mask=ice_mask,
mask_value=4)
ice_n0_a_priori = MaskedRegularGrid(ice_n0_a_priori,
20,
ice_mask,
"altitude",
provide_retrieval_grid=False)
#
# Hydrometeor definition
#
ice = Hydrometeor("ice", D14NDmIce(), [ice_n0_a_priori, ice_dm_a_priori],
ice_shape, ice_shape_meta)
ice.transformations = [
Composition(Log10(), PiecewiseLinear(ice_n0_a_priori)),
Identity()
]
# Lower limits for N_0^* and m in transformed space.
ice.limits_low = [2, 1e-8]
###############################################################################
# Snow particles
###############################################################################
snow_shape = os.path.join(scattering_data, "EvansSnowAggregate.xml")
snow_shape_meta = os.path.join(scattering_data, "EvansSnowAggregate.meta.xml")
snow_mask = And(AltitudeMask(0.0, 19e3), TemperatureMask(0.0, 276.0))
#
# D_m
ice_covariance,
mask=ice_mask,
mask_value=1e-6)
ice_dm_a_priori = ReducedVerticalGrid(ice_dm_a_priori,
z_grid,
"altitude",
provide_retrieval_grid=False)
#
# Hydrometeor definition
#
ice = Hydrometeor("ice", D14NDmIce(), [ice_n0_a_priori, ice_dm_a_priori],
ice_shape, ice_shape_meta)
ice.transformations = [
Composition(Log10(), PiecewiseLinear(ice_n0_a_priori)),
Composition(Identity(), PiecewiseLinear(ice_dm_a_priori))
]
ice.limits_low = [4, 1e-8]
ice.radar_only = False
################################################################################
# Snow particles
################################################################################
# Default shape
snow_shape = os.path.join(scattering_data, "EvansSnowAggregate.xml")
snow_shape_meta = os.path.join(scattering_data, "EvansSnowAggregate.meta.xml")
snow_mask = And(TropopauseMask(), TemperatureMask(0.0, 280.0))
snow_covariance = Diagonal(4 * np.ones(md_z_grid.size))
# ice_mask,
# "altitude",
# provide_retrieval_grid=False)
#ice_dm_a_priori = ReducedVerticalGrid(ice_dm_a_priori,
# z_grid,
# "altitude",
# provide_retrieval_grid=False)
#
# Hydrometeor definition.
#
ice = Hydrometeor("ice", D14NDmIce(), [ice_n0_a_priori, ice_dm_a_priori],
ice_shape, ice_shape_meta)
ice.transformations = [
Composition(Log10(), PiecewiseLinear(ice_n0_a_priori)),
Identity() #Composition(Identity(), PiecewiseLinear(ice_dm_a_priori))
]
ice.limits_low = [4, 1e-10]
ice.radar_only = False
################################################################################
# Rain particles
################################################################################
rain_shape = os.path.join(scattering_data, "LiquidSphere.xml")
rain_shape_meta = os.path.join(scattering_data, "LiquidSphere.meta.xml")
#
# water content
#
ice_n0_a_priori = ReferenceAPriori("ice_n0",
ice_covariance,
mask=ice_mask,
mask_value=2,
a_priori=ice_n0_a_priori,
transformation=Log10())
ice_n0_a_priori = MaskedRegularGrid(ice_n0_a_priori,
10,
ice_mask,
"altitude",
provide_retrieval_grid=False)
ice = Hydrometeor("ice", D14NDmIce(), [ice_n0_a_priori, ice_dm_a_priori],
ice_shape, ice_shape_meta)
ice.transformations = [
Composition(Log10(), PiecewiseLinear(ice_n0_a_priori)),
Identity()
]
ice.limits_low = [4, 1e-8]
ice.psd.cutoff_low = [1e2, 1e-8]
################################################################################
# Snow particles
################################################################################
snow_shape = os.path.join(scattering_data, "8-ColumnAggregate.xml")
snow_shape_meta = os.path.join(scattering_data, "8-ColumnAggregate.meta.xml")
snow_mask = And(AltitudeMask(0.0, 19e3), TemperatureMask(0.0, 276.0))
snow_covariance = Diagonal(100e-6**2, mask=snow_mask, mask_value=1e-12)
snow_covariance = SpatialCorrelation(snow_covariance, 2e3, mask=snow_mask)
def _setup_retrieval(self):
"""
Setup the artssat simulation used to perform the retrieval.
"""
for q in self.hydrometeors:
limit_low_1, limit_low_2 = q.limits_low
if hasattr(q, "limits_high"):
limit_high_1, limit_high_2 = q.limits_high
else:
limit_high_1, limit_high_2 = np.inf, np.inf
md = q.moments[0]
self.simulation.retrieval.add(md)
md.transformation = q.transformations[0]
md.retrieval.limit_low = q.limits_low[0]
md.retrieval.limit_high = limit_high_1
n0 = q.moments[1]
self.simulation.retrieval.add(n0)
n0.transformation = q.transformations[1]
n0.retrieval.limit_low = q.limits_low[1]
n0.retrieval.limit_high = limit_high_2
h2o = self.simulation.atmosphere.absorbers[-1]
h2o_a = [p for p in self.data_provider.subproviders \
if getattr(p, "name", "") == "H2O"]
if len(h2o_a) > 0:
h2o_a = h2o_a[0]
self.simulation.retrieval.add(h2o)
atanh = Atanh(0.0, 1.1)
if h2o_a.transformation is not None:
h2o.transformation = h2o_a.transformation
h2o.retrieval.unit = RelativeHumidity()
if hasattr(h2o_a, "limit_low"):
h2o.retrieval.limit_low = h2o_a.limit_low
if hasattr(h2o_a, "limit_high"):
h2o.retrieval.limit_high = h2o_a.limit_high
self.h2o = h2o
else:
self.h2o = None
if self.include_cloud_water:
cw_a = [p for p in self.data_provider.subproviders \
if getattr(p, "name", "") == "cloud_water"][0]
cw = self.simulation.atmosphere.absorbers[-2]
self.simulation.retrieval.add(cw)
pl = PiecewiseLinear(cw_a)
cw.transformation = Composition(Log10(), pl)
cw.retrieval.limit_high = -3
self.cw = cw
else:
self.cw = None
t_a = [p for p in self.data_provider.subproviders \
if getattr(p, "name", "") == "temperature"]
if len(t_a) > 0:
t = self.simulation.atmosphere.temperature
self.temperature = t
self.simulation.retrieval.add(self.temperature)
else:
self.temperature = None