def test_utci_raises():
with pytest.raises(ValueError):
universal_thermal_climate_index(
{
"tas": np.array([295, 296]),
"sfcWind": np.array([6.0, 6.0]),
"hurs": np.array([100, 100]),
"huss": np.array([0.006, 0.006]),
},
np.array([303, 304]),
)
with pytest.raises(ValueError):
universal_thermal_climate_index({}, np.array([303, 304]))
def test_utci_array():
EXPECTED_RESULT = 273.15 + np.array([19.60850656, 21.2151128])
TEST_RESULT = universal_thermal_climate_index(
{
"tas": np.array([295, 296]),
"sfcWind": np.array([6.0, 6.0]),
"hurs": np.array([100, 100]),
},
np.array([303, 304]),
)
assert np.allclose(TEST_RESULT, EXPECTED_RESULT)
def test_utci_huss():
EXPECTED_RESULT = 273.15 + np.array([19.81876334, 20.98888025])
TEST_RESULT = universal_thermal_climate_index(
{
"tas": np.array([295, 296]),
"sfcWind": np.array([6.0, 6.0]),
"huss": np.array([0.0167, 0.0167]), # approx 100% RH at 295K, 1000hPa
},
np.array([303, 304]),
)
assert np.allclose(TEST_RESULT, EXPECTED_RESULT)
def test_utci_huss_ps():
EXPECTED_RESULT = 273.15 + np.array([17.86596553, 17.84009998])
TEST_RESULT = universal_thermal_climate_index(
{
"tas": np.array([295, 296]),
"sfcWind": np.array([6.0, 6.0]),
"huss": np.array([0.012, 0.010]),
"ps": np.array([96000, 75000]),
},
np.array([303, 304]),
)
assert np.allclose(TEST_RESULT, EXPECTED_RESULT)
def test_integration_array():
EXPECTED_RESULT = 273.15 + np.array([22.32159032, 18.02267449])
mrt = mean_radiant_temperature(
{
"rlds": np.array([150, 50]),
"rlus": np.array([350, 150]),
"rsdsdiff": np.array([400, 200]),
"rsus": np.array([100, 50]),
"rsds": np.array([700, 400]),
},
cos_zenith=np.array([0.5, 0.2]),
lit=np.array([1, 1]),
)
TEST_RESULT = universal_thermal_climate_index(
{
"tas": np.array([295, 296]),
"sfcWind": np.array([6.0, 6.0]),
"hurs": np.array([100, 100]),
},
mrt,
)
assert np.allclose(TEST_RESULT, EXPECTED_RESULT)
emissivity=0.97,
cos_zenith=mean_cosz,
lit=lit
)
# regrid wind to temperature grid
# don't think area-weighting makes sense, use bi-linear
uas_cube_regrid = uas_cube.regrid(tas_cube, iris.analysis.Linear())
vas_cube_regrid = vas_cube.regrid(tas_cube, iris.analysis.Linear())
wind = np.sqrt(uas_cube_regrid.data**2 + vas_cube_regrid.data**2)
# calculate UTCI
utci = universal_thermal_climate_index(
{
"tas": tas_cube.data,
"sfcWind": wind,
"huss": huss_cube.data
},
mrt
)
# make cubes out of output (only UTCI to save space and time)
utci_cube = iris.cube.Cube(
utci,
units='K',
var_name='utci',
long_name='Universal Thermal Climate Index',
dim_coords_and_dims=[
(tas_cube.coord('time'), 0),
(tas_cube.coord('latitude'), 1),
(tas_cube.coord('longitude'), 2)
]