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da/cosmo/observationoperator_octe.py

@@ -114,7 +114,7 @@ class ObservationOperator(object):
         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(0,20):
+        for m in range(0,self.forecast_nmembers):
             co2[m,:] = members[m].param_values
         l[:] = co2
         ofile.close()

da/cosmo/statevector.py

@@ -149,9 +149,7 @@ class StateVector(object):
 
         self.nlag = int(dacycle['time.nlag'])
         self.nmembers = int(dacycle['da.optimizer.nmembers'])
-#        self.nparams = int(dacycle.dasystem['nparameters'])
-        self.nparams = 181
-        #self.nparams = 198
+        self.nparameters = int(dacycle['nparameters'])
         self.nobs = 0
         
         self.obs_to_assimilate = ()  # empty containter to hold observations to assimilate later on
@@ -164,7 +162,6 @@ class StateVector(object):
         for n in range(self.nlag):
             self.ensemble_members[n] = []
 
-
         # This specifies the file to read with the gridded mask at 1x1 degrees. Each gridbox holds a number that specifies the parametermember
         #  that maps onto it. From this map, a dictionary is created that allows a reverse look-up so that we can map parameters to a grid.
 
@@ -181,8 +178,7 @@ class StateVector(object):
 
         # Create a dictionary for state <-> gridded map conversions
 
-#        nparams = 198
-        nparams = 181
+        nparams = self.nparameters
         self.griddict = {}
         for pft in range(1,18):
             for r in range(1, 11):
@@ -291,7 +287,7 @@ class StateVector(object):
         self.ensemble_members[lag].append(newmember)
 
         # Create members 1:nmembers and add to ensemble_members list
-        for member in range(1, self.nmembers+1):
+        for member in range(1, self.nmembers):
 #            rands = np.random.uniform(low=-1., high=1., size=self.nparams-1)
  #           rands_bg = np.random.uniform(low=-0.05, high=0.05, size=1)
 #            rands = np.random.randn(self.nparams-1)