def test_height_to_pressure(self):
"""Ensures correct output from height_to_pressure."""
these_pressures_pascals = standard_atmo.height_to_pressure(
HEIGHTS_M_ASL)
self.assertTrue(
numpy.allclose(these_pressures_pascals, PRESSURES_PASCALS))
def soundings_to_metpy_dictionaries(sounding_matrix,
field_names,
height_levels_m_agl=None,
storm_elevations_m_asl=None):
"""Converts soundings to format required by MetPy.
If `sounding_matrix` contains pressures, `height_levels_m_agl` and
`storm_elevations_m_asl` will not be used.
Otherwise, `height_levels_m_agl` and `storm_elevations_m_asl` will be used
to estimate the pressure levels for each sounding.
:param sounding_matrix: numpy array (E x H_s x F_s) of soundings.
:param field_names: list (length F_s) of field names, in the order that they
appear in `sounding_matrix`.
:param height_levels_m_agl: numpy array (length H_s) of height levels
(metres above ground level), in the order that they appear in
`sounding_matrix`.
:param storm_elevations_m_asl: length-E numpy array of storm elevations
(metres above sea level).
:return: list_of_metpy_dictionaries: length-E list of dictionaries. The
format of each dictionary is described in the input doc for
`sounding_plotting.plot_sounding`.
"""
error_checking.assert_is_string_list(field_names)
error_checking.assert_is_numpy_array(numpy.array(field_names),
num_dimensions=1)
check_soundings(sounding_matrix=sounding_matrix,
num_fields=len(field_names))
try:
pressure_index = field_names.index(soundings.PRESSURE_NAME)
pressure_matrix_pascals = sounding_matrix[..., pressure_index]
except ValueError:
error_checking.assert_is_geq_numpy_array(height_levels_m_agl, 0)
error_checking.assert_is_numpy_array(height_levels_m_agl,
num_dimensions=1)
error_checking.assert_is_numpy_array_without_nan(
storm_elevations_m_asl)
error_checking.assert_is_numpy_array(storm_elevations_m_asl,
num_dimensions=1)
num_height_levels = len(height_levels_m_agl)
num_examples = len(storm_elevations_m_asl)
check_soundings(sounding_matrix=sounding_matrix,
num_examples=num_examples,
num_height_levels=num_height_levels)
height_matrix_m_asl = numpy.full((num_examples, num_height_levels),
numpy.nan)
for i in range(num_examples):
height_matrix_m_asl[i, ...] = (height_levels_m_agl +
storm_elevations_m_asl[i])
pressure_matrix_pascals = standard_atmo.height_to_pressure(
height_matrix_m_asl)
try:
temperature_index = field_names.index(soundings.TEMPERATURE_NAME)
temperature_matrix_kelvins = sounding_matrix[..., temperature_index]
except ValueError:
virtual_pot_temp_index = field_names.index(
soundings.VIRTUAL_POTENTIAL_TEMPERATURE_NAME)
temperature_matrix_kelvins = (
temperature_conversions.temperatures_from_potential_temperatures(
potential_temperatures_kelvins=sounding_matrix[
..., virtual_pot_temp_index],
total_pressures_pascals=pressure_matrix_pascals))
try:
specific_humidity_index = field_names.index(
soundings.SPECIFIC_HUMIDITY_NAME)
dewpoint_matrix_kelvins = (
moisture_conversions.specific_humidity_to_dewpoint(
specific_humidities_kg_kg01=sounding_matrix[
..., specific_humidity_index],
total_pressures_pascals=pressure_matrix_pascals))
except ValueError:
relative_humidity_index = field_names.index(
soundings.RELATIVE_HUMIDITY_NAME)
dewpoint_matrix_kelvins = (
moisture_conversions.relative_humidity_to_dewpoint(
relative_humidities=sounding_matrix[...,
relative_humidity_index],
temperatures_kelvins=temperature_matrix_kelvins,
total_pressures_pascals=pressure_matrix_pascals))
temperature_matrix_celsius = temperature_conversions.kelvins_to_celsius(
temperature_matrix_kelvins)
dewpoint_matrix_celsius = temperature_conversions.kelvins_to_celsius(
dewpoint_matrix_kelvins)
try:
u_wind_index = field_names.index(soundings.U_WIND_NAME)
v_wind_index = field_names.index(soundings.V_WIND_NAME)
include_wind = True
except ValueError:
include_wind = False
num_examples = sounding_matrix.shape[0]
list_of_metpy_dictionaries = [None] * num_examples
for i in range(num_examples):
list_of_metpy_dictionaries[i] = {
soundings.PRESSURE_COLUMN_METPY:
pressure_matrix_pascals[i, :] * PASCALS_TO_MB,
soundings.TEMPERATURE_COLUMN_METPY:
temperature_matrix_celsius[i, :],
soundings.DEWPOINT_COLUMN_METPY: dewpoint_matrix_celsius[i, :],
}
if include_wind:
list_of_metpy_dictionaries[i].update({
soundings.U_WIND_COLUMN_METPY:
(sounding_matrix[i, ..., u_wind_index] *
METRES_PER_SECOND_TO_KT),
soundings.V_WIND_COLUMN_METPY:
(sounding_matrix[i, ..., v_wind_index] *
METRES_PER_SECOND_TO_KT)
})
return list_of_metpy_dictionaries
def _run_experiment_one_example(example_dict, example_index, max_noise_k_day01,
high_res_pressures_pa, high_res_heights_m_asl,
first_interp_method_name,
second_interp_method_name, interp_fluxes,
output_dir_name):
"""Runs interpolation experiment for one example (one profile).
H = number of levels in high-resolution grid
:param example_dict: Dictionary in format returned by
`example_io.read_file`.
:param example_index: Will run experiment for [i]th example, where
i = `example_index`.
:param max_noise_k_day01: See documentation at top of file.
:param high_res_pressures_pa: length-H numpy array of pressures (Pascals) in
high-resolution grid.
:param high_res_heights_m_asl: length-H numpy array of heights (metres above
sea level) in high-resolution grid.
:param first_interp_method_name: See documentation at top of file.
:param second_interp_method_name: Same.
:param interp_fluxes: Same.
:param output_dir_name: Same.
:return: max_difference_k_day01: Column-max difference between
low-resolution and fake low-resolution heating rates.
"""
example_id_string = (
example_dict[example_utils.EXAMPLE_IDS_KEY][example_index])
metadata_dict = example_utils.parse_example_ids([example_id_string])
surface_height_m_asl = geodetic_utils._get_elevation(
latitude_deg=metadata_dict[example_utils.LATITUDES_KEY][0],
longitude_deg=metadata_dict[example_utils.LONGITUDES_KEY][0])[0]
low_res_heights_m_agl = example_dict[example_utils.HEIGHTS_KEY]
low_res_heights_m_asl = surface_height_m_asl + low_res_heights_m_agl
low_res_pressures_pa = standard_atmo.height_to_pressure(
low_res_heights_m_asl)
low_res_heating_rates_k_day01 = example_utils.get_field_from_dict(
example_dict=example_dict,
field_name=example_utils.SHORTWAVE_HEATING_RATE_NAME)[example_index, :]
if interp_fluxes:
low_res_down_fluxes_w_m02 = example_utils.get_field_from_dict(
example_dict=example_dict,
field_name=example_utils.SHORTWAVE_DOWN_FLUX_NAME)[
example_index, :]
interp_object = interp1d(x=low_res_pressures_pa[::-1],
y=low_res_down_fluxes_w_m02[::-1],
kind=first_interp_method_name,
bounds_error=False,
fill_value='extrapolate',
assume_sorted=True)
high_res_down_fluxes_w_m02 = interp_object(high_res_pressures_pa)
low_res_up_fluxes_w_m02 = example_utils.get_field_from_dict(
example_dict=example_dict,
field_name=example_utils.SHORTWAVE_UP_FLUX_NAME)[example_index, :]
interp_object = interp1d(x=low_res_pressures_pa[::-1],
y=low_res_up_fluxes_w_m02[::-1],
kind=first_interp_method_name,
bounds_error=False,
fill_value='extrapolate',
assume_sorted=True)
high_res_up_fluxes_w_m02 = interp_object(high_res_pressures_pa)
high_res_heating_rates_k_day01 = _fluxes_to_heating_rate(
down_fluxes_w_m02=high_res_down_fluxes_w_m02,
up_fluxes_w_m02=high_res_up_fluxes_w_m02,
pressures_pa=high_res_pressures_pa)
interp_object = interp1d(x=high_res_pressures_pa[::-1],
y=high_res_down_fluxes_w_m02[::-1],
kind=second_interp_method_name,
bounds_error=True,
assume_sorted=True)
fake_low_res_down_fluxes_w_m02 = interp_object(low_res_pressures_pa)
interp_object = interp1d(x=high_res_pressures_pa[::-1],
y=high_res_up_fluxes_w_m02[::-1],
kind=second_interp_method_name,
bounds_error=True,
assume_sorted=True)
fake_low_res_up_fluxes_w_m02 = interp_object(low_res_pressures_pa)
fake_low_res_heating_rates_k_day01 = _fluxes_to_heating_rate(
down_fluxes_w_m02=fake_low_res_down_fluxes_w_m02,
up_fluxes_w_m02=fake_low_res_up_fluxes_w_m02,
pressures_pa=low_res_pressures_pa)
else:
interp_object = interp1d(x=low_res_pressures_pa[::-1],
y=low_res_heating_rates_k_day01[::-1],
kind=first_interp_method_name,
bounds_error=False,
fill_value='extrapolate',
assume_sorted=True)
high_res_heating_rates_k_day01 = interp_object(high_res_pressures_pa)
noise_values_k_day01 = numpy.random.uniform(
low=-max_noise_k_day01,
high=max_noise_k_day01,
size=high_res_heating_rates_k_day01.shape)
noise_values_k_day01 *= (
max_noise_k_day01 /
numpy.max(numpy.absolute(noise_values_k_day01)))
high_res_heating_rates_k_day01 += noise_values_k_day01
interp_object = interp1d(x=high_res_pressures_pa[::-1],
y=high_res_heating_rates_k_day01[::-1],
kind=second_interp_method_name,
bounds_error=True,
assume_sorted=True)
fake_low_res_heating_rates_k_day01 = interp_object(
low_res_pressures_pa)
high_res_heights_m_agl = high_res_heights_m_asl - surface_height_m_asl
figure_object, axes_object = profile_plotting.plot_one_variable(
values=low_res_heating_rates_k_day01,
heights_m_agl=low_res_heights_m_agl,
use_log_scale=True,
line_colour=LOW_RES_COLOUR,
line_width=LOW_RES_LINE_WIDTH)
profile_plotting.plot_one_variable(values=high_res_heating_rates_k_day01,
heights_m_agl=high_res_heights_m_agl,
use_log_scale=True,
line_colour=HIGH_RES_COLOUR,
line_width=HIGH_RES_LINE_WIDTH,
figure_object=figure_object)
profile_plotting.plot_one_variable(
values=fake_low_res_heating_rates_k_day01,
heights_m_agl=low_res_heights_m_agl,
use_log_scale=True,
line_colour=FAKE_LOW_RES_COLOUR,
line_width=FAKE_LOW_RES_LINE_WIDTH,
figure_object=figure_object)
axes_object.set_xlim(left=-0.5)
y_max = axes_object.get_ylim()[1]
axes_object.set_ylim(top=y_max * 1.05)
max_difference_k_day01 = numpy.max(
numpy.absolute(low_res_heating_rates_k_day01 -
fake_low_res_heating_rates_k_day01))
title_string = (
'Max diff between low-res and reconstructed low-res = {0:.4f} K day'
).format(max_difference_k_day01)
title_string = title_string + r'$^{-1}$'
axes_object.set_title(title_string, fontsize=20)
output_file_name = '{0:s}/{1:s}.jpg'.format(output_dir_name,
example_id_string)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_object.savefig(output_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
return max_difference_k_day01
THIS_FIRST_MATRIX = numpy.array([[0.9, 300, -10, 5, 0.02, 310],
[0.7, 285, 0, 15, 0.015, 310],
[0.95, 270, 15, 20, 0.01, 325],
[0.93, 260, 30, 30, 0.007, 341]])
THIS_SECOND_MATRIX = numpy.array([[0.7, 305, 0, 0, 0.015, 312.5],
[0.5, 290, 15, 12.5, 0.015, 310],
[0.8, 273, 25, 15, 0.012, 333],
[0.9, 262, 40, 20, 0.008, 345]])
SOUNDING_MATRIX_NO_PRESSURE = numpy.stack(
(THIS_FIRST_MATRIX, THIS_SECOND_MATRIX), axis=0)
HEIGHT_LEVELS_M_AGL = numpy.array([0, 1500, 3000, 5000], dtype=int)
STORM_ELEVATIONS_M_ASL = numpy.array([1045, 645], dtype=float)
FIRST_PRESSURES_PASCALS = standard_atmo.height_to_pressure(
STORM_ELEVATIONS_M_ASL[0] + HEIGHT_LEVELS_M_AGL)
FIRST_PRESSURES_PASCALS = numpy.reshape(FIRST_PRESSURES_PASCALS,
(len(FIRST_PRESSURES_PASCALS), 1))
THIS_FIRST_MATRIX = numpy.hstack((FIRST_PRESSURES_PASCALS, THIS_FIRST_MATRIX))
SECOND_PRESSURES_PASCALS = standard_atmo.height_to_pressure(
STORM_ELEVATIONS_M_ASL[1] + HEIGHT_LEVELS_M_AGL)
SECOND_PRESSURES_PASCALS = numpy.reshape(SECOND_PRESSURES_PASCALS,
(len(SECOND_PRESSURES_PASCALS), 1))
THIS_SECOND_MATRIX = numpy.hstack(
(SECOND_PRESSURES_PASCALS, THIS_SECOND_MATRIX))
SOUNDING_MATRIX_UNNORMALIZED = numpy.stack(
(THIS_FIRST_MATRIX, THIS_SECOND_MATRIX), axis=0)
FIRST_DEWPOINTS_KELVINS = moisture_conversions.specific_humidity_to_dewpoint(