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Commit 8ffdbf8c authored by brunner's avatar brunner
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no cdo afternoon mean

parent 640e5e92
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config.rc
View file @ 8ffdbf8c
...@@ -29,17 +29,16 @@ ...@@ -29,17 +29,16 @@
! The time for which to start and end the data assimilation experiment in format YYYY-MM-DD HH:MM:SS ! The time for which to start and end the data assimilation experiment in format YYYY-MM-DD HH:MM:SS
! the following 3 lines are for initial start ! the following 3 lines are for initial start
time.start : 2019-01-01 00:00:00 time.start : 2019-03-01 00:00:00
time.finish : 2019-01-07 23:00:00 time.finish : 2019-03-07 23:00:00
time.end : 2019-01-07 23:00:00 time.end : 2019-03-07 23:00:00
abs.time.start : 2019-03-01 00:00:00
abs.time.start : 2019-01-01 00:00:00
! Whether to restart the CTDAS system from a previous cycle, or to start the sequence fresh. Valid entries are T/F/True/False/TRUE/FALSE ! Whether to restart the CTDAS system from a previous cycle, or to start the sequence fresh. Valid entries are T/F/True/False/TRUE/FALSE
time.restart : F time.restart : F
!da.restart.tstamp : 2013-01-08 00:00:00 ! da.restart.tstamp : 2019-03-08 00:00:00
da.restart.tstamp : 2013-01-01 00:00:00 da.restart.tstamp : 2019-03-01 00:00:00
! The length of a cycle is given in days, such that the integer 7 denotes the typically used weekly cycle. Valid entries are integers > 1 ! The length of a cycle is given in days, such that the integer 7 denotes the typically used weekly cycle. Valid entries are integers > 1
...@@ -54,6 +53,7 @@ time.nlag : 2 ...@@ -54,6 +53,7 @@ time.nlag : 2
run.name : real run.name : real
dir.da_run : /scratch/snx3000/parsenov/${run.name} dir.da_run : /scratch/snx3000/parsenov/${run.name}
dir.ct_save : /store/empa/em05/parsenov/ct_data/${run.name}/
restartmap.dir : ${dir.da_run}/input restartmap.dir : ${dir.da_run}/input
! The resources used to complete the data assimilation experiment. This depends on your computing platform. ! The resources used to complete the data assimilation experiment. This depends on your computing platform.
...@@ -86,6 +86,7 @@ da.system : CarbonTracker ...@@ -86,6 +86,7 @@ da.system : CarbonTracker
! The specific settings for your system are read from a separate rc-file, which points to the data directories, observations, etc ! The specific settings for your system are read from a separate rc-file, which points to the data directories, observations, etc
da.system.rc : da/rc/carbontracker_cosmo.rc da.system.rc : da/rc/carbontracker_cosmo.rc
locations : /store/empa/em05/parsenov/ct_data/locations.csv
! This flag should probably be moved to the da.system.rc file. It denotes which type of filtering to use in the optimizer ! This flag should probably be moved to the da.system.rc file. It denotes which type of filtering to use in the optimizer
......
cosmo.py
View file @ 8ffdbf8c
...@@ -85,5 +85,3 @@ save_weekly_avg_ext_tc_data(dacycle) ...@@ -85,5 +85,3 @@ save_weekly_avg_ext_tc_data(dacycle)
write_mole_fractions(dacycle) write_mole_fractions(dacycle)
sys.exit(0) sys.exit(0)
da/cosmo/base_optimizer.py
View file @ 8ffdbf8c
...@@ -313,6 +313,8 @@ class Optimizer(object): ...@@ -313,6 +313,8 @@ class Optimizer(object):
def serial_minimum_least_squares(self): def serial_minimum_least_squares(self):
""" Make minimum least squares solution by looping over obs""" """ Make minimum least squares solution by looping over obs"""
# Corrected for bias over all stations
bias = np.mean(self.obs) - np.mean(self.Hx)
for n in range(self.nobs): for n in range(self.nobs):
# Screen for flagged observations (for instance site not found, or no sample written from model) # Screen for flagged observations (for instance site not found, or no sample written from model)
...@@ -322,13 +324,15 @@ class Optimizer(object): ...@@ -322,13 +324,15 @@ class Optimizer(object):
continue continue
# Screen for outliers greather than 3x model-data mismatch, only apply if obs may be rejected # Screen for outliers greather than 3x model-data mismatch, only apply if obs may be rejected
# Calculate residual for rejecting the observations (corrected for bias) - res_rej
res_rej = self.obs[n] - self.Hx[n] - bias
res = self.obs[n] - self.Hx[n] res = self.obs[n] - self.Hx[n]
if self.may_reject[n]: if self.may_reject[n]:
threshold = self.rejection_threshold * np.sqrt(self.R[n]) threshold = self.rejection_threshold * np.sqrt(self.R[n])
if np.abs(res) > threshold: #if np.abs(res) > threshold + abs(bias):
logging.debug('Rejecting observation (%s,%i) because residual (%f) exceeds threshold (%f)' % (self.sitecode[n], self.obs_ids[n], res, threshold)) if np.abs(res_rej) > threshold:
logging.debug('Rejecting observation (%s,%i) because residual (%f) exceeds threshold (%f)' % (self.sitecode[n], self.obs_ids[n], res, threshold + abs(bias)))
self.flags[n] = 2 self.flags[n] = 2
continue continue
......
da/cosmo/covariances.py
View file @ 8ffdbf8c
...@@ -64,17 +64,29 @@ class CO2StateVector(StateVector): ...@@ -64,17 +64,29 @@ class CO2StateVector(StateVector):
fullcov = np.zeros(shape=(90,90)) fullcov = np.zeros(shape=(90,90))
# partcov = np.array([ \
# (0.64, 0.36, 0.16, 0.16, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \
# (0.36, 0.64, 0.16, 0.16, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \
# (0.16, 0.16, 0.64, 0.36, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \
# (0.16, 0.16, 0.36, 0.64, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \
# (0.16, 0.16, 0.16, 0.16, 0.64, 0.36, 0.04, 0.04, 0.04, 0.01), \
# (0.16, 0.16, 0.16, 0.16, 0.36, 0.64, 0.04, 0.04, 0.04, 0.01), \
# (0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.64, 0.16, 0.16, 0.16), \
#(0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.16, 0.64, 0.16, 0.16), \
# (0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.16, 0.16, 0.64, 0.16), \
# (0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.16, 0.16, 0.16, 0.64) ])
partcov = np.array([ \ partcov = np.array([ \
(0.64, 0.36, 0.16, 0.16, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \ (0.1089, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900), \
(0.36, 0.64, 0.16, 0.16, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \ (0.0900, 0.1089, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900), \
(0.16, 0.16, 0.64, 0.36, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \ (0.0900, 0.0900, 0.1089, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900), \
(0.16, 0.16, 0.36, 0.64, 0.16, 0.16, 0.04, 0.04, 0.04, 0.01), \ (0.0900, 0.0900, 0.0900, 0.1089, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900), \
(0.16, 0.16, 0.16, 0.16, 0.64, 0.36, 0.04, 0.04, 0.04, 0.01), \ (0.0900, 0.0900, 0.0900, 0.0900, 0.1089, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900), \
(0.16, 0.16, 0.16, 0.16, 0.36, 0.64, 0.04, 0.04, 0.04, 0.01), \ (0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.1089, 0.0900, 0.0900, 0.0900, 0.0900), \
(0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.64, 0.16, 0.16, 0.16), \ (0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.1089, 0.0900, 0.0900, 0.0900), \
(0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.16, 0.64, 0.16, 0.16), \ (0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.1089, 0.0900, 0.0900), \
(0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.16, 0.16, 0.64, 0.16), \ (0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.1089, 0.0900), \
(0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.16, 0.16, 0.16, 0.64) ]) (0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.0900, 0.1089) ])
# L = 300 km # L = 300 km
......
da/cosmo/observationoperator_cosmo.py
View file @ 8ffdbf8c
...@@ -5,23 +5,22 @@ import logging ...@@ -5,23 +5,22 @@ import logging
import os import os
import sys import sys
import subprocess import subprocess
import csv
import da.cosmo.io4 as io import da.cosmo.io4 as io
import numpy as np import numpy as np
from netCDF4 import Dataset from netCDF4 import Dataset
from scipy import interpolate
from datetime import datetime, timedelta from datetime import datetime, timedelta
from dateutil import rrule from dateutil import rrule
from cdo import *
from . import site_height from . import site_height
from da.cosmo.icbc4ctdas import ct
from itertools import repeat from itertools import repeat
from multiprocessing import Pool from multiprocessing import Pool
from da.tools.general import to_datetime from da.tools.general import to_datetime
import amrs.misc.transform as transform
identifier = 'ObservationOperator' identifier = 'ObservationOperator'
version = '10' version = '10'
#cdo = Cdo()
cdo = Cdo(logging=True, logFile='cdo_commands.log')
################### Begin Class ObservationOperator ################### ################### Begin Class ObservationOperator ###################
class ObservationOperator(object): class ObservationOperator(object):
...@@ -57,15 +56,19 @@ class ObservationOperator(object): ...@@ -57,15 +56,19 @@ class ObservationOperator(object):
self.simulated_file = os.path.join(self.outputdir, 'samples_simulated.%s.nc' % self.dacycle['time.sample.stamp']) 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']) self.forecast_nmembers = int(self.dacycle['da.optimizer.nmembers'])
def run(self,lag,dacycle,statevector,advance=False): def run(self, lag, dacycle, statevector, advance=False):
members = statevector.ensemble_members[lag] members = statevector.ensemble_members[lag]
self.forecast_nmembers = int(self.dacycle['da.optimizer.nmembers']) self.forecast_nmembers = int(self.dacycle['da.optimizer.nmembers'])
self.nparams = int(self.dacycle['nparameters']) self.nparams = int(self.dacycle['nparameters'])
absolute_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H')) self.days = int(dacycle['time.cycle'])
absolute_start_time_ch = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y-%m-%d')) abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
abs_start_time_ch = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y-%m-%d'))
starth = abs((to_datetime(dacycle['abs.time.start'])-dacycle['time.start']).days)*24 starth = abs((to_datetime(dacycle['abs.time.start'])-dacycle['time.start']).days)*24
endh = abs((to_datetime(dacycle['abs.time.start'])-dacycle['time.finish']).days)*24 endh = abs((to_datetime(dacycle['abs.time.start'])-dacycle['time.finish']).days)*24
start = dacycle['time.start']
end = dacycle['time.finish']
f = io.CT_CDF(self.simulated_file, method='create') f = io.CT_CDF(self.simulated_file, method='create')
logging.debug('Creating new simulated observation file in ObservationOperator (%s)' % self.simulated_file) logging.debug('Creating new simulated observation file in ObservationOperator (%s)' % self.simulated_file)
...@@ -116,36 +119,41 @@ class ObservationOperator(object): ...@@ -116,36 +119,41 @@ class ObservationOperator(object):
for m in range(0,self.forecast_nmembers): for m in range(0,self.forecast_nmembers):
co2[m,:] = members[m].param_values co2[m,:] = members[m].param_values
# co2[co2<0] = 0.
l[:] = co2 l[:] = co2
ofile.close() ofile.close()
os.system('cp '+self.lambda_file+' '+dacycle['da.vprm']+'/lambdas.nc') os.system('cp '+self.lambda_file+' '+dacycle['da.vprm']+'/lambdas.nc')
os.chdir(dacycle['da.obsoperator.home']) os.chdir(dacycle['da.obsoperator.home'])
if os.path.exists(dacycle['dir.da_run']+'/'+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"): logging.info('Starting COSMO')
if os.path.exists(dacycle['dir.da_run']+"/non_opt_"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)+"/cosmo/output/"):
os.rename(dacycle['dir.da_run']+"/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), dacycle['dir.da_run']+"/old_non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168)) os.system('python run_chain.py '+self.dacycle['run.name']+' '+abs_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc int2lm post_int2lm oae octe online_vprm cosmo')
else:
os.rename(dacycle['dir.da_run']+"/"+absolute_start_time+"_"+str(starth+lag*168)+"_"+str(endh+lag*168), dacycle['dir.da_run']+"/non_opt_"+dacycle['time.start'].strftime('%Y%m%d%H')+"_"+str(starth+lag*168)+"_"+str(endh+lag*168))
os.system('python run_chain.py '+self.dacycle['run.name']+' '+absolute_start_time_ch+' '+str(starth+lag*168)+' '+str(endh+lag*168)+' -j meteo icbc int2lm post_int2lm oae octe online_vprm cosmo')
logging.info('COSMO done!') logging.info('COSMO done!')
os.chdir(dacycle['dir.da_run'])
# Here the extraction of COSMO output starts
dicts = self.read_csv(dacycle)
rlat, rlon, dicts, path_in = self.get_hhl_data(dacycle, lag, 'lffd'+abs_start_time+'c.nc', dicts, starth, endh)
logging.info('Starting parallel extraction \m/')
args = [ args = [
(dacycle, starth+168*lag, endh+168*lag-1, n) (dacycle, dacycle['time.sample.start']+timedelta(hours = 24*n), dicts, rlat, rlon, path_in)
for n in range(1,self.forecast_nmembers+1) for n in range(self.days)
] ]
with Pool(self.forecast_nmembers) as pool: with Pool(self.days) as pool:
pool.starmap(self.extract_model_data, args) pool.starmap(self.get_cosmo_data, args)
logging.info('Finished parallel extraction \m/')
self.cat_cosmo_data(advance, dacycle)
for i in range(0,self.forecast_nmembers): for i in range(0,self.forecast_nmembers):
idx = str(i+1).zfill(3) idx = str(i+1).zfill(3)
cosmo_file = os.path.join('/store/empa/em05/parsenov/cosmo_data/model_'+idx+'_%s.nc' % dacycle['time.sample.stamp']) cosmo_file = os.path.join(self.dacycle['dir.ct_save'], 'Hx_'+idx+'_%s.nc' % dacycle['time.sample.stamp'])
ifile = Dataset(cosmo_file, mode='r') ifile = Dataset(cosmo_file, mode='r')
model_data[i,:] = (np.squeeze(ifile.variables['CO2'][:])*29./44.01)*1E6 # in ppm model_data[i,:] = np.squeeze(ifile.variables['CO2'][:])
ifile.close() ifile.close()
for j,data in enumerate(zip(ids,obs,mdm)): for j,data in enumerate(zip(ids,obs,mdm)):
...@@ -159,195 +167,261 @@ class ObservationOperator(object): ...@@ -159,195 +167,261 @@ class ObservationOperator(object):
self.prepare_run() self.prepare_run()
self.run(lag, dacycle, statevector, advance) self.run(lag, dacycle, statevector, advance)
def extract_model_data(self, dacycle, hstart, hstop, ensnum): def read_csv(self, dacycle):
"""Reads csv file where information about stations is written"""
self.dacycle = dacycle ensnum = int(dacycle['da.optimizer.nmembers'])
time_stamp = dacycle['time.sample.stamp'] csvfile = dacycle['locations']
dicts = []
with open(csvfile) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
for e in range(ensnum):
e = str(e+1).zfill(3)
dicts = np.append(dicts, {'ensnum':e, 'name':row[1], 'lon':row[2], 'lat':row[3], \
'rlon':None, 'rlat':None, 'h1':None, 'h2':None, \
'hidx1':None, 'hidx2':None, \
'alt':float(row[4])+float(row[5]), 'time':[], 'co2':[], \
'co2_bg':[], 'co2_gpp':[], 'co2_ra':[], 'co2_a':[]})
return dicts
def get_hhl_data(self, dacycle, lag, ncc, dicts, starth, endh):
abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H')) abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
path_in = os.path.join(dacycle['dir.da_run'], abs_start_time+'_'+str(starth+lag*168)+'_'+str(endh+lag*168), "cosmo/output/")
hhl = np.empty(shape=(60))
hhl60 = np.empty(shape=(60, 300, 450))
with Dataset(path_in+ncc) as nc1:
rotpole = nc1.variables['rotated_pole']
pollon = rotpole.getncattr('grid_north_pole_longitude')
pollat = rotpole.getncattr('grid_north_pole_latitude')
rlat = nc1.variables['rlat'][:]
rlon = nc1.variables['rlon'][:]
hhl_3d = np.squeeze(nc1.variables['HHL'][:])
for h in range(0,60):
hhl60[h, :, :]=(hhl_3d[h, :, :]+hhl_3d[h+1, :, :])/2.
for station in dicts:
myrlon, myrlat = transform.rotpole2wgs(float(station['lon']), \
float(station['lat']), \
pollon, pollat, inverse=True)
for h in range(0,60):
hhl[h] = interpolate.interpn((rlat,rlon), hhl60[h,:,:], [myrlat,myrlon], method='linear')
if float(station['alt']) < hhl[59]:
station['h1'] = hhl[59]
station['h2'] = hhl[59]
station['hidx1'] = 59
station['hidx2'] = 59
station['alt'] = hhl[59]
cosmo_out = dacycle['dir.da_run']+"/"+abs_start_time+"_"+str(hstart)+"_"+str(hstop+1)+"/cosmo/output/" else:
hhl_cosmo_out = dacycle['dir.da_run']+"/"+abs_start_time+"_0_168/cosmo/output/" for l, ll in enumerate(hhl):
cosmo_save = "/store/empa/em05/parsenov/cosmo_data/" if float(station['alt']) < ll:
hhl_fn = hhl_cosmo_out+'lffd'+abs_start_time+'c.nc' station['h1'] = hhl[l]
station['h2'] = hhl[l+1]
ens = str(ensnum).zfill(3) station['hidx1'] = l
station['hidx2'] = l+1
files2cat_albs = [] # The following line: we interpolate on the middle of alt - lower level
files2cat_bntg = [] station['alt'] = float(station['alt']) - (float(station['alt']) - hhl[l+1])/2.
files2cat_brm = []
files2cat_chri = [] station['rlon'] = myrlon
files2cat_due1 = [] station['rlat'] = myrlat
files2cat_esmo = []
files2cat_frob = [] return rlat, rlon, dicts, path_in
files2cat_gimm = []
files2cat_hae = [] def get_cosmo_data(self, dacycle, date_begin, dicts, rlat, rlon, path_in):
files2cat_laeg = [] hours = ['12', '13', '14', '15']
files2cat_magn = [] qv_int = np.empty(shape=(60))
files2cat_payn = [] co2_bg_int = np.empty(shape=(60))
files2cat_reck = [] co2_gpp_int = np.empty(shape=(60))
files2cat_rig = [] co2_ra_int = np.empty(shape=(60))
files2cat_save = [] co2_a_int = np.empty(shape=(60))
files2cat_semp = [] qv_int = np.empty(shape=(60))
files2cat_sott = []
files2cat_ssal = [] co2_bg_daily = []
files2cat_taen = [] co2_gpp_daily = []
files2cat_zhbr = [] co2_ra_daily = []
files2cat_zsch = [] co2_a_daily = []
files2cat_zue = [] co2_daily = []
if ens == "001": for hrs in hours:
cdo.selname("HHL", input = hhl_fn, output = cosmo_out+"hhl.nc") with Dataset(path_in+'lffd'+date_begin.strftime("%Y%m%d")+hrs+'.nc') as nc2:
cdo.remapnn("lon=8.51_lat=47.31,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_albs.nc") qv = np.squeeze(nc2.variables['QV'][:])
cdo.remapnn("lon=7.53_lat=46.98,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_bntg.nc") co2_a = np.squeeze(nc2.variables['CO2_A'][:])
cdo.remapnn("lon=8.18_lat=47.19,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_brm.nc") for station in dicts:
cdo.remapnn("lon=7.69_lat=47.57,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_chri.nc") myrlat = station['rlat']
cdo.remapnn("lon=8.61_lat=47.40,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_due1.nc") myrlon = station['rlon']
cdo.remapnn("lon=8.57_lat=47.52,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_esmo.nc") e = station['ensnum']
cdo.remapnn("lon=7.90_lat=47.38,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_frob.nc") h1 = station['h1']
cdo.remapnn("lon=7.25_lat=47.05,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_gimm.nc") h2 = station['h2']
cdo.remapnn("lon=7.82_lat=47.31,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_hae.nc") i1 = station['hidx1']
cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_laeg.nc") i2 = station['hidx2']
cdo.remapnn("lon=8.93_lat=46.16,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_magn.nc")
cdo.remapnn("lon=6.94_lat=46.81,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_payn.nc") co2_bg = np.squeeze(nc2.variables['CO2_BG'+e][:])
cdo.remapnn("lon=8.52_lat=47.43,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_reck.nc") co2_gpp = np.squeeze(nc2.variables['CO2_GPP'+e][:])
cdo.remapnn("lon=8.46_lat=47.07,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_rig.nc") co2_ra = np.squeeze(nc2.variables['CO2_RA'+e][:])
cdo.remapnn("lon=7.36_lat=46.24,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_save.nc")
cdo.remapnn("lon=8.21_lat=47.12,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_semp.nc") for h in range(60):
cdo.remapnn("lon=6.74_lat=46.66,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_sott.nc") qv_int[h] = interpolate.interpn((rlat,rlon), qv[h,:,:], [myrlat,myrlon], method='linear')
cdo.remapnn("lon=8.95_lat=45.98,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_ssal.nc") co2_bg_int[h] = interpolate.interpn((rlat,rlon), co2_bg[h,:,:], [myrlat,myrlon], method='linear')
cdo.remapnn("lon=8.90_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_taen.nc") co2_gpp_int[h] = interpolate.interpn((rlat,rlon), co2_gpp[h,:,:], [myrlat,myrlon], method='linear')
cdo.remapnn("lon=8.57_lat=47.38,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_zhbr.nc") co2_ra_int[h] = interpolate.interpn((rlat,rlon), co2_ra[h,:,:], [myrlat,myrlon], method='linear')
cdo.remapnn("lon=8.52_lat=47.37,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_zsch.nc") co2_a_int[h] = interpolate.interpn((rlat,rlon), co2_a[h,:,:], [myrlat,myrlon], method='linear')
cdo.remapnn("lon=8.53_lat=47.38,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_zue.nc")
co2_bg1 = co2_bg_int[i1]
for dt in rrule.rrule(rrule.HOURLY, dtstart=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstart), until=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstop)): co2_bg2 = co2_bg_int[i2]
dt=dt.strftime('%Y%m%d%H') co2_gpp1 = co2_gpp_int[i1]
if ens == "001": co2_gpp2 = co2_gpp_int[i2]
logging.info('Extracting output for time %s' % (str(dt))) co2_ra1 = co2_ra_int[i1]
co2_in_fn = cosmo_out+'lffd'+dt+'.nc' co2_ra2 = co2_ra_int[i2]
co2_a1 = co2_a_int[i1]
co2_out_albs = cosmo_out+'CO2_albs_'+ens+'_'+dt+'.nc' co2_a2 = co2_a_int[i2]
co2_out_bntg = cosmo_out+'CO2_bntg_'+ens+'_'+dt+'.nc' co2_a2 = co2_a_int[i2]
co2_out_brm = cosmo_out+'CO2_brm_'+ens+'_'+dt+'.nc' qv1 = qv_int[i1]
co2_out_chri = cosmo_out+'CO2_chri_'+ens+'_'+dt+'.nc' qv2 = qv_int[i2]
co2_out_due1 = cosmo_out+'CO2_due1_'+ens+'_'+dt+'.nc' if h1 == h2:
co2_out_esmo = cosmo_out+'CO2_esmo_'+ens+'_'+dt+'.nc' co2_bg_final = co2_bg1
co2_out_frob = cosmo_out+'CO2_frob_'+ens+'_'+dt+'.nc' co2_gpp_final = co2_gpp1
co2_out_gimm = cosmo_out+'CO2_gimm_'+ens+'_'+dt+'.nc' co2_ra_final = co2_ra1
co2_out_hae = cosmo_out+'CO2_hae_'+ens+'_'+dt+'.nc' co2_a_final = co2_a1
co2_out_laeg = cosmo_out+'CO2_laeg_'+ens+'_'+dt+'.nc' qv_final = qv1
co2_out_magn = cosmo_out+'CO2_magn_'+ens+'_'+dt+'.nc' else:
co2_out_payn = cosmo_out+'CO2_payn_'+ens+'_'+dt+'.nc' co2_bg_final = co2_bg1 + (float(station['alt'])-h1)*(co2_bg2-co2_bg1)/(h2-h1)
co2_out_reck = cosmo_out+'CO2_reck_'+ens+'_'+dt+'.nc' co2_gpp_final = co2_gpp1 + (float(station['alt'])-h1)*(co2_gpp2-co2_gpp1)/(h2-h1)
co2_out_rig = cosmo_out+'CO2_rig_'+ens+'_'+dt+'.nc' co2_ra_final = co2_ra1 + (float(station['alt'])-h1)*(co2_ra2-co2_ra1)/(h2-h1)
co2_out_save = cosmo_out+'CO2_save_'+ens+'_'+dt+'.nc' co2_a_final = co2_a1 + (float(station['alt'])-h1)*(co2_a2-co2_a1)/(h2-h1)
co2_out_semp = cosmo_out+'CO2_semp_'+ens+'_'+dt+'.nc' qv_final = qv1 + (float(station['alt'])-h1)*(qv2-qv1)/(h2-h1)
co2_out_sott = cosmo_out+'CO2_sott_'+ens+'_'+dt+'.nc'
co2_out_ssal = cosmo_out+'CO2_ssal_'+ens+'_'+dt+'.nc' kgkg2ppm = 658941.149738696 / (1-qv_final)
co2_out_taen = cosmo_out+'CO2_taen_'+ens+'_'+dt+'.nc'
co2_out_zhbr = cosmo_out+'CO2_zhbr_'+ens+'_'+dt+'.nc' co2_bg_final = kgkg2ppm*co2_bg_final
co2_out_zsch = cosmo_out+'CO2_zsch_'+ens+'_'+dt+'.nc' co2_gpp_final = kgkg2ppm*co2_gpp_final
co2_out_zue = cosmo_out+'CO2_zue_'+ens+'_'+dt+'.nc' co2_ra_final = kgkg2ppm*co2_ra_final
co2_a_final = kgkg2ppm*co2_a_final
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.51_lat=47.31 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_albs) co2_final = co2_bg_final - co2_gpp_final + co2_ra_final + co2_a_final
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.53_lat=46.98 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_bntg)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.18_lat=47.19 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_brm) station['co2_bg'].append(co2_bg_final)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.69_lat=47.57 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_chri) station['co2_gpp'].append(co2_gpp_final)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.61_lat=47.40 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_due1) station['co2_ra'].append(co2_ra_final)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.57_lat=47.52 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_esmo) station['co2_a'].append(co2_a_final)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.90_lat=47.38 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_frob) station['co2'].append(co2_final)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.25_lat=47.05 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_gimm)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.82_lat=47.31 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_hae) for station in dicts:
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.40_lat=47.48 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_laeg) station['co2_bg'] = np.mean(np.asarray(station['co2_bg']))
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.93_lat=46.16 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_magn) station['co2_gpp'] = np.mean(np.asarray(station['co2_gpp']))
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=6.94_lat=46.81 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_payn) station['co2_ra'] = np.mean(np.asarray(station['co2_ra']))
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.52_lat=47.43 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_reck) station['co2_a'] = np.mean(np.asarray(station['co2_a']))
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.46_lat=47.07 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_rig) station['co2'] = np.mean(np.asarray(station['co2']))
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.36_lat=46.24 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_save) station['time'].append((date_begin - datetime(1970,1,1)).total_seconds())
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.21_lat=47.12 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_semp)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=6.74_lat=46.66 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_sott) self.write_cosmo_data(dacycle, dicts, date_begin)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.95_lat=45.98 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_ssal)
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.90_lat=47.48 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_taen) def write_cosmo_data(self, dacycle, dicts, date):
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.57_lat=47.38 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_zhbr) co2_all = []
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.52_lat=47.37 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_zsch) time_all = []
cdo.expr("'CO2=(CO2_BG"+ens+"-CO2_GPP"+ens+"+CO2_RA"+ens+"+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.53_lat=47.38 -selname,QV,CO2_BG"+ens+",CO2_GPP"+ens+",CO2_RA"+ens+",CO2_A "+co2_in_fn, output = co2_out_zue) date = date.strftime("%Y%m%d")
for station in dicts:
files2cat_albs.append(co2_out_albs) e = station['ensnum']