import os, sys, shutil
import warnings
import datetime as dt
import numpy as np
from scipy.interpolate import interp1d
from config.cfg import exp, cluster
from utils import symlink, copy, sed_inplace, append_file, mkdir, try_remove, print
import create_obsseq as osq
import wrfout_add_geo
earth_radius_km = 6370
# fit of Fig 7, Harnisch 2016
x_ci = [0, 5, 10.5, 13, 16]
y_oe = [1, 4.5, 10, 12, 13] # Kelvin
oe_73_linear = interp1d(x_ci, y_oe, assume_sorted=True)
def oe_73(ci):
if ci < 13:
return oe_73_linear(ci)
else:
return 16.
def cloudimpact_73(bt_mod, bt_obs):
"""
follows Harnisch 2016
"""
biascor_obs = 0
bt_lim = 255 # Kelvin for 7.3 micron WV channel
ci_obs = max(0, bt_lim-(bt_obs - biascor_obs))
ci_mod = max(0, bt_lim-bt_mod)
ci = (ci_obs+ci_mod)/2
return ci
def read_prior_obs(f_obs_prior):
"""
docstring
"""
obsseq = open(f_obs_prior, 'r').readlines()
OBSs = []
# read observations from obs_seq.final
for i, line in enumerate(obsseq):
if ' OBS ' in line:
observed = float(obsseq[i+1].strip())
truth = float(obsseq[i+2].strip())
prior_ensmean = float(obsseq[i+3].strip())
prior_enssd = float(obsseq[i+4].strip())
prior_ens = []
for j in range(5, 5+exp.n_ens):
prior_ens.append(float(obsseq[i+j].strip()))
OBSs.append(dict(observed=observed, truth=truth, prior_ens=np.array(prior_ens)))
return OBSs
def read_obsseqout(f):
obsseq = open(f, 'r').readlines()
true = []
obs = []
# read observations from obs_seq.out
for i, line in enumerate(obsseq):
if ' OBS ' in line:
observed = float(obsseq[i+1].strip())
truth = float(obsseq[i+2].strip())
true.append(truth)
obs.append(observed)
return true, obs
def set_DART_nml(sat_channel=False, cov_loc_radius_km=32, cov_loc_vert_km=False,
just_prior_values=False):
"""descr"""
cov_loc_radian = cov_loc_radius_km/earth_radius_km
if just_prior_values:
template = cluster.scriptsdir+'/../templates/input.eval.nml'
else:
template = cluster.scriptsdir+'/../templates/input.nml'
copy(template, cluster.dartrundir+'/input.nml')
# options are overwritten with settings
options = {'<n_ens>': str(int(exp.n_ens)),
'<cov_loc_radian>': str(cov_loc_radian)}
if cov_loc_vert_km:
cov_loc_vert_rad = cov_loc_vert_km*1000/cov_loc_radian
options['<horiz_dist_only>'] = '.false.'
options['<vert_norm_hgt>'] = str(cov_loc_vert_rad)
else:
options['<horiz_dist_only>'] = '.true.'
options['<vert_norm_hgt>'] = '50000.0' # dummy value
for key, value in options.items():
sed_inplace(cluster.dartrundir+'/input.nml', key, value)
# input.nml for RTTOV
if sat_channel > 0:
if sat_channel in [1, 2, 3, 12]: # VIS channels
rttov_nml = cluster.scriptsdir+'/../templates/obs_def_rttov.VIS.nml'
else: # IR channels
rttov_nml = cluster.scriptsdir+'/../templates/obs_def_rttov.IR.nml'
append_file(cluster.dartrundir+'/input.nml', rttov_nml)
else:
# append any rttov segment, needs to exist anyway
rttov_nml = cluster.scriptsdir+'/../templates/obs_def_rttov.IR.nml'
append_file(cluster.dartrundir+'/input.nml', rttov_nml)
def obs_operator_ensemble(istage):
# assumes that prior ensemble is already linked to advance_temp<i>/wrfout_d01
print('running obs operator on ensemble forecast')
os.chdir(cluster.dartrundir)
if sat_channel:
list_ensemble_truths = []
for iens in range(1, exp.n_ens+1):
print('observation operator for ens #'+str(iens))
# ens members are already linked to advance_temp<i>/wrfout_d01
copy(cluster.dartrundir+'/advance_temp'+str(iens)+'/wrfout_d01',
cluster.dartrundir+'/wrfout_d01')
# DART may need a wrfinput file as well, which serves as a template for dimension sizes
symlink(cluster.dartrundir+'/wrfout_d01', cluster.dartrundir+'/wrfinput_d01')
# add geodata, if istage>0, wrfout is DART output (has coords)
if istage == 0:
wrfout_add_geo.run(cluster.dartrundir+'/geo_em.d01.nc', cluster.dartrundir+'/wrfout_d01')
# run perfect_model obs (forward operator)
os.system('mpirun -np 12 ./perfect_model_obs > /dev/null')
# truth values in obs_seq.out are H(x) values
true, _ = read_obsseqout(cluster.dartrundir+'/obs_seq.out')
list_ensemble_truths.append(true)
n_obs = len(list_ensemble_truths[0])
np_array = np.full((exp.n_ens, n_obs), np.nan)
for i in range(exp.n_ens):
np_array[i, :] = list_ensemble_truths[i]
return np_array
else:
raise NotImplementedError()
def obs_operator_nature(time):
print('running obs operator on nature run')
prepare_nature_dart(time)
run_perfect_model_obs()
true, _ = read_obsseqout(cluster.dartrundir+'/obs_seq.out')
return true
def link_nature_to_dart_truth(time):
# get wrfout_d01 from nature run
shutil.copy(time.strftime(cluster.nature_wrfout),
cluster.dartrundir+'/wrfout_d01')
# DART may need a wrfinput file as well, which serves as a template for dimension sizes
symlink(cluster.dartrundir+'/wrfout_d01', cluster.dartrundir+'/wrfinput_d01')
def prepare_nature_dart(time):
link_nature_to_dart_truth(time)
wrfout_add_geo.run(cluster.dartrundir+'/geo_em.d01.nc', cluster.dartrundir+'/wrfout_d01')
def calc_obserr_WV73(Hx_nature, Hx_prior):
n_obs = len(Hx_nature)
OEs = np.ones(n_obs)
for iobs in range(n_obs):
bt_y = Hx_nature[iobs]
bt_x_ens = Hx_prior[:, iobs]
CIs = [cloudimpact_73(bt_x, bt_y) for bt_x in bt_x_ens]
mean_CI = np.mean(CIs)
oe_nature = oe_73(mean_CI)
print('oe_nature:', oe_nature, ', bt_y:', bt_y, ', mean_CI:', mean_CI)
OEs[iobs] = oe_nature
return OEs
def run_perfect_model_obs():
os.chdir(cluster.dartrundir)
try_remove(cluster.dartrundir+'/obs_seq.out')
if not os.path.exists(cluster.dartrundir+'/obs_seq.in'):
raise RuntimeError('obs_seq.in does not exist in '+cluster.dartrundir)
os.system('mpirun -np 12 ./perfect_model_obs > log.perfect_model_obs')
if not os.path.exists(cluster.dartrundir+'/obs_seq.out'):
raise RuntimeError('obs_seq.out does not exist in '+cluster.dartrundir,
'\n look for '+cluster.dartrundir+'log.perfect_model_obs')
def assimilate(nproc=96):
print('running filter')
os.chdir(cluster.dartrundir)
try_remove(cluster.dartrundir+'/obs_seq.final')
os.system('mpirun -genv I_MPI_PIN_PROCESSOR_LIST=0-'+str(int(nproc)-1)+' -np '+str(int(nproc))+' ./filter > log.filter')
def archive_diagnostics(archive_dir, time):
print('archive obs space diagnostics')
mkdir(archive_dir)
fout = archive_dir+time.strftime('/%Y-%m-%d_%H:%M_obs_seq.final')
copy(cluster.dartrundir+'/obs_seq.final', fout)
print(fout, 'saved.')
# try: # what are regression diagnostics?!
# print('archive regression diagnostics')
# copy(cluster.dartrundir+'/reg_diagnostics', archive_dir+'/reg_diagnostics')
# except Exception as e:
# warnings.warn(str(e))
def recycle_output():
print('move output to input')
for iens in range(1, exp.n_ens+1):
os.rename(cluster.dartrundir+'/filter_restart_d01.'+str(iens).zfill(4),
cluster.dartrundir+'/advance_temp'+str(iens)+'/wrfout_d01')
def archive_output(archive_stage):
print('archiving output')
mkdir(archive_stage)
copy(cluster.dartrundir+'/input.nml', archive_stage+'/input.nml')
# single members
# for iens in range(1, exp.n_ens+1):
# savedir = archive_stage+'/'+str(iens)
# mkdir(savedir)
# filter_in = cluster.dartrundir+'/preassim_member_'+str(iens).zfill(4)+'.nc'
# filter_out = cluster.dartrundir+'/filter_restart_d01.'+str(iens).zfill(4)
# copy mean and sd to archive
for f in ['output_mean.nc', 'output_sd.nc']:
copy(cluster.dartrundir+'/'+f, archive_stage+'/'+f)
if __name__ == "__main__":
"""Generate observations (obs_seq.out file)
as defined in config/cfg.py
for a certain timestamp (argument) of the nature run (defined in config/clusters.py)
Workflow:
for each assimilation stage (one obs_seq.in and e.g. one observation type):
1) prepare nature run for DART
optional: 2) calculate obs-error from parametrization
3) create obs_seq.in with obs-errors from 2)
4) generate actual observations (obs_seq.out) with obs_seq.in from 3)
- calculate obs-error from parametrization
1) create obs_seq.in with obs error=0
2) calculate y_nat = H(x_nature) and y_ens = H(x_ensemble)
3) calculate obs error as function of y_nat, y_ensmean
Assumptions:
- x_ensemble is already linked for DART to advance_temp<iens>/wrfout_d01
"""
time = dt.datetime.strptime(sys.argv[1], '%Y-%m-%d_%H:%M')
archive_time = cluster.archivedir()+time.strftime('/%Y-%m-%d_%H:%M/')
os.chdir(cluster.dartrundir)
os.system('rm -f obs_seq.in obs_seq.out obs_seq.final') # remove any existing observation files
n_stages = len(exp.observations)
for istage, obscfg in enumerate(exp.observations):
archive_stage = archive_time + '/assim_stage'+str(istage)
n_obs = obscfg['n_obs']
sat_channel = obscfg.get('sat_channel', False)
obscfg['folder_obs_coords'] = archive_stage+'/obs_coords.pkl'
set_DART_nml(sat_channel=sat_channel,
cov_loc_radius_km=obscfg['cov_loc_radius_km'],
cov_loc_vert_km=obscfg.get('cov_loc_vert_km', False))
use_error_parametrization = obscfg['err_std'] == False
if use_error_parametrization:
if sat_channel != 6:
raise NotImplementedError('sat channel '+str(sat_channel))
osq.create_obsseq_in(time, obscfg, zero_error=True) # zero error to get truth vals
Hx_nat = obs_operator_nature(time)
Hx_prior = obs_operator_ensemble(istage) # files are already linked to DART directory
obscfg['err_std'] = calc_obserr_WV73(Hx_nat, Hx_prior)
else:
obscfg['err_std'] = np.ones(n_obs) * obscfg['err_std'] # fixed stderr
osq.create_obsseq_in(time, obscfg) # now with correct errors
prepare_nature_dart(time)
run_perfect_model_obs()
assimilate()
dir_obsseq = cluster.archivedir()+'/obs_seq_final/assim_stage'+str(istage)
archive_diagnostics(dir_obsseq, time)
if istage < n_stages-1:
# recirculation: filter output -> input
recycle_output()
archive_output(archive_stage)
elif istage == n_stages-1:
# last assimilation, continue integration now
archive_output(archive_stage)
else:
RuntimeError('this should never occur?!')