def cm1_prep(mem, start, end, restart_name='cm1out_rst_000001.nc'):
""" Function to write new namelist.input and check to be
sure files are in order
"""
# If this is a restart, be sure we have the right file
if start != 0:
# Try loading the restart file with the correct name
if not os.path.exists(restart_name):
error_handler(
'unable to find file {:s} in member {:d} directory'.format(
restart_name, mem), 'cm1_prep')
# Make sure that the restart file is indeed at the correct time
rstnc = Dataset(restart_name, 'r')
time_sec = int(rstnc.variables['time'][0])
rstnc.close()
if time_sec != start:
error_handler(
'restart file {:s} for member {:d} does not match current cycle time: {:d} min.'
.format(restart_name, mem, start), 'cm1_prep')
# Find out how long the run should be
dtime = int(end - start)
# Write the namelist
if not os.path.exists('namelist.input'):
os.system('cp {:s}/namelist.input .'.format(dir_dom))
nmld = read_namelist('namelist.input')
if start != 0:
irst = 1
nmld['param2']['irst'] = 1
nmld['param2']['rstnum'] = 1
nmld['param1']['run_time'] = dtime
nmld['param1']['rstfrq'] = cycle_len
write_namelist(nmld, 'namelist.input')
#os.system('./write_cm1_namelist.py -r {:s} -l {:d}'.format(restart_name, dtime))
else:
nmld['param1']['run_time'] = dtime
nmld['param1']['rstfrq'] = cycle_len
write_namelist(nmld, 'namelist.input')
#os.system('./write_cm1_namelist.py -l {:d}'.format(dtime))
if not os.path.exists('namelist.input'):
error_handler('Unable to find namelist.input', 'wrf_prep')
def cm1_prep(mem,start,end,restart_name='cm1out_rst_000001.nc'):
""" Function to write new namelist.input and check to be
sure files are in order
"""
# If this is a restart, be sure we have the right file
if start != 0:
# Try loading the restart file with the correct name
if not os.path.exists(restart_name):
error_handler('unable to find file {:s} in member {:d} directory'.format(restart_name, mem), 'cm1_prep')
# Make sure that the restart file is indeed at the correct time
rstnc = Dataset(restart_name, 'r')
time_sec = int(rstnc.variables['time'][0])
rstnc.close()
if time_sec != start:
error_handler('restart file {:s} for member {:d} does not match current cycle time: {:d} min.'.format(restart_name, mem, start), 'cm1_prep')
# Find out how long the run should be
dtime = int(end-start)
# Write the namelist
if not os.path.exists('namelist.input'):
os.system('cp {:s}/namelist.input .'.format(dir_dom))
nmld = read_namelist('namelist.input')
if start != 0:
irst = 1
nmld['param2']['irst'] = 1
nmld['param2']['rstnum'] = 1
nmld['param1']['run_time'] = dtime
nmld['param1']['rstfrq'] = cycle_len
write_namelist(nmld, 'namelist.input')
#os.system('./write_cm1_namelist.py -r {:s} -l {:d}'.format(restart_name, dtime))
else:
nmld['param1']['run_time'] = dtime
nmld['param1']['rstfrq'] = cycle_len
write_namelist(nmld, 'namelist.input')
#os.system('./write_cm1_namelist.py -l {:d}'.format(dtime))
if not os.path.exists('namelist.input'):
error_handler('Unable to find namelist.input','wrf_prep')
parser = OptionParser(description=desc)
parser.add_option('-d','--datein',dest='datein',action='store',type='string',default=cycle_len,\
help='Date of assimilation cycle (YYYYMMDDHH)')
parser.add_option('-m','--mpi_procs',dest='mpi_procs',action='store',type='string',\
default=mpi_numprocs_filter, help='Number of processors to use for MPI run')
(opts, args) = parser.parse_args()
#datein = datetime.strptime(opts.datein,'%Y%m%d%H')
#prevdate = datein - timedelta(minutes=int(fct_len))
datein = int(opts.datein)
prevdate = datein - cycle_len
mpi_numprocs_filter = int(opts.mpi_procs)
# Get the namelist values
nmld = read_namelist('input.nml')
cm1nmld = read_namelist('namelist.input')
# Change these values to only run certain sections of the code
PRE_CLEAN = True
PRE_CHECK = True
RUN_FILTER = True
ARCHIVE_FILES = True
POST_CLEAN = True
def main():
# First step--write the namelist
# Need to know if we are trying to apply adaptive
# inflation from the previous time or not. If we are using it,
parser = OptionParser(description=desc)
parser.add_option('-d','--datein',dest='datein',action='store',type='string',default=cycle_len,\
help='Date of assimilation cycle (YYYYMMDDHH)')
parser.add_option('-m','--mpi_procs',dest='mpi_procs',action='store',type='string',\
default=mpi_numprocs_filter, help='Number of processors to use for MPI run')
(opts,args) = parser.parse_args()
#datein = datetime.strptime(opts.datein,'%Y%m%d%H')
#prevdate = datein - timedelta(minutes=int(fct_len))
datein = int(opts.datein)
prevdate = datein - cycle_len
mpi_numprocs_filter = int(opts.mpi_procs)
# Get the namelist values
nmld = read_namelist('input.nml')
cm1nmld = read_namelist('namelist.input')
# Change these values to only run certain sections of the code
PRE_CLEAN = True
PRE_CHECK = True
RUN_FILTER = True
ARCHIVE_FILES = True
POST_CLEAN = True
def main():
# First step--write the namelist
# Need to know if we are trying to apply adaptive
# inflation from the previous time or not. If we are using it,
obsdir, assim_target_date)):
print("Could not find obs sequence file:")
print("{:s}/{:%Y%m%d%H%M%S}_obs_seq.prior".format(obsdir,
assim_target_date))
print("Aborting.")
exit(1)
os.system(
'{copy} {obsdir}/{assim_target_date:%Y%m%d%H%M%S}_obs_seq.prior obs_seq.prior'
.format(**locals()))
# Make a subdirectory in the archive directory to hold output for this cycle
os.system('mkdir {:s}/archive/{:%Y%m%d%H%M%S}'.format(centraldir,
assim_target_date))
# Load the input.nml file and change it to reflect this time
dartnml = read_namelist('input.nml')
#> if not initializing, we need to ensure that the output inflation files from
#> previous cycle are used as input for next cycle
if not initialize:
dartnml['filter_nml']['inf_initial_from_restart'] = [True, True]
dartnml['filter_nml']['inf_sd_initial_from_restart'] = [True, True]
# Change the name of the template file
dartnml['model_nml']['cm1_template_file'] = restart_filename
# Set the right obs sequence name
dartnml['filter_nml']['obs_sequence_in_name'] = 'obs_seq.prior'
# Set the number of ensemble members
dartnml['filter_nml']['ens_size'] = ens_size
# set this to "true"
# this script is going to determine several things by reading the input.nml
# file which contains the &filter_nml namelist. make sure it exists first.
#if ( ! -e input.nml ) then
if not os.path.exists('input.nml'):
print("ERROR - input.nml does not exist in local directory.")
print("ERROR - input.nml needed to determine several settings for this script.")
exit(1)
# detect whether the model is supposed to run as an MPI job or not
# by reading the "async = " from the &filter_nml namelist in input.nml.
# some namelists contain the same string - be sure to get the filter_nml one
# by grepping for lines which follow it.
nmld = read_namelist('input.nml')
async_type = nmld['filter_nml']['async']
if (async_type == 0) or (async_type == 2):
parallel_model = False
elif (async_type == 4):
parallel_model = True
else:
print("cannot autodetect async value in the filter_nml namelist in input.nml file.")
print("hardcode the parallel_model shell variable and comment out these lines.")
exit(-1)
parallel_model = False
# Determine the number of ensemble members from input.nml,
# as well as the command for advancing the model.
num_ens = nmld['filter_nml']['ens_size']
def build_obs_structure(intime, rst_file, gridspace=16):
""" Function to specify what variables we want and how dense they should be """
#from make_namelist_dart import set_namelist_sectors, write_namelist
#from write_cm1_namelist import set_namelist_defaults
from namelist_utils import read_namelist, write_dart_namelist
# Generate the DART namelist structure so we
# can query (and modify) it
dartnml = read_namelist('input.nml')
cm1nml = read_namelist('namelist.input')
# Build the epoch from the cm1 namelist
param11 = cm1nml['param11']
startdate = datetime(param11['year'], param11['month'],\
param11['day'], param11['hour'],\
param11['minute'], param11['second'])
# Parse out the observations to assimilate
use_obs = dartnml['obs_kind_nml']['assimilate_these_obs_types']
# I'm just testing this for now
#use_obs = use_obs[0:2]
use_obs = ['LAND_SFC_PRESSURE']
print(use_obs)
# Put time as datetime
intime = startdate + timedelta(seconds=intime)
epoch = datetime(1601,1,1,0)
print("Indate:", intime)
# Figure out the grid structure
dx = cm1nml['param1']['dx']
dy = cm1nml['param1']['dy']
nx = cm1nml['param0']['nx']
ny = cm1nml['param0']['ny']
# Now figure out how many obs we'll need
gridspace *= 1000
obx = range(int(dx/2.),int(nx*dx),gridspace)
oby = range(int(dy/2.),int(ny*dx),gridspace)
#obx = [16000]
#oby = [16000]
total_obs = len(obx)*len(oby)*len(use_obs)
print("Total number of obs:", total_obs)
# Write a new dart namelist for the current time
delt = intime - epoch
#dartnml['perfect_model_obs_nml']['first_obs_days'] = str(delt.days)
#dartnml['perfect_model_obs_nml']['first_obs_seconds'] = str(delt.seconds)
#dartnml['perfect_model_obs_nml']['last_obs_days'] = str(delt.days)
#dartnml['perfect_model_obs_nml']['last_obs_seconds'] = str(delt.seconds)
#dartnml['perfect_model_obs_nml']['init_time_days'] = str(delt.days)
#dartnml['perfect_model_obs_nml']['init_time_seconds'] = str(delt.seconds)
dartnml['perfect_model_obs_nml']['obs_seq_in_file_name'] = 'obs_seq.in'
dartnml['perfect_model_obs_nml']['restart_in_file_name'] = rst_file
# Make sure we have the right io pattern
dartnml['io_filenames_nml']['restart_in_stub'] = 'notinuse'
dartnml['io_filenames_nml']['overwrite_input'] = False
dartnml['io_filenames_nml']['rpointer'] = True
dartnml['io_filenames_nml']['rpointer_file'] = 'input_filelist.txt'
# Write the pointer file
with open('input_filelist.txt', 'w') as pointerfile:
pointerfile.write(rst_file)
os.system('mv input_filelist.txt {:s}/'.format(dir_obs))
# Write the modified namelist
write_dart_namelist(dartnml)
# Set up the input file
with open('obs_seq_input.txt','w') as infile:
infile.write(str(total_obs)+'\n') # Total num obs
infile.write('0\n') # 0 copies
infile.write('0\n') # 0 QC values
for obtype in use_obs:
for x in obx:
for y in oby:
infile.write('0\n')
infile.write(obtype+'\n') # Identify ob type
infile.write('0\n') # Specify location
infile.write(str(x)+'\n') # X coordinate
infile.write(str(y)+'\n') # Y coordinate
infile.write('0\n') # Z coordinate
infile.write('{:d} {:d} {:d} {:d} {:d} {:d}\n'.format(\
intime.year, intime.month,\
intime.day, intime.hour,\
intime.minute, intime.second))
infile.write(str(error_var[obtype])+'\n') # Error variance
infile.write('obs_seq.in\n')
# Move these files to the obs dir
if os.path.exists(os.path.join(dir_obs, 'input.nml')):
os.system('rm -f {:s}'.format(os.path.join(dir_obs, 'input.nml')))
if os.path.exists(os.path.join(dir_obs, 'obs_seq_input.txt')):
os.system('rm -f {:s}'.format(os.path.join(dir_obs, 'obs_seq_input.txt')))
os.system('mv obs_seq_input.txt {:s}'.format(dir_obs))
os.system('cp input.nml {:s}'.format(dir_obs))
return None
def build_obs_structure(intime, rst_file, gridspace=4):
""" Function to specify what variables we want and how dense they should be """
#from make_namelist_dart import set_namelist_sectors, write_namelist
#from write_cm1_namelist import set_namelist_defaults
from namelist_utils import read_namelist, write_dart_namelist
# Generate the DART namelist structure so we
# can query (and modify) it
dartnml = read_namelist('input.nml')
cm1nml = read_namelist('namelist.input')
# Build the epoch from the cm1 namelist
param11 = cm1nml['param11']
startdate = datetime(param11['year'], param11['month'],\
param11['day'], param11['hour'],\
param11['minute'], param11['second'])
# Parse out the observations to assimilate
use_obs = dartnml['obs_kind_nml']['assimilate_these_obs_types']
# I'm just testing this for now
#use_obs = use_obs[0:2]
#use_obs = ['LAND_SFC_U_WIND_COMPONENT', 'LAND_SFC_V_WIND_COMPONENT', 'LAND_SFC_TEMPERATURE',
# 'LAND_SFC_SPECIFIC_HUMIDITY']
use_obs = [
'TEMPERATURE_2M', 'SURFACE_PRESSURE', 'SPECIFIC_HUMIDITY_2M',
'V_WIND_10M', 'U_WIND_10M'
]
print(use_obs)
# Put time as datetime
intime = startdate + timedelta(seconds=intime)
epoch = datetime(1601, 1, 1, 0)
print("Indate:", intime)
# Figure out the grid structure
dx = cm1nml['param1']['dx']
dy = cm1nml['param1']['dy']
nx = cm1nml['param0']['nx']
ny = cm1nml['param0']['ny']
# Now figure out how many obs we'll need
gridspace *= 1000
obx = range(int(dx / 2.), int(nx * dx), gridspace)
oby = range(int(dy / 2.), int(ny * dx), gridspace)
#obx = [16000]
#oby = [16000]
total_obs = len(obx) * len(oby) * len(use_obs)
print("Total number of obs:", total_obs)
# Write a new dart namelist for the current time
delt = intime - epoch
#dartnml['perfect_model_obs_nml']['first_obs_days'] = str(delt.days)
#dartnml['perfect_model_obs_nml']['first_obs_seconds'] = str(delt.seconds)
#dartnml['perfect_model_obs_nml']['last_obs_days'] = str(delt.days)
#dartnml['perfect_model_obs_nml']['last_obs_seconds'] = str(delt.seconds)
#dartnml['perfect_model_obs_nml']['init_time_days'] = str(delt.days)
#dartnml['perfect_model_obs_nml']['init_time_seconds'] = str(delt.seconds)
dartnml['perfect_model_obs_nml']['obs_seq_in_file_name'] = 'obs_seq.in'
dartnml['perfect_model_obs_nml']['restart_in_file_name'] = rst_file
# Make sure we have the right io pattern
dartnml['io_filenames_nml']['restart_in_stub'] = 'notinuse'
dartnml['io_filenames_nml']['overwrite_input'] = False
dartnml['io_filenames_nml']['rpointer'] = True
dartnml['io_filenames_nml']['rpointer_file'] = 'input_filelist.txt'
# Write the pointer file
with open('input_filelist.txt', 'w') as pointerfile:
pointerfile.write(rst_file)
os.system('mv input_filelist.txt {:s}/'.format(dir_obs))
# Write the modified namelist
write_dart_namelist(dartnml)
# Set up the input file
with open('obs_seq_input.txt', 'w') as infile:
infile.write(str(total_obs) + '\n') # Total num obs
infile.write('0\n') # 0 copies
infile.write('0\n') # 0 QC values
for obtype in use_obs:
for x in obx:
for y in oby:
infile.write('0\n')
infile.write(obtype + '\n') # Identify ob type
infile.write('0\n') # Specify location
infile.write(str(x) + '\n') # X coordinate
infile.write(str(y) + '\n') # Y coordinate
infile.write(str(heights[obtype]) + '\n') # Z coordinate
infile.write('{:d} {:d} {:d} {:d} {:d} {:d}\n'.format(\
intime.year, intime.month,\
intime.day, intime.hour,\
intime.minute, intime.second))
infile.write(str(error_var[obtype]) +
'\n') # Error variance
infile.write('obs_seq.in\n')
# Move these files to the obs dir
if os.path.exists(os.path.join(dir_obs, 'input.nml')):
os.system('rm -f {:s}'.format(os.path.join(dir_obs, 'input.nml')))
if os.path.exists(os.path.join(dir_obs, 'obs_seq_input.txt')):
os.system('rm -f {:s}'.format(
os.path.join(dir_obs, 'obs_seq_input.txt')))
os.system('mv obs_seq_input.txt {:s}'.format(dir_obs))
os.system('cp input.nml {:s}'.format(dir_obs))
return None
os.system(
'{copy} {truthdir}/input_sounding {dirname} || exit 2'.
format(**locals()))
os.system(
'{copy} {truthdir}/LANDUSE.TBL {dirname} || exit 2'.
format(**locals()))
os.system(
'{copy} {truthdir}/namelist.input {dirname}/namelist.input.template || exit 2'
.format(**locals()))
# Enforce certain assumptions.
# 1) we are always starting from a netCDF file ... irst == 1
# 2) restart always be named cm1_out_rst_000001.nc ... rstnum == 1
# 3) the run_time value will be a dummy string
# Python version --use namelist utilities
nmld = read_namelist(os.path.join(dirname, 'namelist.input.template'))
nmld['param2']['irst'] = 1
nmld['param2']['rstnum'] = 1
nmld['param1']['run_time'] = cm1_forecast_length
nmld['param1']['rstfrq'] = cm1_forecast_length
write_namelist(nmld, os.path.join(dirname, 'namelist.input.template'))
os.system('{remove} input_filelist.txt'.format(**locals()))
# Python -- write to input_filelist.txt in with
# statement. This will automatically close
# the file when we are out of the block
with open('input_filelist.txt', 'w') as outfile:
outfile.write(os.path.join(dirname, filename) + '\n')
# When perfect_model_obs starts, it needs to read the grid information.
#!/usr/bin/env python
import os, sys, getopt
# Get parameters from ens_dart_param and the default cm1 namelist
from ens_dart_param import *
from namelist_utils import read_namelist, write_namelist
cm1nml = read_namelist(os.path.join(dir_dom, 'namelist.input'))
"""
This is a quickly-reformatted version of the make_ensemble script
from version 1 of this code that makes it compatible with the new
namelist version. This script will make an ensemble (in serial) with
a number of members as specified in the namelist variable "Ne" or manually
specified from the command line. The script will copy in all of the necessary
files. No perturbation of initial conditions is enabled at this time.
LEM 09/2015
"""
(opts, args) = getopt.getopt(sys.argv[1:], 'n:')
for o, a in opts:
if o == '-n':
Ne = int(a)
def main():
DOMDIR = dir_dom
os.chdir(DOMDIR)
"""
if os.path.isdir(DOMDIR+'/mems'):
remove = raw_input('Remove contents of current ensemble directory (0 or 1)?')
if remove:
'{copy} {ensembledir}/input_sounding . || exit 2'.format(
**locals()))
os.system(
'{copy} {ensembledir}/LANDUSE.TBL . || exit 2'.format(
**locals()))
os.system(
'{copy} {ensembledir}/namelist.input . || exit 2'.format(
**locals()))
# Enforce certain assumptions.
# 1) we are always starting from a netCDF file ... irst == 1
# 2) restart always be named cm1_out_rst_000001.nc ... rstnum == 1
# 3) the run_time value will be a dummy string
# Python version --use namelist utilities
# read the namelist
nmld = read_namelist('namelist.input')
# Check for environment variable that
# contains the current forecast length
# Otherwise leave as is
if os.environ.get('CM1_FORECAST_LENGTH') not in [None, '']:
cm1_forecast_length = float(os.envion.get('CM1_FORECAST_LENGTH'))
nmld['param1']['run_time'] = cm1_forecast_length
nmld['param1']['rstfrq'] = cm1_forecast_length
# Set the other namelist requirements and write
nmld['param2']['irst'] = 1
nmld['param2']['rstnum'] = 1
write_namelist(nmld, 'namelist.input')
# Python -- write to input_filelist.txt in with
#!/usr/bin/env python
import os, sys, getopt
# Get parameters from ens_dart_param and the default cm1 namelist
from ens_dart_param import *
from namelist_utils import read_namelist, write_namelist
cm1nml = read_namelist(os.path.join(dir_dom, 'namelist.input'))
"""
This is a quickly-reformatted version of the make_ensemble script
from version 1 of this code that makes it compatible with the new
namelist version. This script will make an ensemble (in serial) with
a number of members as specified in the namelist variable "Ne" or manually
specified from the command line. The script will copy in all of the necessary
files. No perturbation of initial conditions is enabled at this time.
LEM 09/2015
"""
(opts,args) = getopt.getopt(sys.argv[1:],'n:')
for o,a in opts:
if o == '-n':
Ne = int(a)
def main():
DOMDIR = dir_dom
os.chdir(DOMDIR)
"""