parser.add_argument('output_file', metavar = 'output_file', type = str, nargs = 1)
args = parser.parse_args()
i_start = args.start_index[0]
i_end = args.end_index[0]
scene = args.scene[0]
output_file = args.output_file[0]
print(i_start, i_end, scene, output_file)
n = i_end - i_start
#
# Setup the simulation.
#
hydrometeors = gem_hydrometeors
kwargs = {"ice_psd" : hydrometeors[0].psd,
"snow_psd" : hydrometeors[1].psd,
"hail_psd" : hydrometeors[2].psd,
"graupel_psd" : hydrometeors[3].psd,
"liquid_psd" : hydrometeors[4].psd}
data_provider = ModelDataProvider(99, scene = scene, **kwargs)
sensors = [lcpr, mwi, ici]
simulation = CloudSimulation(hydrometeors[1:2], sensors, data_provider)
simulation.setup(verbosity = 0)
dimensions = [("profile", n, i_start)]
simulation.simulation.initialize_output_file(output_file, dimensions, "w")
simulation.simulation.run_ranges(range(i_start, i_end))
from mcrf.liras.single_species import ice, rain
from mcrf.liras import snow, h2o_a_priori, cloud_water_a_priori
ice_shape = os.path.join(liras_path, "data", "scattering", ice_shape)
ice.scattering_data = ice_shape
hydrometeors = [ice, rain]
#
# Create the data provider.
#
if not snow_shape == "None":
kwargs = {"ice_psd": ice.psd, "snow_psd": snow.psd, "liquid_psd": rain.psd}
else:
kwargs = {"ice_psd": ice.psd, "liquid_psd": rain.psd}
data_provider = ModelDataProvider(99, scene=scene.upper(), **kwargs)
#
# Define hydrometeors and sensors.
#
sensors = [lcpr]
#
# Add a priori providers.
#
observation_errors = mcrf.liras.ObservationError(sensors)
data_provider.add(ice.a_priori[0])
data_provider.add(ice.a_priori[1])
data_provider.add(snow.a_priori[0])
data_provider.add(snow.a_priori[1])
def get_reference_data(scene="a", i_start=3000, i_end=3800):
"""
Get reference data from cloud scene.
Arguments:
scene: From which scene to take the values ("a", "b")
i_start: Start index of the scene
i_end: End index of the scene
"""
n = i_end - i_start
ice_psd = D14NDmIce()
snow_psd = D14NDmSnow()
liquid_psd = D14NDmLiquid()
data_provider = ModelDataProvider(99,
scene=scene.upper(),
ice_psd=ice_psd,
snow_psd=snow_psd,
hail_psd=snow_psd,
graupel_psd=snow_psd,
liquid_psd=liquid_psd)
z = data_provider.get_altitude(i_start)
iwc, swc, gwc, hwc, lwc, rwc = np.zeros((6, n, z.size))
iwc_nd, swc_nd, gwc_nd, hwc_nd, lwc_nd, rwc_nd = np.zeros((6, n, z.size))
iwc_dm, swc_dm, gwc_dm, hwc_dm, lwc_dm, rwc_dm = np.zeros((6, n, z.size))
iwc_n0, swc_n0, gwc_n0, hwc_n0, lwc_n0, rwc_n0 = np.zeros((6, n, z.size))
lats, lons = np.zeros((2, n))
h2o = np.zeros((n, z.size))
temperature = np.zeros((n, z.size))
for i in range(i_start, i_end):
j = i - i_start
iwc[j, :] = data_provider.get_gem_ice_mass_density(i)
swc[j, :] = data_provider.get_gem_snow_mass_density(i)
hwc[j, :] = data_provider.get_gem_hail_mass_density(i)
gwc[j, :] = data_provider.get_gem_graupel_mass_density(i)
rwc[j, :] = data_provider.get_gem_rain_mass_density(i)
lwc[j, :] = data_provider.get_gem_liquid_mass_density(i)
iwc_nd[j, :] = data_provider.get_gem_ice_number_density(i)
swc_nd[j, :] = data_provider.get_gem_snow_number_density(i)
hwc_nd[j, :] = data_provider.get_gem_hail_number_density(i)
gwc_nd[j, :] = data_provider.get_gem_graupel_number_density(i)
rwc_nd[j, :] = data_provider.get_gem_rain_number_density(i)
lwc_nd[j, :] = data_provider.get_gem_liquid_number_density(i)
iwc_dm[j, :] = data_provider.get_ice_dm(i)
swc_dm[j, :] = data_provider.get_snow_dm(i)
hwc_dm[j, :] = data_provider.get_hail_dm(i)
gwc_dm[j, :] = data_provider.get_graupel_dm(i)
rwc_dm[j, :] = data_provider.get_rain_dm(i)
lwc_dm[j, :] = data_provider.get_cloud_water_dm(i)
iwc_n0[j, :] = data_provider.get_ice_n0(i)
swc_n0[j, :] = data_provider.get_snow_n0(i)
hwc_n0[j, :] = data_provider.get_hail_n0(i)
gwc_n0[j, :] = data_provider.get_graupel_n0(i)
rwc_n0[j, :] = data_provider.get_rain_n0(i)
lwc_n0[j, :] = data_provider.get_cloud_water_n0(i)
h2o[j, :] = data_provider.get_relative_humidity(i)
lats[j] = data_provider.get_latitude(i)
lons[j] = data_provider.get_longitude(i)
temperature[j, :] = data_provider.get_temperature(i)
return {
"iwc": iwc,
"iwc_nd": iwc_nd,
"iwc_dm": iwc_dm,
"iwc_n0": iwc_n0,
"swc": swc,
"swc_nd": swc_nd,
"swc_dm": swc_dm,
"swc_n0": swc_n0,
"hwc": hwc,
"hwc_nd": hwc_nd,
"hwc_dm": hwc_dm,
"hwc_n0": hwc_n0,
"gwc": gwc,
"gwc_nd": gwc_nd,
"gwc_dm": gwc_dm,
"gwc_n0": gwc_n0,
"rwc": rwc,
"rwc_nd": rwc_nd,
"rwc_dm": rwc_dm,
"rwc_n0": rwc_n0,
"lwc": lwc,
"lwc_nd": lwc_nd,
"lwc_dm": lwc_dm,
"lwc_n0": lwc_n0,
"h2o": h2o,
"lat": lats,
"lon": lons,
"z": z,
"temperature": temperature
}
offsets = {"a": 3000, "b": 2800}
scene = "b"
offset = offsets[scene]
observations = NetCDFDataProvider(filename)
observations.add_offset("profile", -offset)
#
# Create the data provider.
#
ip = offset + 253 * 3 + 32
data_provider = ModelDataProvider(99,
ice_psd=ice.psd,
liquid_psd=rain.psd,
scene=scene.upper())
#
# Define hydrometeors and sensors.
#
hydrometeors = [ice, rain]
sensors = [mwi, ici]
#
# Add a priori providers.
#
observation_error = mcrf.liras.ObservationError(sensors,
forward_model_error=False,
offsets = {"a": 3000, "b": 2800}
scene = filename[-4]
offset = offsets[scene]
observations = NetCDFDataProvider(filename)
observations.add_offset("profile", -offset)
#
# Create the data provider.
#
ip = offset + 300
data_provider = ModelDataProvider(99,
ice_psd=ice.psd,
snow_psd=snow.psd,
liquid_psd=liquid.psd,
scene=scene.upper())
#
# Define hydrometeors and sensors.
#
hydrometeors = [ice, rain]
sensors = [lcpr, mwi, ici]
#
# Add a priori providers.
#
data_provider.add(ice.a_priori[0])
filename = os.path.join(mcrf.liras.liras_path, "data",
"forward_simulations_a_noise.nc")
offsets = {"a": 3000, "b": 2200}
scene = filename.split("_")[-2]
offset = offsets[scene]
observations = NetCDFDataProvider(filename)
observations.add_offset("profile", -offset)
shape = "LargePlateAggregate"
#
# Create the data provider.
#
ip = offset + 655
data_provider = ModelDataProvider(99,
ice_psd=ice.psd,
snow_psd=snow.psd,
scene=scene.upper())
#
# Define hydrometeors and sensors.
#
#hydrometeors = [snow, ice, rain]
hydrometeors = [ice, rain]
sensors = [lcpr, mwi, ici]
#
# Add a priori providers.
#
observation_error = mcrf.liras.ObservationError(sensors,
forward_model_error=False,
