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da/cosmo/observationoperator.py

@@ -15,6 +15,8 @@ from cdo import *
 identifier = 'ObservationOperator'
 version = '10'
 
+cdo = Cdo()
+
 ################### Begin Class ObservationOperator ###################
 class ObservationOperator(object):
     def __init__(self, dacycle=None):
@@ -52,7 +54,7 @@ class ObservationOperator(object):
         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):
+    def run(self,dacycle):
 
 
         f = io.CT_CDF(self.simulated_file, method='create')
@@ -117,19 +119,12 @@ class ObservationOperator(object):
 #        for ncfile in ncfilelist:
 #            infile = os.path.join(ncfile + '.nc')
 
-
-        # here comes int2lm
-        
-#        cosmo = os.path.join(dacycle.dasystem['cosmo_path'],"run_chain.py")
-#        os.system('python '+cosmo+'ctdas'+dacycle['time.start'].strftime('%Y-%m-%d')+'0 672 -j meteo icbc emissions biofluxes int2lm post_int2lm')
-        # HERE MULTIPLY COSMO FLUXES INT2LM WITH CTDAS PARAMS
         for ens in range(1,self.forecast_nmembers+1):
             ens = str(ens).zfill(3)
-            os.system('ln *nc /scratch/snx3000/parsenov/ctdas/2013040100_0_168/int2lm/output /scratch/snx3000/parsenov/ctdas/cosmo_ensemble/'+ens)
-            os.system('rm /scratch/snx3000/parsenov/ctdas/cosmo_ensemble/*f.nc')
-            for dt in rrule.rrule(rrule.HOURLY, dtstart=self['time.start'], until=self['time.start']+timedelta(hours=168)):
-                print(dt)
-                cdo.mul("parameters."+ens+".nc", input = "/scratch/snx3000/parsenov/ctdas/2013040100_0_168/int2lm/output/laf"+dt.strftime('%Y%m%d%H')+"f.nc", output = "/scratch/snx3000/parsenov/ctdas/cosmo_ensemble/"+ens+"laf"+dt.strftime('%Y%m%d%H')+"f.nc")
+            for dt in rrule.rrule(rrule.HOURLY, dtstart=dacycle['time.start'], until=dacycle['time.start']+timedelta(hours=672)):
+                cdo.mul("parameters."+ens+".nc", input = os.path.join(dacycle['da.bio.input'], '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')))
+
+#        os.system('python '+dacycle['da.obsoperator.home']+' ctdas '+dacycle['time.start'].strftime('%Y-%m-%d')+' 0 672 -j meteo icbc emissions biofluxes int2lm post_int2lm')
         # here comes COSMO
             sys.exit()
             os.system('python '+cosmo+' ctdas '+self['time.start'].strftime('%Y-%m-%d')+' 0 672 -j cosmo')
@@ -166,13 +161,12 @@ class ObservationOperator(object):
 
         logging.info('ObservationOperator finished successfully, output file written (%s)' % self.simulated_file)
 
-    def run_forecast_model(self):
+    def run_forecast_model(self,dacycle):
         self.prepare_run()
-        self.run()
+        self.run(dacycle)
 
     def extract_model_data(ens):
         
-        cdo = Cdo()
         files2cat=[]
 
         time_stamp = self['time.sample.stamp']

template.py

@@ -26,7 +26,7 @@ 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.cosmo.pipeline import ensemble_smoother_pipeline, header, footer
 from da.platform.maunaloa import MaunaloaPlatform 
 from da.baseclasses.dasystem import DaSystem 
 from da.baseclasses.statevector import StateVector 

template.rc

@@ -92,14 +92,15 @@ da.obsoperator         : TM5
 ! be used as observation operator in this experiment.
 !
 
-da.obsoperator.home    : ${HOME}/TM5
+da.obsoperator.home    : /store/empa/em05/parsenov/cosmo_processing_chain/run_chain.py
+da.bio.input           : /store/empa/em05/parsenov/cosmo_input/vprm/processed
 da.obsoperator.rc      : ${da.obsoperator.home}/tm5-ctdas-ei-zoom.rc
 
 !
 ! The number of ensemble members used in the experiment. Valid entries are integers > 2
 !
 
-da.optimizer.nmembers  : 10
+da.optimizer.nmembers  : 20
 
 ! 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