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assim_synth_obs.py
exp_config.py 5.94 KiB
from dartwrf import utils
exp = utils.Experiment()
exp.expname = "template_experiment"
exp.model_dx = 2000
exp.n_ens = 40
exp.superob_km = False # False or int (spatial averaging of observations, unit: km)
exp.do_quality_control = False # bool
# Use existing observations (path to obs_seq.out) or generate observations (False)
exp.use_existing_obsseq = False
#exp.use_existing_obsseq = '/users/students/lehre/advDA_s2023/dartwrf_tutorial/very_cold_observation.out'
# path from where observations can be generated
exp.nature_wrfout_pattern = '/jetfs/home/lkugler/data/sim_archive/exp_v1.19_P3_wbub7_nat/*/1/wrfout_d01_%Y-%m-%d_%H:%M:%S'
# for initialization profiles
exp.input_profile = '/mnt/jetfs/home/lkugler/data/initial_profiles/wrf/ens/2022-03-31/raso.fc.<iens>.wrfprof'
exp.dart_nml = {'&assim_tools_nml':
dict(filter_kind='1',
sampling_error_correction='.false.',
# obs_impact_filename='/jetfs/home/lkugler/DART-WRF/templates/impactfactor_T.txt',
),
'&filter_nml':
dict(ens_size=exp.n_ens,
num_output_state_members=exp.n_ens,
num_output_obs_members=exp.n_ens,
inf_flavor=['0', '0'],
output_members='.true.',
output_mean='.true.',
output_sd='.true.',
stages_to_write='output',
),
'&quality_control_nml':
dict(outlier_threshold='-1',
),
'&location_nml':
dict(horiz_dist_only='.true.',
),
'&model_nml':
dict(wrf_state_variables =
[['U', 'QTY_U_WIND_COMPONENT', 'TYPE_U', 'UPDATE','999',],
['V', 'QTY_V_WIND_COMPONENT', 'TYPE_V', 'UPDATE','999',],
['W', 'QTY_VERTICAL_VELOCITY', 'TYPE_W', 'UPDATE','999',],
['PH', 'QTY_GEOPOTENTIAL_HEIGHT', 'TYPE_GZ', 'UPDATE','999',],
['THM', 'QTY_POTENTIAL_TEMPERATURE','TYPE_T', 'UPDATE','999',],
['MU', 'QTY_PRESSURE', 'TYPE_MU', 'UPDATE','999',],
['QVAPOR','QTY_VAPOR_MIXING_RATIO', 'TYPE_QV', 'UPDATE','999',],
['QICE', 'QTY_ICE_MIXING_RATIO', 'TYPE_QI', 'UPDATE','999',],
['QCLOUD','QTY_CLOUDWATER_MIXING_RATIO','TYPE_QC', 'UPDATE','999',],
['CLDFRA','QTY_CLOUD_FRACTION', 'TYPE_CFRAC','UPDATE','999',],
['PSFC', 'QTY_SURFACE_PRESSURE', 'TYPE_PSFC', 'UPDATE','999',],
['T2', 'QTY_2M_TEMPERATURE', 'TYPE_T', 'UPDATE','999',],
['TSK', 'QTY_SKIN_TEMPERATURE', 'TYPE_T', 'UPDATE','999',],
['REFL_10CM','QTY_RADAR_REFLECTIVITY','TYPE_REFL', 'UPDATE','999',]]),
}
# n_obs can be 22500: 2km, 5776: 4km, 121: 30km, 256:16x16 (20km); 961: 10km resoltn
# if radar: then n_obs is for each observation height level
vis = dict(var_name='VIS 0.6µm', unit='[1]',
kind='MSG_4_SEVIRI_BDRF', sat_channel=1,
n_obs=256, obs_locations='square_array_evenly_on_grid',
# n_obs=1, obs_locations=[(44.141, -0.99)],
error_generate=0.03, error_assimilate=0.03,
loc_horiz_km=10)
wv62 = dict(var_name='Brightness temperature WV 6.2µm', unit='[K]',
kind='MSG_4_SEVIRI_TB', sat_channel=5,
n_obs=961, obs_locations='square_array_evenly_on_grid',
error_generate=1., error_assimilate=2.,
loc_horiz_km=20)
wv73 = dict(var_name='Brightness temperature WV 7.3µm', unit='[K]',
kind='MSG_4_SEVIRI_TB', sat_channel=6,
n_obs=961, obs_locations='square_array_evenly_on_grid',
error_generate=1., error_assimilate=3.,
loc_horiz_km=20)
ir108 = dict(var_name='Brightness temperature IR 10.8µm', unit='[K]',
kind='MSG_4_SEVIRI_TB', sat_channel=9,
n_obs=1, obs_locations='square_array_evenly_on_grid',
error_generate=5., error_assimilate=10.,
loc_horiz_km=32)
radar = dict(var_name='Radar reflectivity', unit='[dBz]',
kind='RADAR_REFLECTIVITY',
n_obs=5776, obs_locations='square_array_evenly_on_grid',
error_generate=2.5, error_assimilate=2.5,
heights=range(2000, 14001, 2000),
loc_horiz_km=20, loc_vert_km=2.5)
t = dict(var_name='Temperature', unit='[K]',
kind='RADIOSONDE_TEMPERATURE',
#n_obs=22500, obs_locations='square_array_evenly_on_grid',
n_obs=1, obs_locations=[(45., 0.)],
error_generate=0.2, error_assimilate=0.2,
heights=[1000,], #range(1000, 17001, 2000),
loc_horiz_km=50, loc_vert_km=2.5)
q = dict(var_name='Specific humidity', unit='[kg/kg]',
kind='RADIOSONDE_SPECIFIC_HUMIDITY',
n_obs=961, obs_locations='square_array_evenly_on_grid',
error_generate=0., error_assimilate=5*1e-5,
heights=[1000], #range(1000, 17001, 2000),
loc_horiz_km=0.1, loc_vert_km=2.5)
t2m = dict(var_name='SYNOP Temperature', unit='[K]',
kind='SYNOP_TEMPERATURE',
n_obs=961, obs_locations='square_array_evenly_on_grid',
error_generate=0.1, error_assimilate=0.1,
loc_horiz_km=40, loc_vert_km=2.5)
psfc = dict(var_name='SYNOP Pressure', unit='[Pa]',
kind='SYNOP_SURFACE_PRESSURE',
n_obs=961, obs_locations='square_array_evenly_on_grid',
error_generate=50., error_assimilate=100.,
loc_horiz_km=32, loc_vert_km=5)
exp.observations = [vis]
exp.update_vars = ['U', 'V', 'W', 'THM', 'PH', 'MU', 'QVAPOR', 'QCLOUD', 'QICE', 'PSFC']