+'CO2_semp_'+ens+'_'+dt+'.nc'
co2_out_sott = cosmo_out+'CO2_sott_'+ens+'_'+dt+'.nc'
co2_out_ssal = cosmo_out+'CO2_ssal_'+ens+'_'+dt+'.nc'
co2_out_taen = cosmo_out+'CO2_taen_'+ens+'_'+dt+'.nc'
co2_out_zhbr = cosmo_out+'CO2_zhbr_'+ens+'_'+dt+'.nc'
co2_out_zsch = cosmo_out+'CO2_zsch_'+ens+'_'+dt+'.nc'
co2_out_zue = cosmo_out+'CO2_zue_'+ens+'_'+dt+'.nc'
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.51_lat=47.31 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_albs)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.53_lat=46.98 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_bntg)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.18_lat=47.19 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_brm)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.69_lat=47.57 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_chri)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.61_lat=47.40 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_due1)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.57_lat=47.52 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_esmo)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.90_lat=47.38 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_frob)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.25_lat=47.05 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_gimm)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.82_lat=47.31 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_hae)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.40_lat=47.48 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_laeg)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.93_lat=46.16 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_magn)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=6.94_lat=46.81 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_payn)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.52_lat=47.43 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_reck)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.46_lat=47.07 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_rig)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.36_lat=46.24 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_save)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.21_lat=47.12 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_semp)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=6.74_lat=46.66 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_sott)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.95_lat=45.98 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_ssal)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.90_lat=47.48 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_taen)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.57_lat=47.38 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_zhbr)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.52_lat=47.37 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_zsch)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.53_lat=47.38 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_zue)
files2cat_albs.append(co2_out_albs)
files2cat_bntg.append(co2_out_bntg)
files2cat_brm.append(co2_out_brm)
files2cat_chri.append(co2_out_chri)
files2cat_due1.append(co2_out_due1)
files2cat_esmo.append(co2_out_esmo)
files2cat_frob.append(co2_out_frob)
files2cat_gimm.append(co2_out_gimm)
files2cat_hae.append(co2_out_hae)
files2cat_laeg.append(co2_out_laeg)
files2cat_magn.append(co2_out_magn)
files2cat_payn.append(co2_out_payn)
files2cat_reck.append(co2_out_reck)
files2cat_rig.append(co2_out_rig)
files2cat_save.append(co2_out_save)
files2cat_semp.append(co2_out_semp)
files2cat_sott.append(co2_out_sott)
files2cat_ssal.append(co2_out_ssal)
files2cat_taen.append(co2_out_taen)
files2cat_zhbr.append(co2_out_zhbr)
files2cat_zsch.append(co2_out_zsch)
files2cat_zue.append(co2_out_zue)
cdo.cat(input = files2cat_albs, output = cosmo_out+"CO2_albs_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_bntg, output = cosmo_out+"CO2_bntg_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_brm, output = cosmo_out+"CO2_brm_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_chri, output = cosmo_out+"CO2_chri_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_due1, output = cosmo_out+"CO2_due1_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_esmo, output = cosmo_out+"CO2_esmo_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_frob, output = cosmo_out+"CO2_frob_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_gimm, output = cosmo_out+"CO2_gimm_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_hae, output = cosmo_out+"CO2_hae_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_laeg, output = cosmo_out+"CO2_laeg_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_magn, output = cosmo_out+"CO2_magn_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_payn, output = cosmo_out+"CO2_payn_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_reck, output = cosmo_out+"CO2_reck_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_rig, output = cosmo_out+"CO2_rig_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_save, output = cosmo_out+"CO2_save_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_semp, output = cosmo_out+"CO2_semp_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_sott, output = cosmo_out+"CO2_sott_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_ssal, output = cosmo_out+"CO2_ssal_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_taen, output = cosmo_out+"CO2_taen_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_zhbr, output = cosmo_out+"CO2_zhbr_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_zsch, output = cosmo_out+"CO2_zsch_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_zue, output = cosmo_out+"CO2_zue_"+ens+"_"+time_stamp+".nc")
sites = ("albs", "bntg", "brm", "chri", "due1", "esmo", "frob", "gimm", "hae", "laeg", "magn", "payn", "reck", "rig", "save", "semp", "sott", "ssal", "taen", "zhbr", "zsch", "zue")
for s,ss in enumerate(sites):
site_height.main(cosmo_out, str(ens), ss, time_stamp)
cdo.intlevel("857.25", input = cosmo_out+"CO2_60lev_"+ens+"_albs_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_albs_"+time_stamp+".nc")
cdo.intlevel("995.35", input = cosmo_out+"CO2_60lev_"+ens+"_bntg_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_bntg_"+time_stamp+".nc")
cdo.intlevel("1009.7", input = cosmo_out+"CO2_60lev_"+ens+"_brm_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_brm_"+time_stamp+".nc")
cdo.intlevel("592.8", input = cosmo_out+"CO2_60lev_"+ens+"_chri_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_chri_"+time_stamp+".nc")
cdo.intlevel("432.4", input = cosmo_out+"CO2_60lev_"+ens+"_due1_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_due1_"+time_stamp+".nc")
cdo.intlevel("560.9", input = cosmo_out+"CO2_60lev_"+ens+"_esmo_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_esmo_"+time_stamp+".nc")
cdo.intlevel("897.3", input = cosmo_out+"CO2_60lev_"+ens+"_frob_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_frob_"+time_stamp+".nc")
cdo.intlevel("478.5", input = cosmo_out+"CO2_60lev_"+ens+"_gimm_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_gimm_"+time_stamp+".nc")
cdo.intlevel("430.2", input = cosmo_out+"CO2_60lev_"+ens+"_hae_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_hae_"+time_stamp+".nc")
cdo.intlevel("855", input = cosmo_out+"CO2_60lev_"+ens+"_laeg_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_laeg_"+time_stamp+".nc")
cdo.intlevel("208", input = cosmo_out+"CO2_60lev_"+ens+"_magn_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_magn_"+time_stamp+".nc")
cdo.intlevel("488.7", input = cosmo_out+"CO2_60lev_"+ens+"_payn_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_payn_"+time_stamp+".nc")
cdo.intlevel("443.1", input = cosmo_out+"CO2_60lev_"+ens+"_reck_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_reck_"+time_stamp+".nc")
cdo.intlevel("1030.5", input = cosmo_out+"CO2_60lev_"+ens+"_rig_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_rig_"+time_stamp+".nc")
cdo.intlevel("789.4", input = cosmo_out+"CO2_60lev_"+ens+"_save_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_save_"+time_stamp+".nc")
cdo.intlevel("583.3", input = cosmo_out+"CO2_60lev_"+ens+"_semp_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_semp_"+time_stamp+".nc")