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da/cosmo/observationoperator.py

@@ -133,8 +133,8 @@ class ObservationOperator(object):
                 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['restartmap.dir'],"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['restartmap.dir'],"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')))
+                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)

da/cosmo/pipeline.py

@@ -267,9 +267,9 @@ def prepare_state(dacycle, statevector):
     current_sv = os.path.join(dacycle['dir.restart'], 'savestate_%s.nc' % dacycle['time.start'].strftime('%Y%m%d'))
     statevector.write_to_file(current_sv, 'prior')  # write prior info 
     nlag = int(dacycle['time.nlag'])
-    for l in range(0,nlag):                      # pavle added from here
-#        statevector.write_members_to_file(l, dacycle['restartmap.dir'])
-        statevector.write_members_for_cosmo(l, dacycle['restartmap.dir'])
+#    for l in range(0,nlag):                      # pavle added from here
+##        statevector.write_members_to_file(l, dacycle['restartmap.dir'])
+  #      statevector.write_members_for_cosmo(l, dacycle['restartmap.dir'])
 
 def sample_state(dacycle, samples, statevector, obsoperator):
     """ Sample the filter state for the inversion """

template.rc

@@ -56,7 +56,8 @@ time.nlag           : 2
 ! '/' will be replaced through the start_ctdas.sh script by a user-specified folder name. DO NOT REPLACE
 
 dir.da_run          : /scratch/snx3000/parsenov/ctdas
-restartmap.dir      : /scratch/snx3000/parsenov/ctdas/restart
+restartmap.dir      : /scratch/snx3000/parsenov/ctdas/input
+!restartmap.dir      : /scratch/snx3000/parsenov/ctdas/restart
 
 ! 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