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Commit cee00f6a authored by brunner's avatar brunner
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da/cosmo/covariances.py
View file @ cee00f6a
......@@ -68,7 +68,8 @@ class CO2StateVector(StateVector):
(0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.16, 1.00, 0.16, 0.16, 0.00), \
(0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.16, 0.16, 1.00, 0.16, 0.00), \
(0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.16, 0.16, 0.16, 1.00, 0.00), \
(0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.e-10) ])
# (0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.e-10) ])
(0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00) ])
return fullcov
......
da/cosmo/observationoperator.py
View file @ cee00f6a
......@@ -125,32 +125,32 @@ class ObservationOperator(object):
# UNCOMMENT FROM HERE
co2_bg = np.empty(self.forecast_nmembers)
logging.info('Multiplying emissions with parameters for lag %d' % (lag))
for dt in rrule.rrule(rrule.HOURLY, dtstart=dacycle['time.start']+timedelta(hours=24*lag*int(dacycle['time.cycle'])), until=dacycle['time.start']+timedelta(hours=(lag+1)*24*int(dacycle['time.cycle']))):
for ens in range(0,self.forecast_nmembers):
dthh = dt.strftime('%H')
co2_bg[ens] = members[ens].param_values[-1]
ens = str(ens).zfill(3)
cdo.setunit("'kg m-2 s-1' -expr,GPP_"+ens+"_F=CO2_GPP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'gpp_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.input'],"parameters_gpp_lag"+str(lag)+"."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
cdo.setunit("'kg m-2 s-1' -expr,RESP_"+ens+"_F=CO2_RESP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'ra_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.input'],"parameters_resp_lag"+str(lag)+"."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
# logging.debug('Background CO2 params are (%s)' % co2_bg)
if dthh=='00':
ct(dt.strftime('%Y%m%d'), co2_bg)
cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_%s.nc" % dt.strftime('%Y%m%d%H')))
cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_%s.nc" % dt.strftime('%Y%m%d%H')))
os.chdir(dacycle['da.obsoperator.home'])
if os.path.exists("/scratch/snx3000/parsenov/ctdas/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
if os.path.exists("/scratch/snx3000/parsenov/ctdas/non_opt_"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
os.rename("/scratch/snx3000/parsenov/ctdas/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), "/scratch/snx3000/parsenov/ctdas/old_non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
# co2_bg = np.empty(self.forecast_nmembers)
#
# logging.info('Multiplying emissions with parameters for lag %d' % (lag))
# for dt in rrule.rrule(rrule.HOURLY, dtstart=dacycle['time.start']+timedelta(hours=24*lag*int(dacycle['time.cycle'])), until=dacycle['time.start']+timedelta(hours=(lag+1)*24*int(dacycle['time.cycle']))):
# for ens in range(0,self.forecast_nmembers):
# dthh = dt.strftime('%H')
# co2_bg[ens] = members[ens].param_values[-1]
# ens = str(ens).zfill(3)
# cdo.setunit("'kg m-2 s-1' -expr,GPP_"+ens+"_F=CO2_GPP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'gpp_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.input'],"parameters_gpp_lag"+str(lag)+"."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
# cdo.setunit("'kg m-2 s-1' -expr,RESP_"+ens+"_F=CO2_RESP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'ra_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.input'],"parameters_resp_lag"+str(lag)+"."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
# # logging.debug('Background CO2 params are (%s)' % co2_bg)
# if dthh=='00':
# ct(dt.strftime('%Y%m%d'), co2_bg)
#
# cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_%s.nc" % dt.strftime('%Y%m%d%H')))
# cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_%s.nc" % dt.strftime('%Y%m%d%H')))
#
# 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("/scratch/snx3000/parsenov/ctdas/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), "/scratch/snx3000/parsenov/ctdas/non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
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 ctdas '+absolute_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc emissions biofluxes int2lm post_int2lm cosmo')
os.system('python run_chain.py 21ens '+absolute_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc emissions biofluxes int2lm post_int2lm cosmo')
os.chdir(dacycle['dir.da_run'])
args = [
......@@ -165,6 +165,7 @@ class ObservationOperator(object):
for i in range(0,self.forecast_nmembers):
idx = str(i).zfill(3)
# cosmo_file = os.path.join('/store/empa/em05/parsenov/cosmo_data/51ens/model_'+idx+'_%s.nc' % dacycle['time.sample.stamp'])
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
......@@ -189,8 +190,8 @@ class ObservationOperator(object):
time_stamp = dacycle['time.sample.stamp']
abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
cosmo_out = "/scratch/snx3000/parsenov/ctdas/"+abs_start_time+"_"+str(hstart)+"_"+str(hstop+1)+"/cosmo/output/"
hhl_cosmo_out = "/scratch/snx3000/parsenov/ctdas/"+abs_start_time+"_0_168/cosmo/output/"
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'
......
da/cosmo/observationoperator_parallel.py
View file @ cee00f6a
......@@ -13,7 +13,7 @@ from dateutil import rrule
from cdo import *
from . import site_height
from da.cosmo.icbc4ctdas import ct
from itertools import repeat
#from itertools import repeat
from multiprocessing import Pool
from da.tools.general import to_datetime
......@@ -131,6 +131,14 @@ class ObservationOperator(object):
for dt in rrule.rrule(rrule.HOURLY, dtstart=dacycle['time.start']+timedelta(hours=24*lag*int(dacycle['time.cycle'])), until=dacycle['time.start']+timedelta(hours=(lag+1)*24*int(dacycle['time.cycle']))):
for ens in range(0,self.forecast_nmembers):
co2_bg[ens] = members[ens].param_values[-1]
dthh = dt.strftime('%H')
if dthh=='00':
ct(dt.strftime('%Y%m%d'), co2_bg)
print("ct proso",dt)
args = [
(dacycle, lag, members, dt, n)
for n in range(0,self.forecast_nmembers)
......@@ -138,20 +146,18 @@ class ObservationOperator(object):
with Pool(self.forecast_nmembers) as pool:
pool.starmap(self.multiply_flux, args)
if dthh=='00':
ct(dt.strftime('%Y%m%d'), co2_bg)
print("vprm proso",dt)
cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_%s.nc" % dt.strftime('%Y%m%d%H')))
cdo.merge(input = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_???_%s.nc" % dt.strftime('%Y%m%d%H')), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_%s.nc" % dt.strftime('%Y%m%d%H')))
os.chdir(dacycle['da.obsoperator.home'])
if os.path.exists("/scratch/snx3000/parsenov/ctdas/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
if os.path.exists("/scratch/snx3000/parsenov/ctdas/non_opt_"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
os.rename("/scratch/snx3000/parsenov/ctdas/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), "/scratch/snx3000/parsenov/ctdas/old_non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
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("/scratch/snx3000/parsenov/ctdas/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), "/scratch/snx3000/parsenov/ctdas/non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
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 ctdas '+absolute_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc emissions biofluxes int2lm post_int2lm cosmo')
os.chdir(dacycle['dir.da_run'])
......@@ -187,8 +193,6 @@ class ObservationOperator(object):
self.run(lag, dacycle, statevector)
def multiply_flux(self,dacycle,lag,members,dt,ens):
dthh = dt.strftime('%H')
co2_bg[ens] = members[ens].param_values[-1]
ens = str(ens).zfill(3)
cdo.setunit("'kg m-2 s-1' -expr,GPP_"+ens+"_F=CO2_GPP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'gpp_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.input'],"parameters_gpp_lag"+str(lag)+"."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "gpp_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
cdo.setunit("'kg m-2 s-1' -expr,RESP_"+ens+"_F=CO2_RESP_F*parametermap -merge "+os.path.join(dacycle['da.bio.input'], 'ra_%s.nc' % dt.strftime('%Y%m%d%H')), input = os.path.join(dacycle['dir.input'],"parameters_resp_lag"+str(lag)+"."+ens+".nc"), output = os.path.join(dacycle['da.bio.input'], 'ensemble', "ra_"+ens+"_%s.nc" % dt.strftime('%Y%m%d%H')))
......@@ -199,8 +203,8 @@ class ObservationOperator(object):
time_stamp = dacycle['time.sample.stamp']
abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
cosmo_out = "/scratch/snx3000/parsenov/ctdas/"+abs_start_time+"_"+str(hstart)+"_"+str(hstop+1)+"/cosmo/output/"
hhl_cosmo_out = "/scratch/snx3000/parsenov/ctdas/"+abs_start_time+"_0_168/cosmo/output/"
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'
......
da/cosmo/statevector.py
View file @ cee00f6a
......@@ -380,8 +380,8 @@ class StateVector(object):
newmember = EnsembleMember(member)
# newmember.param_values = rands + newmean
newmember.param_values = (np.hstack((np.dot(C, rands[0:11]),np.dot(C, rands[11:22]), rands_bg)) + newmean).ravel()
newmember.param_values[10] = 0.
newmember.param_values[21] = 0.
# newmember.param_values[10] = 0.
# newmember.param_values[21] = 0.
newmember.param_values[newmember.param_values<0.] = 0.
newmember.param_values[newmember.param_values>2.] = 2.
self.ensemble_members[lag].append(newmember)
......
template.py
View file @ cee00f6a
......@@ -34,6 +34,7 @@ from da.cosmo.covariances import CO2StateVector
from da.cosmo.obspack_globalviewplus2 import ObsPackObservations
#from da.cosmo.obs import Obs
from da.cosmo.optimizer import CO2Optimizer
#from da.cosmo.observationoperator_parallel import ObservationOperator # does not fully work
from da.cosmo.observationoperator import ObservationOperator
#from da.analysis.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
......
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