def _do_test_isothermal_universe(pc, thickness_1):
T = 265
substrate = make_soil('soil_wegmuller',
permittivity_model=complex(10, 1),
roughness_rms=0.001,
temperature=T)
snowpack = make_snowpack([0.3, thickness_1],
"exponential",
density=[200, 300],
temperature=T,
corr_length=pc,
substrate=substrate)
atmosphere = SimpleIsotropicAtmosphere(tbdown=T, tbup=0, trans=1)
# create the sensor
theta = range(10, 80, 5)
radiometer = sensor_list.passive(37e9, theta)
# create the EM Model
m = make_model("iba", "dort")
# run the model
sresult = m.run(radiometer, snowpack, atmosphere)
np.testing.assert_allclose(sresult.TbV(), T, atol=0.01)
np.testing.assert_allclose(sresult.TbH(), T, atol=0.01)
def test_snowpack_with_coherent_layer():
# this test is only here to avoid regression, it is not scientifically validated
density = [300, 917, 400, 500]
thickness = [0.10, 0.01, 0.20, 1000]
temperature = 270
corr_length = [200e-6, 0, 200e-6, 200e-6]
theta = 60
radiometer = sensor_list.passive(5e9, theta)
sp = make_snowpack(thickness, "exponential",
density=density, temperature=temperature,
corr_length=corr_length)
# create the EM Model - Equivalent DMRTML
m = make_model("iba", "dort", rtsolver_options={'n_max_stream': 64, 'process_coherent_layers': True})
res = m.run(radiometer, sp)
print(res.TbV(), res.TbH())
assert abs(res.TbV() - 261.1994214138529) < 1e-4
assert abs(res.TbH() - 201.1848483718344) < 1e-4
def test_oneconfig_for_multiyear_sea_ice():
# prepare inputs
l, n_max_stream, thickness, temperature, salinity = setup_seaice()
p_ex = np.array([1000e-6] * l) # correlation length
porosity = 0.08 # ice porosity, in fraction
# create an ice column with assumption of spherical brine inclusions (inclusion_shape="spheres"):
ice_column = make_ice_column(ice_type="multiyear",
thickness=thickness,
temperature=temperature,
microstructure_model="exponential",
brine_inclusion_shape="spheres",
salinity=salinity,
porosity=porosity,
corr_length=p_ex,
add_water_substrate="ocean")
# create the sensor
sensor = sensor_list.passive(1.4e9, 40.)
n_max_stream = 128
m = make_model("iba",
"dort",
rtsolver_options={"n_max_stream": n_max_stream})
# run the model
res = m.run(sensor, ice_column)
print(res.TbV(), res.TbH())
# absorption with effective permittivity
assert abs(res.TbV() - 257.5689162188296) < 1e-4
assert abs(res.TbH() - 232.03924683304172) < 1e-4
def test_oneconfig_for_firstyear_sea_ice():
# prepare inputs
l, n_max_stream, thickness, temperature, salinity = setup_seaice()
p_ex = np.array([500e-6] * l) # correlation length
# create an ice column with assumption of spherical brine inclusions (brine_inclusion_shape="spheres"):
ice_column = make_ice_column(
ice_type="firstyear",
thickness=thickness,
temperature=temperature,
microstructure_model="exponential",
brine_inclusion_shape=
"spheres", #inclusion_shape can be "spheres" or "random_needles", or "mix_spheres_needles"
salinity=
salinity, #either 'salinity' or 'brine_volume_fraction' should be given for sea ice; if salinity is given, brine volume fraction is calculated in the model; if none is given, ice is treated as fresh water ice
corr_length=p_ex,
add_water_substrate="ocean")
# create the sensor
sensor = sensor_list.passive(1.4e9, 40.)
n_max_stream = 128
m = make_model("iba",
"dort",
rtsolver_options={"n_max_stream": n_max_stream})
# run the model
res = m.run(sensor, ice_column)
print(res.TbV(), res.TbH())
#absorption with effective permittivity
assert abs(res.TbV() - 256.01165061598317) < 1e-4
assert abs(res.TbH() - 228.47447378338745) < 1e-4
def _do_test_kirchoff_law(pc, thickness_1):
T = 265
substrate = make_soil('soil_wegmuller',
permittivity_model=complex(10, 1),
roughness_rms=0.001,
temperature=T)
snowpack = make_snowpack([0.3, thickness_1],
"exponential",
density=[200, 300],
temperature=T,
corr_length=pc,
substrate=substrate)
atmosphere1K = SimpleIsotropicAtmosphere(tbdown=1, tbup=0, trans=1)
# create the sensor
theta = range(10, 80, 5)
radiometer = sensor_list.passive(37e9, theta)
# create the EM Model
m = make_model("iba", "dort")
# run the model
sresult_0 = m.run(radiometer, snowpack)
sresult_1 = m.run(radiometer, snowpack, atmosphere1K)
# V-pol
emissivity_V = (sresult_0.TbV() + sresult_1.TbV()) / 2 / T
reflectivity_V = (sresult_1.TbV() - sresult_0.TbV())
print(emissivity_V, 1 - reflectivity_V)
np.testing.assert_allclose(emissivity_V, 1 - reflectivity_V, atol=0.002)
# H-pol
emissivity_H = (sresult_0.TbH() + sresult_1.TbH()) / 2 / T
reflectivity_H = (sresult_1.TbH() - sresult_0.TbH())
print(emissivity_H, 1 - reflectivity_H)
np.testing.assert_allclose(emissivity_H, 1 - reflectivity_H, atol=0.002)
def test_snowpack_with_coherent_layer():
# this test is only here to avoid regression, it is not scientifically validated
density = [300, 916.7, 400, 500]
thickness = [0.10, 0.01, 0.20, 1000]
temperature = 270
corr_length = [200e-6, 0, 200e-6, 200e-6]
theta = 60
radiometer = sensor_list.passive(5e9, theta)
sp = make_snowpack(thickness,
"exponential",
density=density,
temperature=temperature,
corr_length=corr_length)
# create the EM Model - Equivalent DMRTML
m = make_model("iba",
"dort",
rtsolver_options={
'n_max_stream': 64,
'process_coherent_layers': True
})
res = m.run(radiometer, sp)
print(res.TbV(), res.TbH())
#assert abs(res.TbV() - 261.1994214138529) < 1e-4
#assert abs(res.TbH() - 201.1848483718344) < 1e-4
# the new values may come from the correction of the bug in dort which limited
# the streams to the non-total reflection ones. This is not all clear yet...
#assert abs(res.TbV() - 261.05630770071855) < 1e-4
#assert abs(res.TbH() - 196.83495992559307) < 1e-4
# the new values come form the correction of 917->916.7
assert abs(res.TbV() - 261.06421847662216) < 1e-4
assert abs(res.TbH() - 196.8660443556061) < 1e-4
l_s = 2 #2 snow layers
thickness_s = np.array([0.05, 0.2])
p_ex_s = np.array([5e-5] * l_s)
temperature_s = np.linspace(273.15 - 25., 273.15 - 20, l_s)
density_s = [200, 340]
# create the snowpack
snowpack = make_snowpack(thickness=thickness_s,
microstructure_model="exponential",
density=density_s,
temperature=temperature_s,
corr_length=p_ex_s)
#add snowpack on top of ice column:
medium = snowpack + ice_column
# create the sensor
sensor = sensor_list.passive(1.4e9, 40.)
n_max_stream = 128 #TB calculation is more accurate if number of streams is increased (currently: default = 32);
#needs to be increased when using > 1 snow layer on top of sea ice!
m = make_model("iba", "dort", rtsolver_options={"n_max_stream": n_max_stream})
# run the model for bare sea ice:
res1 = m.run(sensor, ice_column)
# run the model for snow-covered sea ice:
res2 = m.run(sensor, medium)
# print TBs at horizontal and vertical polarization:
print(res1.TbH(), res1.TbV())
print(res2.TbH(), res2.TbV())
from smrt import make_snowpack, make_model, sensor_list
# import for memls
from smrt.utils import memls_legacy
# prepare snowpack
pc = 0.2e-3
snowpack = make_snowpack(thickness=[10],
microstructure_model="exponential",
density=[300],
temperature=[265],
corr_length=pc)
# create the sensor
theta = range(10, 80, 5)
radiometer = sensor_list.passive(37e9, theta)
# create the EM Model
m = make_model("iba", "dort")
# run the model
sresult = m.run(radiometer, snowpack)
# run MEMLS matlab code
mresult = memls_legacy.run(radiometer, snowpack)
# outputs
plt.plot(theta, sresult.TbV(), 'r-', label='SMRT V')
plt.plot(theta, sresult.TbH(), 'r--', label='SMRT H')
plt.plot(theta, mresult.TbV(), 'b-', label='MEMLS V')
plt.plot(theta, mresult.TbH(), 'b--', label='MEMLS H')
def test_equivalence_porosity_density():
l, n_max_stream, thickness, temperature, salinity = setup_seaice()
p_ex = np.array([1.0e-3] * (l)) # correlation length
ice_type = 'multiyear' # first-year (FY) or multi-year (MY) sea ice
porosity = 0.08 # ice porosity, in fraction
ice_column1 = make_ice_column(
ice_type,
thickness=thickness,
temperature=temperature,
microstructure_model="exponential",
brine_inclusion_shape="spheres",
# brine_inclusion_shape can be "spheres", "random_needles" or "mix" (a mix of spheres and needles)
salinity=salinity,
# either 'salinity' or 'brine_volume_fraction' should be given for sea ice; if salinity is given, brine volume fraction is calculated in the model; if none is given, ice is treated as fresh water ice
porosity=porosity,
# either density or 'porosity' should be set for sea ice. If porosity is given, density is calculated in the model. If none is given, ice is treated as having a porosity of 0% (no air inclusions)
# density = density,
corr_length=p_ex,
add_water_substrate="ocean" # see comment below
)
# Same, but giving the density instead:
density = [
bulk_ice_density(temp, salt, porosity)
for temp, salt in zip(temperature, salinity)
]
ice_column2 = make_ice_column(
ice_type,
thickness=thickness,
temperature=temperature,
microstructure_model="exponential",
brine_inclusion_shape="spheres",
# brine_inclusion_shape can be "spheres", "random_needles" or "mix" (a mix of spheres and needles)
salinity=salinity,
# either 'salinity' or 'brine_volume_fraction' should be given for sea ice; if salinity is given, brine volume fraction is calculated in the model; if none is given, ice is treated as fresh water ice
# porosity = porosity, # either density or 'porosity' should be set for sea ice. If porosity is given, density is calculated in the model. If none is given, ice is treated as having a porosity of 0% (no air inclusions)
density=density,
corr_length=p_ex,
add_water_substrate="ocean" # see comment below
)
sensor = sensor_list.passive(1.4e9, 40.)
n_max_stream = 128 # TB calculation is more accurate if number of streams is increased (currently: default = 32);
# needs to be increased when using > 1 snow layer on top of sea ice!
m = make_model("iba",
"dort",
rtsolver_options={"n_max_stream": n_max_stream})
# run the model for sea ice with porosity / density
res1 = m.run(sensor, ice_column1)
res2 = m.run(sensor, ice_column2)
print(res1.TbV(), res1.TbH())
print(res2.TbV(), res2.TbH())
# The two should be similar
assert abs(res1.TbV() - res2.TbV()) < 1e-4
assert abs(res1.TbH() - res2.TbH()) < 1e-4