different time profiles for different countries

da/ccffdas/category_info.py

@@ -49,31 +49,11 @@ categories = {
   'CO2.uncertainty': 'n',
   'CO': -7.52,
   'CO.uncertainty': 'l'},
- 'cars highway': {'name': 'cars highway',
+ 'cars': {'name': 'cars',
   'model': 1,
   'spatial': 'Road transport',
-  'temporal': 't_carhw',
-  'fraction_of_total': 0.47,
-  'emission_factors': 36200000,
-  'CO2' : 1,
-  'CO2.uncertainty': 'n',
-  'CO': -4.32,
-  'CO.uncertainty': 'l'},
- 'cars middle road': {'name': 'cars middle road',
-  'model': 1,
-  'spatial': 'Road transport',
-  'temporal': 't_carmr',
-  'fraction_of_total': 0.28,
-  'emission_factors': 36200000,
-  'CO2' : 1,
-  'CO2.uncertainty': 'n',
-  'CO': -4.32,
-  'CO.uncertainty': 'l'},
- 'cars urban road': {'name': 'cars urban road',
-  'model': 1,
-  'spatial': 'Road transport',
-  'temporal': 't_carur',
-  'fraction_of_total': 0.25,
+  'temporal': 't_road',
+  'fraction_of_total': 1,
   'emission_factors': 36200000,
   'CO2' : 1,
   'CO2.uncertainty': 'n',
@@ -82,7 +62,7 @@ categories = {
  'heavy duty highway': {'name': 'heavy duty highway',
   'model': 1,
   'spatial': 'Road transport',
-  'temporal': 't_hdvhw',
+  'temporal': 't_road',
   'fraction_of_total': 0.56,
   'emission_factors': 36650000,
   'CO2' : 1,
@@ -92,7 +72,7 @@ categories = {
  'heavy duty middle road': {'name': 'heavy duty middle road',
   'model': 1,
   'spatial': 'Road transport',
-  'temporal': 't_hdvmr',
+  'temporal': 't_road',
   'fraction_of_total': 0.24,
   'emission_factors': 36650000,
   'CO2' : 1,
@@ -102,7 +82,7 @@ categories = {
  'heavy duty urban': {'name': 'heavy duty urban',
   'model': 1,
   'spatial': 'Road transport',
-  'temporal': 't_hdvur',
+  'temporal': 't_road',
   'fraction_of_total': 0.2,
   'emission_factors': 36650000,
   'CO2' : 1,

da/ccffdas/energy_use_country.py

 energy_use_per_country = {
 'AUS': {
         'Public power': 161.01,
+        'Public power ratio gas': 0.78,
         'Industry': 158.02,
         'Other stationary combustion': 120.04,
         'Road transport': 150.8,
@@ -8,6 +9,7 @@ energy_use_per_country = {
         },
  'BEL': {
         'Public power': 254.82,
+        'Public power ratio gas': 0.72,
         'Industry': 203.68,
         'Other stationary combustion': 383.16,
         'Road transport': 172.455,
@@ -15,12 +17,14 @@ energy_use_per_country = {
         },
  'CZE': {
         'Public power': 582.9,
+        'Public power ratio gas': 0.10,
         'Industry': 140.13,
         'Other stationary combustion': 178.46,
         'Road transport': 121.935,
         'Shipping': 0.17},
  'FRA': {
         'Public power': 585.52,
+        'Public power ratio gas': 0.56,
         'Industry': 731.9,
         'Other stationary combustion': 1262.1,
         'Road transport': 845.07,
@@ -28,6 +32,7 @@ energy_use_per_country = {
         },
  'DEU': {
         'Public power': 3515.68,
+        'Public power ratio gas': 0.19,
         'Industry': 1516.34,
         'Other stationary combustion': 2069.16,
         'Road transport': 1066.98,
@@ -35,6 +40,7 @@ energy_use_per_country = {
         },
  'LUX': {
         'Public power': 3.75,
+        'Public power ratio gas': 0.99,
         'Industry': 15.23,
         'Other stationary combustion': 25.22,
         'Road transport': 37.045,
@@ -42,6 +48,7 @@ energy_use_per_country = {
         },
  'NED': {
         'Public power': 868.96,
+        'Public power ratio gas': 0.47,
         'Industry': 427.43,
         'Other stationary combustion': 599.52,
         'Road transport': 199.74,
@@ -49,6 +56,7 @@ energy_use_per_country = {
         },
  'POL': {
         'Public power': 1702.42,
+        'Public power ratio gas': 0.05,
         'Industry': 338.92,
         'Other stationary combustion': 618.77,
         'Road transport': 362.94,
@@ -56,6 +64,7 @@ energy_use_per_country = {
         },
  'CHE': {
         'Public power': 43.41,
+        'Public power ratio gas': 0.53,
         'Industry': 66.5,
         'Other stationary combustion': 194.77,
         'Road transport': 100.28,
@@ -63,6 +72,7 @@ energy_use_per_country = {
         },
  'GBR': {
         'Public power': 1716.77,
+        'Public power ratio gas': 0.76,
         'Industry': 620.58,
         'Other stationary combustion': 1485.23,
         'Road transport': 787.845,

da/ccffdas/observationoperator.py

@@ -207,10 +207,11 @@ class STILTObservationOperator(ObservationOperator):
 
         self.paramdict = rc.read(dacycle.dasystem['paramdict'])
         self.pftfile = dacycle.dasystem['file.pft']
-        with rasterio.Env(logging.getLogger().setLevel(logging.DEBUG)):
+        with rasterio.Env(logging.getLogger().setLevel(logging.INFO)):
             with rasterio.open(self.pftfile) as dataset:
                 self.pft_shape_orig = dataset.read().shape[-2:]
 
+        logging.getLogger().setLevel(logging.DEBUG)
         self.lon_start = float(dacycle.dasystem['domain.lon.start'])
         self.lon_end =   float(dacycle.dasystem['domain.lon.end'])
         self.lat_start = float(dacycle.dasystem['domain.lat.start'])
@@ -353,11 +354,12 @@ class STILTObservationOperator(ObservationOperator):
                      for i in range(2)])
         logging.debug('Shape for paint-by-number: {}'.format(new_shape))
 
-        with rasterio.Env(logging.getLogger().setLevel(logging.DEBUG)):
+        with rasterio.Env(logging.getLogger().setLevel(logging.INFO)):
             with rasterio.open(self.pftfile) as dataset:
                 pftdata = dataset.read(out_shape=new_shape,
                                     resampling=Resampling.mode)
                 pftdata = pftdata.reshape(pftdata.shape[1:])
+        logging.getLogger().setLevel(logging.DEBUG)
 
         scaling_factors = np.array(np.array(new_shape) / np.array(self.lu_pref[0].shape), dtype=int)
 
@@ -388,7 +390,9 @@ class STILTObservationOperator(ObservationOperator):
             for mem, values in enumerate(all_param_values):
                 param_values = all_param_values[mem]
                 index_in_state = find_in_state(param_values, 'BIO', str(lutype), return_index=True)
-                if index_in_state: param_value = param_values[index_in_state]
+                if index_in_state:
+                    param_value = param_values[index_in_state]
+                    print(param_value)
                 else: param_value = 1
                 nee_lut_mem = (nee_lut.sum(axis=0) * mask) * param_value
                 nee_lut_mem_scaled = average2d(nee_lut_mem, domain_shape)

da/ccffdas/stilt-ops_urbanall.rc

@@ -17,16 +17,17 @@ do.c14integrated:    0
 do.c14targeted:      0
 obs.spec.name  : CO2
 ! number of emission categories defined in the emission model
-obs.cat.nr     : 14
+obs.cat.nr     : 12
 ! For Rdam obs
 obs.sites.rc        : ${datadir}/sites_weights2.rc
 ! number of parameters
 ! In the covmatrix and statevector, the ff parameters are first, then the bio parameters!
 nffparameters     : 24
-nbioparameters    : 6
+nbioparameters    : 8
 nparameters       : ${nffparameters} + ${nbioparameters}
 
-file.pft          : /projects/0/ctdas/RINGO/inversions/Data/SiBPFTs.nc
+file.pft          : ${datadir}/SiBPFTs.nc
+file.timeprofs    : ${datadir}/CAMS_TEMPO_Tprof_subset.nc
 
 ! Settings for the domain:
 domain.lon.start  : -5.95
@@ -36,7 +37,7 @@ domain.lat.end    : 54.975
 domain.lon.num    : 260
 domain.lat.num    : 240
 
-paramdict         : ${datadir}/paramdict.rc
+paramdict         : ${datadir}/paramdict_sib.rc
 ! set fixed seed for random number generator, or use 0 if you want to use any random seed
 random.seed     : 4385
 !file with prior estimate of scaling factors (statevector) and covariances

develop.rc.easy

@@ -28,7 +28,7 @@
 !
 ! The time for which to start and end the data assimilation experiment in format YYYY-MM-DD HH:MM:SS
 
-time.start          : 2016-01-04 00:00:00
+time.start          : 2016-01-06 00:00:00
 time.finish         : 2016-01-20 00:00:00
 time.fxstart        : 2016-01-01 00:00:00