def test_less_refringent_bottom_layer():
# Regression test 19-03-2018: value may change if other bugs found
snowpack = make_snowpack([0.2, 0.3],
"sticky_hard_spheres",
density=[290.0, 250.0],
radius=50e-6,
stickiness=0.2,
substrate=make_soil("transparent", 1, 270))
m = make_model("dmrt_qcacp_shortrange", "dort")
scat = active(10e9, 45)
res = m.run(scat, snowpack)
print(res.sigmaVV_dB(), res.sigmaHH_dB())
assert abs(res.sigmaVV_dB() - -50.25547167709486) < 1e-1
assert abs(res.sigmaHH_dB() - -50.52755576862734) < 1e-1
# The following test fails
# def test_less_refringent_bottom_layer_VV():
# # Regression test 19-03-2018: value may change if other bugs found
# snowpack = make_snowpack([0.2, 0.3], "sticky_hard_spheres", density = [290.0, 250.0], radius = 1e-4, stickiness=0.2)
# m = make_model("dmrt_qcacp_shortrange", "dort")
# scat = active(10e9, 45)
# res = m.run(scat, snowpack)
# print(res.sigmaVV())
# assert abs(res.sigmaVV() - 7.54253344e-05) < 1e-7
#
#
# def test_less_refringent_bottom_layer_HH():
# # Regression test 19-03-2018: value may change if other bugs found
# snowpack = make_snowpack([0.2, 0.3], "sticky_hard_spheres", density = [290.0, 250.0], radius = 1e-4, stickiness=0.2)
# m = make_model("dmrt_qcacp_shortrange", "dort")
# scat = active(10e9, 45)
# res = m.run(scat, snowpack)
# print(res.sigmaHH())
# assert abs(res.sigmaHH() - 7.09606407e-05) < 1e-7
def test_iba_oneconfig():
# prepare inputs
l = 2
n_max_stream = 64
nl = l//2 # // Forces integer division
thickness = np.array([0.1, 0.1]*nl)
thickness[-1] = 100 # last one is semi-infinit
p_ex = np.array([5e-5]*l)
temperature = np.array([250.0, 250.0]*nl)
density = [200, 400]*nl
# create the snowpack
snowpack = make_snowpack(thickness=thickness,
microstructure_model="exponential",
density=density,
temperature=temperature,
corr_length=p_ex)
# create the snowpack
m = make_model("iba_original", "dort")
# create the sensor
radiometer = sensor_list.amsre('37V')
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
#original absorption (Maetzler 1998)
assert abs(res.TbV() - 247.92344524715216) < 1e-4
assert abs(res.TbH() - 237.0863426896562) < 1e-4
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_simple_isotropic_atmosphere():
# prepare inputs
density = [300, 300]
temperature = [265, 265]
thickness = [0.4, 10]
radius = [200e-6, 400e-6]
#stickiness = [1000, 1000]
stickiness = [0.2, 0.2]
rads = sensor_list.amsre('36V')
atmos = SimpleIsotropicAtmosphere(30., 6., 0.90)
snowpack = make_snowpack(thickness, "sticky_hard_spheres",
density=density, temperature=temperature, radius=radius, stickiness=stickiness)
# create the EM Model - Equivalent DMRTML
iba = make_model("iba", "dort")
res1 = iba.run(rads, snowpack)
res2 = iba.run(rads, snowpack, atmosphere=atmos)
print('TB 1: ', res1.TbV(), 'TB2: ', res2.TbV())
#absorption with effective permittivity
#ok_(abs(res1.TbV() - 227.59158654174465) < 1e-2)
#ok_(abs(res2.TbV() - 214.64368439876984) < 1e-2)
#original absorption (Maetzler 1998)
ok_(abs(res1.TbV() - 223.925496277253) < 1e-2)
ok_(abs(res2.TbV() - 211.7175839349701) < 1e-2)
def test_less_refringent_bottom_layer_HH():
# Regression test 19-03-2018: value may change if other bugs found
snowpack = make_snowpack([0.2, 0.3],
"sticky_hard_spheres",
density=[290.0, 250.0],
radius=50e-6,
stickiness=0.2)
m = make_model("dmrt_qcacp_shortrange", "dort")
scat = active(10e9, 45)
res = m.run(scat, snowpack)
print(res.sigmaHH())
assert abs(res.sigmaHH() - 8.85784528e-06) < 1e-9
# The following test fails
# def test_less_refringent_bottom_layer_VV():
# # Regression test 19-03-2018: value may change if other bugs found
# snowpack = make_snowpack([0.2, 0.3], "sticky_hard_spheres", density = [290.0, 250.0], radius = 1e-4, stickiness=0.2)
# m = make_model("dmrt_qcacp_shortrange", "dort")
# scat = active(10e9, 45)
# res = m.run(scat, snowpack)
# print(res.sigmaVV())
# assert abs(res.sigmaVV() - 7.54253344e-05) < 1e-7
#
#
# def test_less_refringent_bottom_layer_HH():
# # Regression test 19-03-2018: value may change if other bugs found
# snowpack = make_snowpack([0.2, 0.3], "sticky_hard_spheres", density = [290.0, 250.0], radius = 1e-4, stickiness=0.2)
# m = make_model("dmrt_qcacp_shortrange", "dort")
# scat = active(10e9, 45)
# res = m.run(scat, snowpack)
# print(res.sigmaHH())
# assert abs(res.sigmaHH() - 7.09606407e-05) < 1e-7
def test_dmrt_oneconfig():
# prepare inputs
l = 2
nl = l//2 # // Forces integer division
thickness = np.array([0.1, 0.1]*nl)
thickness[-1] = 100 # last one is semi-infinit
radius = np.array([2e-4]*l)
temperature = np.array([250.0, 250.0]*nl)
density = [200, 400]*nl
stickiness = [0.1, 0.1]*nl
# create the snowpack
snowpack = make_snowpack(thickness,
"sticky_hard_spheres",
density=density,
temperature=temperature,
radius=radius,
stickiness=stickiness)
# create the EM Model
m = make_model("dmrt_shortrange", "dort")
# create the sensor
radiometer = amsre('37V')
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
ok_((res.TbV() - 202.34939425929616) < 1e-4)
ok_((res.TbH() - 187.05199255031036) < 1e-4)
def test_simple_isotropic_atmosphere():
# prepare inputs
density = [300, 300]
temperature = [265, 265]
thickness = [0.4, 10]
radius = [200e-6, 400e-6]
stickiness = [0.2, 0.2]
rads = sensor_list.amsre('36V')
atmos = SimpleIsotropicAtmosphere(30., 6., 0.90)
snowpack = make_snowpack(thickness, "sticky_hard_spheres", density=density,
temperature=temperature, radius=radius, stickiness=stickiness)
# create the EM Model - Equivalent DMRTML
iba = make_model("iba", "dort")
res1 = iba.run(rads, snowpack)
snowpack.atmosphere = atmos
res2 = iba.run(rads, snowpack)
print('TB 1: ', res1.TbV(), 'TB2: ', res2.TbV())
# absorption with effective permittivity
assert abs(res1.TbV() - 227.61318467710458) < 1e-2
assert abs(res2.TbV() - 214.66092232541834) < 1e-2
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 test_reflector_backscattering():
# prepare inputs
density = [0.1]
temperature = [210.0]
thickness = [0.01]
theta = np.arange(5, 60, 1)
radar = sensor_list.active(1e9, theta)
backscattering_coefficient = {'VV': invdB(-10), 'HH': invdB(-15)}
# Substrate: same for both sample locations. Permittivity is irrelevant parameter
backscatter_reflector = make_reflector(
specular_reflection=0,
backscattering_coefficient=backscattering_coefficient)
snowpack = make_snowpack(thickness,
"homogeneous",
density=density,
temperature=temperature,
substrate=backscatter_reflector)
# create the EM Model - Equivalent DMRTML
m = make_model("nonscattering", "dort", rtsolver_options=dict(m_max=5))
res = m.run(radar, snowpack)
print(res.sigmaVV_dB())
print(res.sigmaHH_dB())
assert np.all(np.abs(res.sigmaVV_dB() - (-10)) < 0.01)
assert np.all(np.abs(res.sigmaHH_dB() - (-15)) < 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 GetTb(Tz, H, NbLayers, Freq, Angle, NbStreams, Perm, Model, Tbatmo):
l = NbLayers # number of layers
nbinputlayers = np.size(Tz)
temperature = np.transpose(Tz) + 273.15
thickness = np.array([H / l] * l)
depthini = np.linspace(0, H, nbinputlayers)
depthfin = np.linspace(0, H, l)
temp = np.interp(depthfin, depthini, temperature[::-1])[::-1]
density = np.zeros(l)
radius = 1e-4
medium = list()
soilp = dmrtml.HUTRoughSoilParams(273)
for d in depthfin:
i = np.where(depthfin == d)[0]
density[i] = 921.9 - 595.3 * math.exp(-0.01859 * d)
if density[i] < 458.5:
medium.append('S')
elif density[i] < 458.5 and density[i] <= 900.:
medium.append('F')
else:
medium.append('I')
# Compute the permittivity for the whole profile
e_ice = Permittivity(Perm, temp, density, Freq)
if Model == "DMRT-ML":
res = dmrtml.dmrtml_bis(Freq,
NbStreams,
thickness,
density,
radius,
temp,
medium=medium,
soilp=soilp,
tbatmodown=Tbatmo,
eps_ice=(e_ice[0, :], e_ice[1, :]))
print(temp)
return res.TbV(Angle)
if Model == "SMRT":
if Perm == "Tiuri":
ice_permittivity_model = ice_permittivity_tiuri84
if Perm == "Matzler":
ice_permittivity_model = ice_permittivity_matzler87
# create the snowpack
snowpack = make_snowpack(thickness=thickness,
microstructure_model="sticky_hard_spheres",
density=density,
temperature=temp,
radius=radius,
ice_permittivity_model=ice_permittivity_model)
# create the snowpack
m = make_model("dmrt_qcacp_shortrange", "dort")
radiometer = sensor.passive(Freq, Angle)
res = m.run(radiometer, snowpack)
return res.TbV()
def test_less_refringent_bottom_layer_VV():
# Regression test 19-03-2018: value may change if other bugs found
snowpack = make_snowpack([0.2, 0.3], "sticky_hard_spheres", density = [290.0, 250.0], radius = 50e-6, stickiness=0.2,
substrate=make_soil("transparent", 1, 270))
m = make_model("dmrt_qcacp_shortrange", "dort")
scat = active(10e9, 45)
res = m.run(scat, snowpack)
print(res.sigmaVV())
assert abs(res.sigmaVV() - 9.42202173e-06) < 1e-9
def GetTb_SMRT(Tz, H, NbLayers, Freq, Angle, Perm):
l = NbLayers # number of layers
nbinputlayers = np.size(Tz)
thickness = np.array([H / l] * l)
#Prepare temperature field and interpolate on the layer altitude
temperature = np.transpose(Tz) + 273.15
zini = np.linspace(0, H, nbinputlayers)
zfin = np.linspace(0, H, l)
temp = np.interp(zfin, zini, temperature[::-1])[::-1]
#print(temperature, temp)
#plt.plot(temperature, zini[::-1])
#plt.plot(temp, zfin[::-1])
#plt.show()
density = np.zeros(l)
p_ex = np.zeros(l)
i = 0
for z in zfin:
density[i] = 1000. * (0.916 - 0.593 * math.exp(-0.01859 * z)
) #From Macelloni et al, 2016
#p_ex[i] = 1 - 0.9999 * math.exp(-0.01859 * z / 1e4)
i = i + 1
p_ex = 1. / (917 - density + 100) #100)
p_ex[0] = 1e-4
if Perm == "Tiuri":
ice_permittivity_model = ice_permittivity_tiuri84
if Perm == "Matzler":
ice_permittivity_model = ice_permittivity_matzler87
# create the snowpack
snowpack = make_snowpack(
thickness=thickness,
microstructure_model="exponential", #sticky_hard_spheres",
density=density,
temperature=temp,
#radius=p_ex,
corr_length=p_ex)
#stickiness = 0.1)
# ice_permittivity_model=ice_permittivity_model)
# create the snowpack
m = make_model("iba", "dort")
#m = make_model("dmrt_qcacp_shortrange", "dort")
# create the sensor
radiometer = sensor.passive(Freq, Angle)
# run the model
res = m.run(radiometer, snowpack)
# outputs
return res.TbV()
def run_model(snowpack):
# create the EM Model
m = make_model("dmrt_qcacp_shortrange", "dort")
# create the sensor
radiometer = sensor.passive(37e9,
40) # test at 40° to avoid the Brewster angle
# run the model
res = m.run(radiometer, snowpack)
return res
def test_dmrt_with_soil():
# prepare inputs
density = [300, 300]
temperature = [245, 245]
thickness = [0.1, 0.1]
radius = [200e-6, 400e-6]
stickiness = [1000, 1000]
soiltemperature = 270
clay = 0.3
sand = 0.4
drymatter = 1100
moisture = 0.2
roughness_rms = 1e-2
substrate = make_soil("soil_wegmuller",
"dobson85",
soiltemperature,
moisture=moisture,
roughness_rms=roughness_rms,
clay=clay,
sand=sand,
drymatter=drymatter)
snowpack = make_snowpack(thickness,
"sticky_hard_spheres",
density=density,
temperature=temperature,
radius=radius,
stickiness=stickiness,
substrate=substrate)
# create the EM Model
m = make_model("dmrt_qcacp_shortrange", "dort")
# create the sensor
radiometer = sensor.passive(37e9,
40) # test at 40° to avoid the Brewster angle
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
#assert (res.TbV() - 262.6214674671272) < 1e-4
assert (res.TbV() - 262.62154074526325) < 1e-4
#assert (res.TbH() - 255.88791903746) < 1e-4
assert (res.TbH() - 255.88831382514428) < 1e-4
def test_dmrt_oneconfig():
snowpack = setup_snowpack()
# create the EM Model
m = make_model("dmrt_qcacp_shortrange", "dort")
# create the sensor
radiometer = amsre("37V")
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
assert (res.TbV() - 202.1726891947754) < 1e-4
assert (res.TbH() - 187.45835882462404) < 1e-4
def test_geometrical_optics():
# prepare inputs
snowpack = setup_snowpack(l=2)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
radar = sensor_list.active(13e9, 55)
# run the model
res = m.run(radar, snowpack)
print(res.sigmaVV_dB(), res.sigmaHH_dB())
assert abs(res.sigmaVV_dB() - -27.35490756934666) < 1e-4
assert abs(res.sigmaHH_dB() - -27.727715758558222) < 1e-4
def test_geometrical_optics():
# prepare inputs
snowpack = setup_snowpack(l=2)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
radar = sensor_list.active(13e9, 55)
# run the model
res = m.run(radar, snowpack)
print(res.sigmaVV_dB(), res.sigmaHH_dB())
assert abs(res.sigmaVV_dB() - -29.098930693890576) < 1e-4
assert abs(res.sigmaHH_dB() - -28.974976041582334) < 1e-4
def test_iba_oneconfig_active():
# prepare inputs
snowpack = setup_snowpack(l=2)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
radar = sensor_list.active(frequency=19e9, theta_inc=55)
# run the model
res = m.run(radar, snowpack)
print(res.sigmaVV_dB(), res.sigmaHH_dB(), res.sigmaHV_dB())
abs(res.sigmaVV_dB() - (-24.04497237)) < 1e-4
abs(res.sigmaHH_dB() - (-24.41628343)) < 1e-4
abs(res.sigmaHV_dB() - (-51.53673914)) < 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_mixed_emmodel():
# prepare inputs
l = 2
nl = l // 2 # // Forces integer division
thickness = np.array([0.1, 0.1] * nl)
thickness[-1] = 100 # last one is semi-infinit
radius = np.array([2e-4] * l)
temperature = np.array([250.0, 250.0] * nl)
density = [200, 400] * nl
stickiness = [0.1, 0.1] * nl
emmodel = ["dmrt_qcacp_shortrange", "iba"] * nl
# create the snowpack
snowpack = make_snowpack(thickness,
"sticky_hard_spheres",
density=density,
temperature=temperature,
radius=radius,
stickiness=stickiness)
# create the EM Model
m = make_model(emmodel, "dort")
# create the sensor
radiometer = sensor_list.amsre('37V')
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
#assert (res.TbV() - 203.84730126016882) < 1e-4
#assert (res.TbH() - 189.53130277932084) < 1e-4
#assert (res.TbV() - 203.8473395866384) < 1e-4
#assert (res.TbH() - 189.53346053779396) < 1e-4
#assert (res.TbV() - 204.6641326749464) < 1e-4
#assert (res.TbH() - 190.42438454209372) < 1e-4
assert (res.TbV() - 204.61156255625286) < 1e-4
assert (res.TbH() - 190.5085529486018) < 1e-4
def test_iba_oneconfig_passive():
# prepare inputs
snowpack = setup_snowpack(l=2)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
radiometer = sensor_list.amsre('37V')
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
#absorption with effective permittivity
# abs(res.TbV() - 248.08794944809972) < 1e-4
# abs(res.TbH() - 237.3056263719142) < 1e-4
assert abs(res.TbV() - 248.08744066791073) < 1e-4
assert abs(res.TbH() - 237.30720491883298) < 1e-4
def test_dmrt_twoconfig():
snowpack = setup_snowpack()
# create the EM Model
m = make_model("dmrt_qcacp_shortrange", "dort")
# create the sensor
radiometer = amsre(["19", "37"])
print(radiometer.configurations)
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
assert (res.Tb(channel="37V") - 202.1726891947754) < 1e-4
assert (res.Tb(channel="37H") - 187.45835882462404) < 1e-4
assert (res.Tb(channel="19V") - 242.550043) < 1e-4
assert (res.Tb(channel="19H") - 230.118448) < 1e-4
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
def test_iba_oneconfig_active():
# prepare inputs
snowpack = setup_snowpack(l=2)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
radar = sensor_list.active(frequency=19e9, theta_inc=55)
# run the model
res = m.run(radar, snowpack)
print(res.sigmaVV_dB(), res.sigmaHH_dB(), res.sigmaHV_dB())
#assert abs(res.sigmaVV_dB() - (-24.04497237)) < 1e-4
#assert abs(res.sigmaHH_dB() - (-24.41628343)) < 1e-4
#assert abs(res.sigmaHV_dB() - (-51.53673914)) < 1e-4
assert abs(res.sigmaVV_dB() - (-25.784531777404144)) < 1e-4
assert abs(res.sigmaHH_dB() - (-25.661186004429812)) < 1e-4
assert abs(res.sigmaHV_dB() - (-32.33796852861883)) < 1e-4
def test_iba_oneconfig():
# prepare inputs
l = 2
n_max_stream = 64
nl = l // 2 # // Forces integer division
thickness = np.array([0.1, 0.1] * nl)
thickness[-1] = 100 # last one is semi-infinit
p_ex = np.array([5e-5] * l)
temperature = np.array([250.0, 250.0] * nl)
density = [200, 400] * nl
# create the snowpack
snowpack = make_snowpack(thickness=thickness,
microstructure_model="exponential",
density=density,
temperature=temperature,
corr_length=p_ex)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
radiometer = sensor_list.amsre('37V')
# run the model
res = m.run(radiometer, snowpack)
print(res.TbV(), res.TbH())
#absorption with effective permittivity
#ok_(abs(res.TbV() - 248.08807673119517) < 1e-4)
#ok_(abs(res.TbH() - 237.3106696695165) < 1e-4)
#original absorption (Maetzler 1998)
ok_(abs(res.TbV() - 247.92402825320272) < 1e-4)
ok_(abs(res.TbH() - 237.08967658310334) < 1e-4)
# prepare inputs
l = 2
n_max_stream = 64
nl = l // 2 # // Forces integer division
thickness = np.array([0.1, 0.1] * nl)
thickness[-1] = 100 # last one is semi-infinit
p_ex = np.array([5e-5] * l)
temperature = np.array([250.0, 250.0] * nl)
density = [200, 400] * nl
# create the snowpack
snowpack = make_snowpack(thickness=thickness,
microstructure_model="exponential",
density=density,
temperature=temperature,
corr_length=p_ex)
# create the snowpack
m = make_model("iba", "dort")
# create the sensor
sensor = sensor_list.amsre('37V')
# run the model
res = m.run(sensor, snowpack)
# outputs
print(res.TbV())
def run_model(
snow_depth,
ice_thickness,
ice_salinity,
ice_density,
temp,
snow_CL_e3,
ice_CL_e3,
snow_density,
snow_sal,
snow_roughness_rms,
snow_roughness_CL,
ice_roughness_rms,
ice_roughness_CL,
angles=np.arange(0, 51, 5),
):
"""Runs SMRT from geophysical variables and returns a dictionary of results"""
K_u = 13.575e9
K_a = 35e9
freqs = {K_u: 'Ku', K_a: 'Ka'}
snow_air_interface = IEM_Fung92_Briogoni10(
roughness_rms=snow_roughness_rms * 1e-3,
corr_length=snow_roughness_CL * 1e-3)
snow_ice_interface = IEM_Fung92_Briogoni10(
roughness_rms=ice_roughness_rms * 1e-3,
corr_length=ice_roughness_CL * 1e-3)
ice_column = make_ice_column(
ice_type='multiyear',
thickness=[ice_thickness],
temperature=temp,
brine_inclusion_shape='needles',
corr_length=ice_CL_e3 / 1000,
density=ice_density,
salinity=ice_salinity * PSU,
microstructure_model='exponential',
add_water_substrate='ocean',
ice_permittivity_model=saline_ice_permittivity_pvs_mixing,
interface=snow_ice_interface,
)
snowpack = make_snowpack(
thickness=[snow_depth],
microstructure_model="exponential",
density=snow_density,
temperature=temp,
corr_length=snow_CL_e3 / 1000,
salinity=snow_sal,
# ice_permittivity_model = saline_snow_permittivity_geldsetzer09,
interface=snow_air_interface,
)
medium = snowpack + ice_column
sensor = smrt.sensor.active([K_u, K_a],
angles,
polarization_inc=['H'],
polarization=['V', 'H'])
m = make_model("iba", "dort")
res = m.run(sensor, medium)
results = {}
for pol_inc, pol, freq in itertools.product(['H'], ['V', 'H'], [K_a, K_u]):
# Turn SMRT results object into a sliced dataframe
s_res = res.sigma_dB_as_dataframe(polarization_inc=pol_inc,
polarization=pol,
frequency=freq)
# Process slice into dictionary
results[f'{freqs[freq]}_{pol_inc}{pol}'] = np.array(s_res['sigma'])
return (results)
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())
