Changeset 524 in lmdz_wrf for trunk/tools

Timestamp:

Jun 22, 2015, 12:47:29 PM (10 years ago)

Author:

lfita

Message:

First attempt of working with 'exact', 'closest', 'between'

File:

• trunk/tools/validation_sim.py (modified) (8 diffs)

trunk/tools/validation_sim.py

@@ -1485 +1485 @@
 
 stdescsim = ['E', 'C', 'B']
+prestdescsim = ['exact', 'closest', 'between']
 Lstdescsim = ['exact time', 'cloest time', 'between observational time-steps']
 
@@ -1848 +1849 @@
 
 # Dimensions
-newdim = onewnc.createDimension('time',None)
-newdim = onewnc.createDimension('betweentime',None)
+for kst in range(Nstsim):
+    newdim = onewnc.createDimension(prestdescsim[kst] + 'time',None)
+    if stdescsim[kst] != 'E':
+        newdim = onewnc.createDimension(prestdescsim[kst] + 'obstime',None)
+
 newdim = onewnc.createDimension('bnds',2)
-newdim = onewnc.createDimension('obstime',None)
 newdim = onewnc.createDimension('couple',2)
 newdim = onewnc.createDimension('StrLength',StringLength)
@@ -1863 +1866 @@
 # Variable dimensions
 ##
-newvar = onewnc.createVariable('exacttime','f8',('time'))
-basicvardef(newvar, 'obstime', 'exact coincident time observations and simulation', obstunits)
-set_attribute(newvar, 'calendar', 'standard')
-print 'Lluis Nexacttvalues:',len(exacttvalues[:,3])
-newvar[:] = exacttvalues[:,3]
-
-newvar = onewnc.createVariable('obstime','f8',('obstime'))
-basicvardef(newvar, 'obstime', 'time observations for between values', obstunits)
-set_attribute(newvar, 'calendar', 'standard')
-newvar[:] = closesttvalues[:,3]
-
 newvar = onewnc.createVariable('couple', 'c', ('couple','StrLength'))
 basicvardef(newvar, 'couple', 'couples of values', '-')
@@ -1947 +1939 @@
 
     for kst in range(Nstsim):
+        timedn = prestdescsim[kst] + 'time'
 
         if stdescsim[kst] == 'E':
@@ -1954 +1947 @@
               ovobs, exacttvalues, oFillValue, Ngrid, 'instantaneous')
 
+            vname = prestdescsim[kst] + 'time'
+ 
+            newvar = onewnc.createVariable(vname,'f8', (timedn))
+            basicvardef(newvar, vname, 'exact coincident time observations and ' +   \
+              'simulation', obstunits)
+            set_attribute(newvar, 'calendar', 'standard')
+            newvar[:] = exacttvalues[:,3]
+
         elif = stdescsim[kst] == 'C':
 # Simualted closest to Observed times
@@ -1960 +1961 @@
               ovobs, closesttvalues, oFillValue, Ngrid, 'instantaneous')
 
-            newvar = onewnc.createVariable('closesttime','f8',('obstime'))
-            basicvardef(newvar, 'closesttime', 'time simulations closest to ' +      \
-              'observed values', simtunits )
+            vname = prestdescsim[kst] + 'time'
+
+            newvar = onewnc.createVariable(vname,'f8', (timedn))
+            basicvardef(newvar, vname, 'time simulations closest to observed values',\
+              obstunits )
+            set_attribue(newvar, 'calendar', 'standard')
+            newvar[:] = closesttvalues[:,2]
+
+            vname  = (prestdescsim[kst] + 'obstime'
+            newvar = onewnc.createVariable(vname,'f8', (vname))
+            basicvardef(newvar, vname, 'closest time observations', obstunits)
             set_attribute(newvar, 'calendar', 'standard')
-            newvar[:] = closesttvalues[:,2]
+            newvar[:] = closesttvalues[:,3]
 
         elif = stdescsim[kst] == 'B':
@@ -1972 +1981 @@
               coindtvalues, oFillValue, Ngrid, 'tbackwardSmean')
 
-            newvar = onewnc.createVariable('betweentime','f8',('betweentime'))
-            basicvardef(newvar, 'obstime', 'time simulations for between values',    \
-              simtunits )
+            vname = prestdescsim[kst] + 'time'
+            newvar = onewnc.createVariable(vname,'f8', (timedn))
+            basicvardef(newvar, vname, 'simulation time between observations',       \
+              obstunits )
             set_attribute(newvar, 'calendar', 'standard')
             set_attribute(newvar, 'bounds', 'time_bnds')
             newvar[:] = simobsTtvalues[:,1]
 
+            vname = prestdescsim[kst] + 'obstime'
+            newvar = onewnc.createVariable(vname,'f8', (vname))
+            basicvardef(newvar, vname, 'observed between time', obstunits )
+            set_attribute(newvar, 'calendar', 'standard')
+            newvar[:] = np.unique(coindtvalues[:,2])
+
 # Re-arranging values...
-    arrayvals = np.array(simobsvalues)
-    if len(valvars[ivar]) > 2:
-        const=np.float(valvars[ivar][3])
-        if valvars[ivar][2] == 'sumc':
-            simobsSvalues = simobsSvalues + const
-            arrayvals[:,0] = arrayvals[:,0] + const
-        elif valvars[ivar][2] == 'subc':
-            simobsSvalues = simobsSvalues - const
-            arrayvals[:,0] = arrayvals[:,0] - const
-        elif valvars[ivar][2] == 'mulc':
-            simobsSvalues = simobsSvalues * const
-            arrayvals[:,0] = arrayvals[:,0] * const
-        elif valvars[ivar][2] == 'divc':
-            simobsSvalues = simobsSvalues / const
-            arrayvals[:,0] = arrayvals[:,0] / const
-        else:
-            print errormsg
-            print '  ' + fname + ": operation '" + valvars[ivar][2] + "' not ready!!"
-            quit(-1)
+        arrayvals = np.array(simobsvalues)
+        if len(valvars[ivar]) > 2:
+            const=np.float(valvars[ivar][3])
+            if valvars[ivar][2] == 'sumc':
+                simobsSvalues = simobsSvalues + const
+                arrayvals[:,0] = arrayvals[:,0] + const
+            elif valvars[ivar][2] == 'subc':
+                simobsSvalues = simobsSvalues - const
+                arrayvals[:,0] = arrayvals[:,0] - const
+            elif valvars[ivar][2] == 'mulc':
+                simobsSvalues = simobsSvalues * const
+                arrayvals[:,0] = arrayvals[:,0] * const
+            elif valvars[ivar][2] == 'divc':
+                simobsSvalues = simobsSvalues / const
+                arrayvals[:,0] = arrayvals[:,0] / const
+            else:
+                print errormsg
+                print '  ' + fname + ": operation '"+valvars[ivar][2]+"' not ready!!"
+                quit(-1)
 
 # statisics sim
-    simstats = np.zeros((2,5), dtype=np.float)
-    simstats[0,0] = np.min(Earrayvals[:,0])
-    simstats[0,1] = np.max(Earrayvals[:,0])
-    simstats[0,2] = np.mean(Earrayvals[:,0])
-    simstats[0,3] = np.mean(Earrayvals[:,0]*Earrayvals[:,0])
-    simstats[0,4] = np.sqrt(simstats[0,3] - simstats[0,2]*simstats[0,2])
-
-    simstats[1,0] = np.min(arrayvals[:,0])
-    simstats[1,1] = np.max(arrayvals[:,0])
-    simstats[1,2] = np.mean(arrayvals[:,0])
-    simstats[1,3] = np.mean(arrayvals[:,0]*arrayvals[:,0])
-    simstats[1,4] = np.sqrt(simstats[1,3] - simstats[1,2]*simstats[1,2])
+        simstats = np.zeros((Nstsim,5), dtype=np.float)
+        simstats[kst,0] = np.min(arrayvals[:,0])
+        simstats[kst,1] = np.max(arrayvals[:,0])
+        simstats[kst,2] = np.mean(arrayvals[:,0])
+        simstats[kst,3] = np.mean(arrayvals[:,0]*arrayvals[:,0])
+        simstats[kst,4] = np.sqrt(simstats[kst,3] - simstats[kst,2]*simstats[kst,2])
 
 # statisics obs
 # Masking 'nan'
-    Eobsmask0 = np.where(Earrayvals[:,1] != Earrayvals[:,1], fillValueF, 
-      Earrayvals[:,1]) 
-    obsmask0 = np.where(arrayvals[:,1] != arrayvals[:,1], fillValueF, arrayvals[:,1]) 
-
-    Eobsmask = ma.masked_equal(Eobsmask0, fillValueF)
-    Eobsmask2 = Eobsmask*Eobsmask
-    obsmask = ma.masked_equal(obsmask0, fillValueF)
-    obsmask2 = obsmask*obsmask
-
-    obsstats = np.zeros((2,5), dtype=np.float)
-    obsstats[0,0] = obsmask.min()
-    obsstats[0,1] = obsmask.max()
-    obsstats[0,2] = obsmask.mean()
-    obsstats[0,3] = obsmask2.mean()
-    obsstats[0,4] = np.sqrt(obsstats[0,3] - obsstats[0,2]*obsstats[0,2])
-
-    obsstats[1,0] = obsmask.min()
-    obsstats[1,1] = obsmask.max()
-    obsstats[1,2] = obsmask.mean()
-    obsstats[1,3] = obsmask2.mean()
-    obsstats[1,4] = np.sqrt(obsstats[1,3] - obsstats[1,2]*obsstats[1,2])
- 
+        obsmask0 = np.where(arrayvals[:,1] != arrayvals[:,1], fillValueF,            \
+          arrayvals[:,1])
+
+        obsmask = ma.masked_equal(obsmask0, fillValueF)
+        obsmask2 = obsmask*obsmask
+
+        obsstats = np.zeros((Nstsim,5), dtype=np.float)
+        obsstats[kst,0] = obsmask.min()
+        obsstats[kst,1] = obsmask.max()
+        obsstats[kst,2] = obsmask.mean()
+        obsstats[kst,3] = obsmask2.mean()
+        obsstats[kst,4] = np.sqrt(obsstats[kst,3] - obsstats[kst,2]*obsstats[kst,2])
+
 # Statistics sim-obs
-    simobsstats = np.zeros((2,13), dtype=np.float)
-    Ediffvals = np.zeros((Nexactt), dtype=np.float)
-    diffvals = np.zeros((Ncoindt), dtype=np.float)
-
-    Ediffvals = Earrayvals[:,0] - Eobsmask
-    diffvals = arrayvals[:,0] - obsmask
-
-    Ediff2vals = Ediffvals*Ediffvals
-    Esumdiff = Ediffvals.sum()
-    Esumdiff2 = Ediff2vals.sum()
-
-    diff2vals = diffvals*diffvals
-    sumdiff = diffvals.sum()
-    sumdiff2 = diff2vals.sum()
-
-    simobsstats[0,0] = simstats[0,0] - obsstats[0,0]
-    simobsstats[0,1] = np.mean(Earrayvals[:,0]*Eobsmask)
-    simobsstats[0,2] = Ediffvals.min()
-    simobsstats[0,3] = Ediffvals.max()
-    simobsstats[0,4] = Ediffvals.mean()
-    simobsstats[0,5] = np.abs(Ediffvals).mean()
-    simobsstats[0,6] = np.sqrt(Ediff2vals.mean())
-    simobsstats[0,7], simobsstats[0,8] = sts.mstats.pearsonr(Earrayvals[:,0],        \
-      Eobsmask)
-
-    simobsstats[1,0] = simstats[1,0] - obsstats[1,0]
-    simobsstats[1,1] = np.mean(arrayvals[:,0]*obsmask)
-    simobsstats[1,2] = diffvals.min()
-    simobsstats[1,3] = diffvals.max()
-    simobsstats[1,4] = diffvals.mean()
-    simobsstats[1,5] = np.abs(diffvals).mean()
-    simobsstats[1,6] = np.sqrt(diff2vals.mean())
-    simobsstats[1,7], simobsstats[1,8] = sts.mstats.pearsonr(arrayvals[:,0],         \
-      obsmask)
+        simobsstats = np.zeros((Nstsim,13), dtype=np.float)
+        diffvals = np.zeros((Ncoindt), dtype=np.float)
+
+        diffvals = arrayvals[:,0] - obsmask
+
+        diff2vals = diffvals*diffvals
+        sumdiff = diffvals.sum()
+        sumdiff2 = diff2vals.sum()
+
+        simobsstats[kst,0] = simstats[kst,0] - obsstats[kst,0]
+        simobsstats[kst,1] = np.mean(arrayvals[:,0]*obsmask)
+        simobsstats[kst,2] = diffvals.min()
+        simobsstats[kst,3] = diffvals.max()
+        simobsstats[kst,4] = diffvals.mean()
+        simobsstats[kst,5] = np.abs(diffvals).mean()
+        simobsstats[kst,6] = np.sqrt(diff2vals.mean())
+        simobsstats[kst,7], simobsstats[kst,8] = sts.mstats.pearsonr(arrayvals[:,0], \
+          obsmask)
 # From:
 #Willmott, C. J. 1981. 'On the validation of models. Physical Geography', 2, 184-194
@@ -2086 +2071 @@
 #
 # Deviation of residuals (SDR)
-    simobsstats[0,9] = np.mean(np.sqrt(np.abs((Ediffvals-simobsstats[0,0])*(Ediffvals-\
-      obsstats[0,0]))))
-    simobsstats[1,9] = np.mean(np.sqrt(np.abs((diffvals-simobsstats[1,0])*(diffvals- \
-      obsstats[1,0]))))
+        simobsstats[kst,9] = np.mean(np.sqrt(np.abs((diffvals-simobsstats[kst,0])*   \
+          (diffvals-obsstats[kst,0]))))
 # Index of Efficiency (IOE)
-    Eobsbias2series = (Eobsmask - obsstats[0,0])*(Eobsmask - obsstats[0,0])
-    Esumobsbias2series = Eobsbias2series.sum()
-    obsbias2series = (obsmask - obsstats[1,0])*(obsmask - obsstats[1,0])
-    sumobsbias2series = obsbias2series.sum()
-
-    simobsstats[0,10] = 1. - Esumdiff2/(Esumobsbias2series)
-    simobsstats[1,10] = 1. - sumdiff2/(sumobsbias2series)
+        obsbias2series = (obsmask - obsstats[kst,0])*(obsmask - obsstats[kst,0])
+        sumobsbias2series = obsbias2series.sum()
+
+        simobsstats[kst,10] = 1. - sumdiff2/(sumobsbias2series)
 # Index of Agreement (IOA)
-    Esimbias2series = Earrayvals[:,0] - obsstats[0,0]
-    simbias2series = arrayvals[:,0] - obsstats[1,0]
-
-    Eobsbias2series = Eobsmask - obsstats[0,0]
-    obsbias2series = obsmask - obsstats[1,0]
-
-    Eabssimbias2series = np.abs(Esimbias2series)
-    abssimbias2series = np.abs(simbias2series)
-    Eabsobsbias2series = np.where(Eobsbias2series<0, -Eobsbias2series, Eobsbias2series)
-    absobsbias2series = np.where(obsbias2series<0, -obsbias2series, obsbias2series)
-
-    Eabssimobsbias2series=(Eabssimbias2series+Eabsobsbias2series)*(Eabssimbias2series+\
-      Eabsobsbias2series)
-    abssimobsbias2series = (abssimbias2series+absobsbias2series)*(abssimbias2series +\
-      absobsbias2series)
-
-    simobsstats[0,11] = 1. - Esumdiff2/(Eabssimobsbias2series.sum())
-    simobsstats[1,11] = 1. - sumdiff2/(abssimobsbias2series.sum())
+        simbias2series = arrayvals[:,0] - obsstats[kst,0]
+        obsbias2series = obsmask - obsstats[kst,0]
+
+        abssimbias2series = np.abs(simbias2series)
+        absobsbias2series = np.where(obsbias2series<0, -obsbias2series,              \
+          obsbias2series)
+
+        abssimobsbias2series = (abssimbias2series+absobsbias2series)*(               \
+          abssimbias2series + absobsbias2series)
+
+        simobsstats[kst,11] = 1. - sumdiff2/(abssimobsbias2series.sum())
 # Fractional Mean Bias (FMB)
-    simobsstats[0,12]=(simstats[0,0]-obsstats[0,0])/(0.5*(simstats[0,0]+obsstats[0,0]))
-    simobsstats[1,12]=(simstats[1,0]-obsstats[1,0])/(0.5*(simstats[1,0]+obsstats[1,0]))
-
-# Statistics around sim values exact
-    Earoundstats = np.zeros((5,Nexactt), dtype=np.float)
-    for it in range(Nexactt):
-        Earoundstats[0,it] = np.min(EsimobsSvalues[it,])
-        Earoundstats[1,it] = np.max(EsimobsSvalues[it,])
-        Earoundstats[2,it] = np.mean(EsimobsSvalues[it,])
-        Earoundstats[3,it] = np.mean(EsimobsSvalues[it,]*EsimobsSvalues[it,])
-        Earoundstats[4,it] = np.sqrt(Earoundstats[3,it] - Earoundstats[2,it]*        \
-          Earoundstats[2,it])
-
-# Statistics around sim values between
-    aroundstats = np.zeros((5,Ncoindt), dtype=np.float)
-    for it in range(Ncoindt):
-        aroundstats[0,it] = np.min(simobsSvalues[it,])
-        aroundstats[1,it] = np.max(simobsSvalues[it,])
-        aroundstats[2,it] = np.mean(simobsSvalues[it,])
-        aroundstats[3,it] = np.mean(simobsSvalues[it,]*simobsSvalues[it,])
-        aroundstats[4,it] = np.sqrt(aroundstats[3,it] - aroundstats[2,it]*           \
-          aroundstats[2,it])
-
-# exact sim Values to netCDF
-    newvar = onewnc.createVariable(valvars[ivar][0] + '_Esim', 'f', ('time'),        \
-      fill_value=fillValueF)
-    descvar = 'exact time simulated: ' + valvars[ivar][0]
-    basicvardef(newvar, valvars[ivar][0], descvar, ovobs.getncattr('units'))
-    print 'Lluis NEarratvals:',len(Earrayvals[:,0])
-    newvar[:] = Earrayvals[:,0]
-
-# exact obs Values to netCDF
-    newvar = onewnc.createVariable(valvars[ivar][1] + '_Eobs', 'f', ('time'),        \
-      fill_value=fillValueF)
-    descvar = 'exact time observed: ' + valvars[ivar][1]
-    basicvardef(newvar, valvars[ivar][1], descvar, ovobs.getncattr('units'))
-    newvar[:] = Earrayvals[:,1]
-
-# between sim Values to netCDF
-    newvar = onewnc.createVariable(valvars[ivar][0] + '_sim', 'f', ('betweentime'),  \
-      fill_value=fillValueF)
-    descvar = 'between observed time simulated: ' + valvars[ivar][0]
-    basicvardef(newvar, valvars[ivar][0], descvar, ovobs.getncattr('units'))
-    newvar[:] = arrayvals[:,0]
-
-# between obs Values to netCDF
-    newvar = onewnc.createVariable(valvars[ivar][1] + '_obs', 'f', ('obstime'),      \
-      fill_value=fillValueF)
-    descvar = 'observed: ' + valvars[ivar][1]
-    basicvardef(newvar, valvars[ivar][1], descvar, ovobs.getncattr('units'))
-    newvar[:] = arrayvals[:,1]
-
-# Around values exact
-    if not onewnc.variables.has_key(valvars[ivar][0] + 'Earound'):
+        simobsstats[ksy,12]=(simstats[ksy,0]-obsstats[kst,0])/(0.5*(simstats[kst,0] +\
+          obsstats[kst,0]))
+
+# Statistics around sim values
+        aroundstats = np.zeros((5,Ncoindt), dtype=np.float)
+        for it in range(Ncoindt):
+            aroundstats[0,it] = np.min(simobsSvalues[it,])
+            aroundstats[1,it] = np.max(simobsSvalues[it,])
+            aroundstats[2,it] = np.mean(simobsSvalues[it,])
+            aroundstats[3,it] = np.mean(simobsSvalues[it,]*simobsSvalues[it,])
+            aroundstats[4,it] = np.sqrt(aroundstats[3,it] - aroundstats[2,it]*       \
+              aroundstats[2,it])
+
+# sim Values to netCDF
+        newvar = onewnc.createVariable(valvars[ivar][0] + '_' + stdescsim[kst] +     \
+          'sim', 'f', (timedn), fill_value=fillValueF)
+        descvar = prestdescsim[kst] + ' time simulated: ' + valvars[ivar][0]
+        basicvardef(newvar, valvars[ivar][0], descvar, ovobs.getncattr('units'))
+        newvar[:] = arrayvals[:,0]
+
+# obs Values to netCDF
+        newvar = onewnc.createVariable(valvars[ivar][1] + '_' + stdescsim[kst] +     \
+          'obs', 'f', (timedn), fill_value=fillValueF)
+        descvar = prestdescsim[kst] + ' time simulated: ' + valvars[ivar][1]
+        basicvardef(newvar, valvars[ivar][1], descvar, ovobs.getncattr('units'))
+        newvar[:] = arrayvals[:,1]
+
+# Around values
+        if not onewnc.variables.has_key(valvars[ivar][0] + 'around'):
+            vname = prestdescsim[kst] + valvars[ivar][0] + 'around'
+        else:
+            vname = prestdescsim[kst] + valvars[ivar][0] + 'Around'
+
         if dims.has_key('Z'):
             if not onewnc.dimensions.has_key('zaround'):
                 newdim = onewnc.createDimension('zaround',Ngrid*2+1)
-                newvar = onewnc.createVariable(valvars[ivar][0] + 'Earound', 'f',    \
-                  ('time','zaround','yaround','xaround'), fill_value=fillValueF)
+                newvar = onewnc.createVariable(vname, 'f', (timedn,'zaround',        \
+                  'yaround','xaround'), fill_value=fillValueF)
         else:
-            newvar = onewnc.createVariable(valvars[ivar][0] + 'Earound', 'f',        \
-              ('time','yaround','xaround'), fill_value=fillValueF)
-
-        descvar = 'exact around simulated values +/- grid values: ' + valvars[ivar][0]
-        basicvardef(newvar, varsimobs + 'Earound', descvar, ovobs.getncattr('units'))
-        newvar[:] = EsimobsSvalues
-
-# Around values between
-    if not onewnc.variables.has_key(valvars[ivar][0] + 'around'):
-        if dims.has_key('Z'):
-            if not onewnc.dimensions.has_key('zaround'):
-                newdim = onewnc.createDimension('zaround',Ngrid*2+1)
-                newvar = onewnc.createVariable(valvars[ivar][0] + 'around', 'f',     \
-                  ('betweentime','zaround','yaround','xaround'),fill_value=fillValueF)
+            newvar = onewnc.createVariable(vname, 'f', (timedn,'yaround','xaround'), \
+              fill_value=fillValueF)
+
+        descvar = prestdescsim[kst] + 'around simulated values +/- grid values: ' +  \
+          valvars[ivar][0]
+        basicvardef(newvar, vname, descvar, ovobs.getncattr('units'))
+        newvar[:] = simobsSvalues
+
+
+# around sim Statistics between
+        if not searchInlist(onewnc.variables,prestdescsim[kst] + valvars[ivar][0] +  \
+          'staround'):
+            vname = prestdescsim[kst] + valvars[ivar][0] + 'staround'
         else:
-            newvar = onewnc.createVariable(valvars[ivar][0] + 'around', 'f',         \
-              ('betweentime','yaround','xaround'), fill_value=fillValueF)
-
-        descvar = 'around simulated values +/- grid values: ' + valvars[ivar][0]
-        basicvardef(newvar, varsimobs + 'around', descvar, ovobs.getncattr('units'))
-        newvar[:] = simobsSvalues
+            vname = prestdescsim[kst] + valvars[ivar][0] + 'Staround'
+
+        newvar = onewnc.createVariable(vname, 'f', (timedn,'stats'),                 \
+          fill_value=fillValueF)
+        descvar = prestdescsim[kst] + ' around (' +  str(Ngrid) + ', ' + str(Ngrid) +\
+          ') simulated statisitcs: ' + valvars[ivar][0]
+        basicvardef(newvar, vname, descvar, ovobs.getncattr('units'))
+        set_attribute(newvar, 'cell_methods', 'time_bnds')
+        newvar[:] = aroundstats.transpose()
+
+        if stdescsim[kst] == 'B':
+            if not searchInlist(onewnc.variables, 'time_bnds'):
+                newvar = onewnc.createVariable('time_bnds','f8',(imedn,'bnds'))
+                basicvardef(newvar, 'time_bnds', timedn, obstunits )
+                set_attribute(newvar, 'calendar', 'standard')
+                newvar[:] = simobsTtvalues
+
+# around obs Statistics between
+        if not searchInlist(onewnc.variables, prestdescsim[kst] + valvars[ivar][1] + \
+          'staround'):
+            vname = prestdescsim[kst] + valvars[ivar][1] + 'staround'
+        else:
+            vname = prestdescsim[kst] + valvars[ivar][1] + 'Staround'
+
+        newvar = onewnc.createVariable(vname, 'f', (timedn,'tstats'),                \
+          fill_value=fillValueF)
+        descvar = prestdescsim[kst] + ' around temporal observed statisitcs: ' +     \
+          valvars[ivar][1]
+        basicvardef(newvar, vname, descvar, ovobs.getncattr('units'))
+        set_attribute(newvar, 'cell_methods', 'time_bnds')
+        newvar[:] = aroundostats.transpose()
 
 # sim Statistics
     if not searchInlist(onewnc.variables,valvars[ivar][0] + 'stsim'):
-        newvar = onewnc.createVariable(valvars[ivar][0] + 'stsim', 'f', ('Nstsim',   \
-          'stats'), fill_value=fillValueF)
-        descvar = 'simulated statisitcs: ' + valvars[ivar][0]
-        basicvardef(newvar, valvars[ivar][0] + 'stsim', descvar,                     \
-          ovobs.getncattr('units'))
-        newvar[:] = simstats
+        vname = valvars[ivar][0] + 'stsim'
+    else:
+        vname = valvars[ivar][0] + 'stSim'
+
+    newvar = onewnc.createVariable(vname, 'f', ('Nstsim', 'stats'),                  \
+      fill_value=fillValueF)
+    descvar = 'simulated statisitcs: ' + valvars[ivar][0]
+    basicvardef(newvar, vname, descvar, ovobs.getncattr('units'))
+    newvar[:] = simstats
 
 # obs Statistics
     if not searchInlist(onewnc.variables,valvars[ivar][1] + 'stobs'):
-        newvar = onewnc.createVariable(valvars[ivar][1] + 'stobs', 'f', ('Nstsim',   \
-          'stats'), fill_value=fillValueF)
-        descvar = 'observed statisitcs: ' + valvars[ivar][1]
-        basicvardef(newvar, valvars[ivar][1] + 'stobs', descvar,                     \
-          ovobs.getncattr('units'))
-        newvar[:] = obsstats
+        vname = valvars[ivar][1] + 'stobs'
+    else:
+        vname = valvars[ivar][1] + 'stObs'
+
+    newvar = onewnc.createVariable(vname, 'f', ('Nstsim', 'stats'),                  \
+      fill_value=fillValueF)
+    descvar = 'observed statisitcs: ' + valvars[ivar][1]
+    basicvardef(newvar, vname, descvar, ovobs.getncattr('units'))
+    newvar[:] = obsstats
 
 # sim-obs Statistics
     if not searchInlist(onewnc.variables,varsimobs + 'st'):
-        newvar = onewnc.createVariable(varsimobs + 'st', 'f', ('Nstsim', 'gstats'),  \
-          fill_value=fillValueF)
-        descvar = 'simulated-observed tatisitcs: ' + varsimobs
-        basicvardef(newvar, varsimobs + 'st', descvar, ovobs.getncattr('units'))
-        newvar[:] = simobsstats
-
-# around sim Statistics exact
-    if not searchInlist(onewnc.variables,valvars[ivar][0] + 'Estaround'):
-        newvar = onewnc.createVariable(valvars[ivar][0] + 'Estaround', 'f',          \
-          ('time','stats'), fill_value=fillValueF)
-        descvar = 'exact around (' +  str(Ngrid) + ', ' + str(Ngrid) +               \
-          ') simulated statisitcs: ' + valvars[ivar][0]
-        basicvardef(newvar, valvars[ivar][0] + 'Estaround', descvar,                 \
-          ovobs.getncattr('units'))
-        set_attribute(newvar, 'cell_methods', 'Etime_bnds')
-        newvar[:] = Earoundstats.transpose()
-
-    if not searchInlist(onewnc.variables, 'Etime_bnds'):
-        newvar = onewnc.createVariable('Etime_bnds','f8',('time','bnds'))
-        basicvardef(newvar, 'Etime_bnds', 'betweentime', obstunits )
-        set_attribute(newvar, 'calendar', 'standard')
-        newvar[:] = EsimobsTtvalues
-
-# around obs Statistics exact
-    if not searchInlist(onewnc.variables,valvars[ivar][1] + 'Estaround'):
-        newvar = onewnc.createVariable(valvars[ivar][1] + 'Estaround', 'f',          \
-          ('time','tstats'), fill_value=fillValueF)
-        descvar = 'exact around temporal observed statisitcs: ' + valvars[ivar][1]
-        basicvardef(newvar, valvars[ivar][1] + 'Estaround', descvar,                  \
-          ovobs.getncattr('units'))
-        set_attribute(newvar, 'cell_methods', 'Etime_bnds')
-
-        newvar[:] = aroundostats.transpose()
-
-# around sim Statistics between
-    if not searchInlist(onewnc.variables,valvars[ivar][0] + 'staround'):
-        newvar = onewnc.createVariable(valvars[ivar][0] + 'staround', 'f',           \
-          ('betweentime','stats'), fill_value=fillValueF)
-        descvar = 'between around (' +  str(Ngrid) + ', ' + str(Ngrid) +             \
-          ') simulated statisitcs: ' + valvars[ivar][0]
-        basicvardef(newvar, valvars[ivar][0] + 'staround', descvar,                  \
-          ovobs.getncattr('units'))
-        set_attribute(newvar, 'cell_methods', 'time_bnds')
-        newvar[:] = aroundstats.transpose()
-
-    if not searchInlist(onewnc.variables, 'time_bnds'):
-        newvar = onewnc.createVariable('time_bnds','f8',('betweentime','bnds'))
-        basicvardef(newvar, 'time_bnds', 'betweentime', obstunits )
-        set_attribute(newvar, 'calendar', 'standard')
-        newvar[:] = simobsTtvalues
-
-# around obs Statistics between
-    if not searchInlist(onewnc.variables,valvars[ivar][1] + 'staround'):
-        newvar = onewnc.createVariable(valvars[ivar][1] + 'staround', 'f',           \
-          ('betweentime','tstats'), fill_value=fillValueF)
-        descvar = 'betweem around temporal observed statisitcs: ' + valvars[ivar][1]
-        basicvardef(newvar, valvars[ivar][1] + 'staround', descvar,                  \
-          ovobs.getncattr('units'))
-        set_attribute(newvar, 'cell_methods', 'time_bnds')
-        newvar[:] = aroundostats.transpose()
-
-        onewnc.sync()
+        vname = varsimobs + 'st'
+    else:
+        vname = varSimobs + 'st'
+
+    newvar = onewnc.createVariable(vname, 'f', ('Nstsim', 'gstats'),                 \
+      fill_value=fillValueF)
+    descvar = 'simulated-observed tatisitcs: ' + varsimobs
+    basicvardef(newvar, vname, descvar, ovobs.getncattr('units'))
+    newvar[:] = simobsstats
+
+    onewnc.sync()
 
 if trjsim is not None: