def _create_output_cube(
self, cube: Cube, advected_data: ndarray, timestep: timedelta
) -> Cube:
"""
Create a cube and appropriate metadata to contain the advected forecast
Args:
cube:
Source cube (before advection)
advected_data:
Advected data
timestep:
Time difference between the advected output and the source
Returns:
The output cube
"""
attributes = generate_mandatory_attributes([cube])
if "institution" in cube.attributes.keys():
attributes["source"] = "{} Nowcast".format(attributes["institution"])
else:
attributes["source"] = "Nowcast"
advected_cube = create_new_diagnostic_cube(
cube.name(), cube.units, cube, attributes, data=advected_data
)
amend_attributes(advected_cube, self.attributes_dict)
set_history_attribute(advected_cube, "Nowcast")
self._update_time(cube.coord("time").copy(), advected_cube, timestep)
self._add_forecast_reference_time(cube.coord("time").copy(), advected_cube)
self._add_forecast_period(advected_cube, timestep)
return advected_cube
def create_wet_bulb_temperature_cube(self, temperature: Cube,
relative_humidity: Cube,
pressure: Cube) -> Cube:
"""
Creates a cube of wet bulb temperature values
Args:
temperature:
Cube of air temperatures.
relative_humidity:
Cube of relative humidities.
pressure:
Cube of air pressures.
Returns:
Cube of wet bulb temperature (K).
"""
temperature.convert_units("K")
relative_humidity.convert_units(1)
pressure.convert_units("Pa")
wbt_data = self._calculate_wet_bulb_temperature(
pressure.data, relative_humidity.data, temperature.data)
attributes = generate_mandatory_attributes(
[temperature, relative_humidity, pressure])
wbt = create_new_diagnostic_cube("wet_bulb_temperature",
"K",
temperature,
attributes,
data=wbt_data)
return wbt
def _create_template_cube(self, cube):
"""
Create a template cube to store the timezone masks. This cube has only
one scalar coordinate which is time, denoting when it is valid; this is
only relevant if using daylight savings. The attribute
includes_daylight_savings is set to indicate this.
Args:
cube (iris.cube.Cube):
A cube with the desired grid from which coordinates are taken
for inclusion in the template.
Returns:
iris.cube.Cube:
A template cube in which each timezone mask can be stored.
"""
time_point = np.array(self.time.timestamp(), dtype=np.int64)
time_coord = iris.coords.DimCoord(
time_point,
"time",
units=Unit("seconds since 1970-01-01 00:00:00", calendar="gregorian"),
)
for crd in cube.coords(dim_coords=False):
cube.remove_coord(crd)
cube.add_aux_coord(time_coord)
attributes = generate_mandatory_attributes([cube])
attributes["includes_daylight_savings"] = str(self.include_dst)
return create_new_diagnostic_cube(
"timezone_mask", 1, cube, attributes, dtype=np.int32
)
def create_symbol_cube(cubes):
"""
Create an empty weather symbol cube
Args:
cubes (list or iris.cube.CubeList):
List of input cubes used to generate weather symbols
Returns:
iris.cube.Cube:
A cube with suitable metadata to describe the weather symbols
that will fill it
"""
threshold_coord = find_threshold_coordinate(cubes[0])
template_cube = next(cubes[0].slices_over([threshold_coord])).copy()
# remove coordinates and bounds that do not apply to weather symbols
template_cube.remove_coord(threshold_coord)
for coord in template_cube.coords():
if coord.name() in ['forecast_period', 'time']:
coord.bounds = None
attributes = generate_mandatory_attributes(cubes)
symbols = create_new_diagnostic_cube(
"weather_code",
"1",
template_cube,
attributes,
optional_attributes=weather_code_attributes(),
dtype=np.int32)
return symbols
def _calculate_snow_fraction(self) -> Cube:
"""
Calculates the snow fraction data and interpolates to fill in the missing points.
Returns:
Snow fraction cube.
"""
with np.errstate(divide="ignore", invalid="ignore"):
snow_fraction = self.snow.data / (self.rain.data + self.snow.data)
snow_fraction_cube = create_new_diagnostic_cube(
"snow_fraction",
"1",
template_cube=self.rain,
mandatory_attributes=generate_mandatory_attributes(
iris.cube.CubeList([self.rain, self.snow]),
model_id_attr=self.model_id_attr,
),
data=snow_fraction,
)
spatial_dims = [
snow_fraction_cube.coord(axis=n).name() for n in ["y", "x"]
]
snow_fraction_interpolated = iris.cube.CubeList()
for snow_fraction_slice in snow_fraction_cube.slices(spatial_dims):
snow_fraction_interpolated.append(
snow_fraction_slice.copy(
interpolate_missing_data(snow_fraction_slice.data,
method="nearest")))
return snow_fraction_interpolated.merge_cube()
def process(self, cubes):
"""
Calculate the convective ratio from the convective and dynamic components as:
convective_ratio = convective / (convective + dynamic)
If convective + dynamic is zero, then the resulting point is masked.
Args:
cubes (List[iris.cube.Cube, iris.cube.Cube]):
Both the convective and dynamic components as iris.cube.Cube in a list
with names 'lwe_convective_precipitation_rate' and
'lwe_stratiform_precipitation_rate'
Returns:
iris.cube.Cube:
Cube containing the convective ratio.
"""
self._split_input(cubes)
attributes = generate_mandatory_attributes([self.convective])
output_cube = create_new_diagnostic_cube(
"convective_ratio",
"1",
self.convective,
attributes,
data=self._convective_ratio(),
)
return output_cube
def calculate_sleet_probability(prob_of_snow,
prob_of_rain):
"""
This calculates the probability of sleet using the calculation:
prob(sleet) = 1 - (prob(snow) + prob(rain))
Args:
prob_of_snow (iris.cube.Cube):
Cube of the probability of snow.
prob_of_rain (iris.cube.Cube):
Cube of the probability of rain.
Returns:
iris.cube.Cube:
Cube of the probability of sleet.
Raises:
ValueError: If the cube contains negative values for the the
probability of sleet.
"""
ones = np.ones((prob_of_snow.shape), dtype="float32")
sleet_prob = (ones - (prob_of_snow.data + prob_of_rain.data))
if np.any(sleet_prob < 0.0):
msg = ("Negative values of sleet probability have been calculated.")
raise ValueError(msg)
# In this case we want to copy all the attributes from the prob_of_snow cube
mandatory_attributes = generate_mandatory_attributes([
prob_of_rain, prob_of_snow])
probability_of_sleet = create_new_diagnostic_cube(
'probability_of_sleet', '1', prob_of_snow, mandatory_attributes,
data=sleet_prob)
return probability_of_sleet
def create_symbol_cube(self, cubes: Union[List[Cube], CubeList]) -> Cube:
"""
Create an empty weather symbol cube
Args:
cubes:
List of input cubes used to generate weather symbols
Returns:
A cube with suitable metadata to describe the weather symbols
that will fill it and data initiated with the value -1 to allow
any unset points to be readily identified.
"""
threshold_coord = find_threshold_coordinate(self.template_cube)
template_cube = next(self.template_cube.slices_over([threshold_coord
])).copy()
# remove coordinates and bounds that do not apply to weather symbols
template_cube.remove_coord(threshold_coord)
mandatory_attributes = generate_mandatory_attributes(cubes)
optional_attributes = weather_code_attributes()
if self.model_id_attr:
optional_attributes.update(
update_model_id_attr_attribute(cubes, self.model_id_attr))
symbols = create_new_diagnostic_cube(
"weather_code",
"1",
template_cube,
mandatory_attributes,
optional_attributes=optional_attributes,
data=np.ma.masked_all_like(template_cube.data).astype(np.int32),
)
return symbols
def _reformat_analysis_cube(self, attribute_changes):
"""
Add forecast reference time and forecast period coordinates (if they do
not already exist) and nowcast attributes to analysis cube
"""
coords = [coord.name() for coord in self.analysis_cube.coords()]
if "forecast_reference_time" not in coords:
frt_coord = self.analysis_cube.coord("time").copy()
frt_coord.rename("forecast_reference_time")
self.analysis_cube.add_aux_coord(frt_coord)
if "forecast_period" not in coords:
self.analysis_cube.add_aux_coord(
AuxCoord(np.array([0], dtype=np.int32), "forecast_period", "seconds")
)
self.analysis_cube.attributes = generate_mandatory_attributes(
[self.analysis_cube]
)
self.analysis_cube.attributes["source"] = "MONOW"
self.analysis_cube.attributes[
"title"
] = "MONOW Extrapolation Nowcast on UK 2 km Standard Grid"
set_history_attribute(self.analysis_cube, "Nowcast")
if attribute_changes is not None:
amend_attributes(self.analysis_cube, attribute_changes)
def test_metadata(self):
"""Test that the metadata on the resulting cube is as expected"""
expected_attributes = generate_mandatory_attributes([self.cube])
result = self.plugin.process(self.cube)
self.assertEqual(result.name(), self.cube.name() + "_integral")
self.assertEqual(result.units, "{} m".format(self.cube.units))
self.assertDictEqual(result.attributes, expected_attributes)
def calculate_feels_like_temperature(temperature,
wind_speed,
relative_humidity,
pressure,
model_id_attr=None):
"""
Calculates the feels like temperature using a combination of
the wind chill index and Steadman's apparent temperature equation.
Args:
temperature (iris.cube.Cube):
Cube of air temperatures
wind_speed (iris.cube.Cube):
Cube of 10m wind speeds
relative_humidity (iris.cube.Cube):
Cube of relative humidities
pressure (iris.cube.Cube):
Cube of air pressure
model_id_attr (str):
Name of the attribute used to identify the source model for
blending.
Returns:
iris.cube.Cube:
Cube of feels like temperatures in the same units as the input
temperature cube.
"""
t_cube = temperature.copy()
t_cube.convert_units('degC')
t_celsius = t_cube.data
w_cube = wind_speed.copy()
w_cube.convert_units('m s-1')
p_cube = pressure.copy()
p_cube.convert_units('Pa')
rh_cube = relative_humidity.copy()
rh_cube.convert_units('1')
apparent_temperature = _calculate_apparent_temperature(
t_celsius, w_cube.data, rh_cube.data, p_cube.data)
w_cube.convert_units('km h-1')
wind_chill = _calculate_wind_chill(t_celsius, w_cube.data)
feels_like_temperature = _feels_like_temperature(t_celsius,
apparent_temperature,
wind_chill)
attributes = generate_mandatory_attributes(
[temperature, wind_speed, relative_humidity, pressure],
model_id_attr=model_id_attr)
feels_like_temperature_cube = create_new_diagnostic_cube(
"feels_like_temperature",
"degC",
temperature,
attributes,
data=feels_like_temperature)
feels_like_temperature_cube.convert_units(temperature.units)
return feels_like_temperature_cube
def test_model_id_consensus(self):
"""Test model ID attribute can be specified and inherited"""
expected_attributes = self.attributes.copy()
expected_attributes["mosg__model_configuration"] = "uk_det"
result = generate_mandatory_attributes(
[self.t_cube, self.p_cube, self.rh_cube],
model_id_attr="mosg__model_configuration")
self.assertDictEqual(result, expected_attributes)
def test_missing_attribute(self):
"""Test defaults are triggered if mandatory attribute is missing
from one input"""
expected_attributes = self.attributes
expected_attributes["title"] = MANDATORY_ATTRIBUTE_DEFAULTS["title"]
self.t_cube.attributes.pop("title")
result = generate_mandatory_attributes([self.t_cube, self.p_cube, self.rh_cube])
self.assertDictEqual(result, expected_attributes)
def _output_metadata(self) -> Tuple[Cube, Dict]:
"""Returns template cube and mandatory attributes for result"""
template = next(
self.cubes[0].slices_over(find_threshold_coordinate(self.cubes[0]))
)
template.remove_coord(find_threshold_coordinate(self.cubes[0]))
attributes = generate_mandatory_attributes(self.cubes)
return template, attributes
def _apply_error_to_forecast(self, forecast_cube: Cube,
error_percentiles_cube: Cube) -> Cube:
"""Apply the error distributions (as error percentiles) to the forecast cube.
The result is a series (sub-ensemble) of values for each forecast realization.
Note:
Within the RainForests approach we work with an additive error correction
as opposed to a multiplicative correction used in ECPoint. The advantage of
using an additive error is that we are also able to calibrate zero-values in
the input forecast.
Warning:
After applying the error distributions to the forecast cube, values outside
the expected bounds of the forecast parameter can arise. These values occur when
when the input forecast value is between error thresholds and there exists a
lower bound on the observable value (eg. 0 in the case of rainfall).
In this situation, error thresholds below the residual value (min(obs) - fcst)
must have a probability of exceedance of 1, whereas as error thresholds above
this value can take on any value between [0, 1]. In the subsequent step where
error percentile values are extracted, the linear interpolation in mapping from
probabilities to percentiles can give percentile values that lie below the
residual value; when these are applied to the forecast value, they result in
forecast values outside the expected bounds of the forecast parameter in the
resultant sub-ensemble.
To address this, we remap all values outside of the expected bounds to nearest
bound (eg. negative values are mapped to 0 in the case of rainfall).
Args:
forecast_cube:
Cube containing the forecast to be calibrated.
error_percentiles_cube:
Cube containing percentile values for the error distributions.
Returns:
Cube containing the forecast sub-ensembles.
"""
# Apply the error_percentiles to the forecast_cube (additive correction)
forecast_subensembles_data = (forecast_cube.data[:, np.newaxis] +
error_percentiles_cube.data)
# RAINFALL SPECIFIC IMPLEMENTATION:
# As described above, we need to address value outside of expected bounds.
# In the case of rainfall, we map all negative values to 0.
forecast_subensembles_data = np.maximum(0.0,
forecast_subensembles_data)
# Return cube containing forecast subensembles
return create_new_diagnostic_cube(
name=forecast_cube.name(),
units=forecast_cube.units,
template_cube=error_percentiles_cube,
mandatory_attributes=generate_mandatory_attributes([forecast_cube
]),
optional_attributes=forecast_cube.attributes,
data=forecast_subensembles_data,
)
def process(self, cubes: CubeList, model_id_attr: str = None) -> Cube:
"""
From the supplied CAPE and precipitation-rate cubes, calculate a probability
of lightning cube.
Args:
cubes:
Cubes of CAPE and Precipitation rate.
model_id_attr:
The name of the dataset attribute to be used to identify the source
model when blending data from different models.
Returns:
Cube of lightning data
Raises:
ValueError:
If one of the cubes is not found or doesn't match the other
"""
cape, precip = self._get_inputs(cubes)
cape_true = LatitudeDependentThreshold(
lambda lat: latitude_to_threshold(
lat, midlatitude=350.0, tropics=500.0),
threshold_units="J kg-1",
comparison_operator=">",
)(cape)
precip_true = LatitudeDependentThreshold(
lambda lat: latitude_to_threshold(
lat, midlatitude=1.0, tropics=4.0),
threshold_units="mm h-1",
comparison_operator=">",
)(precip)
data = cape_true.data * precip_true.data
cube = create_new_diagnostic_cube(
name=
"probability_of_number_of_lightning_flashes_per_unit_area_above_threshold",
units="1",
template_cube=precip,
data=data.astype(FLOAT_DTYPE),
mandatory_attributes=generate_mandatory_attributes(
cubes, model_id_attr=model_id_attr),
)
coord = DimCoord(
np.array([0], dtype=FLOAT_DTYPE),
units="m-2",
long_name="number_of_lightning_flashes_per_unit_area",
var_name="threshold",
attributes={"spp__relative_to_threshold": "greater_than"},
)
cube.add_aux_coord(coord)
return cube
def test_no_consensus(self):
"""Test default values if input fields do not all agree"""
self.t_cube.attributes = {
"source": "Met Office Unified Model Version 1000",
"institution": "BOM",
"title": "UKV Model Forecast on 20 km Global Grid",
}
result = generate_mandatory_attributes([self.t_cube, self.p_cube, self.rh_cube])
self.assertDictEqual(result, MANDATORY_ATTRIBUTE_DEFAULTS)
def create_output_cube(self, cube: Cube, local_time: datetime) -> Cube:
"""
Constructs the output cube
Args:
cube:
Cube of data to extract timezone-offsets from. Must contain a time
coord spanning all the timezones.
local_time:
The "local" time of the output cube as %Y%m%dT%H%MZ. This will form a
scalar "time_in_local_timezone" coord on the output cube, while the
"time" coord will be auxillary to the spatial coords and will show the
UTC time that matches the local_time at each point.
"""
template_cube = cube.slices_over("time").next().copy()
template_cube.remove_coord("time")
template_cube.remove_coord("forecast_period")
output_cube = create_new_diagnostic_cube(
template_cube.name(),
template_cube.units,
template_cube,
generate_mandatory_attributes([template_cube]),
optional_attributes=template_cube.attributes,
data=self.output_data,
)
# Copy cell-methods from template_cube
[output_cube.add_cell_method(cm) for cm in template_cube.cell_methods]
# Create a local time coordinate to help with plotting data.
local_time_coord_standards = TIME_COORDS["time_in_local_timezone"]
local_time_units = cf_units.Unit(
local_time_coord_standards.units,
calendar=local_time_coord_standards.calendar,
)
timezone_points = np.array(
np.round(local_time_units.date2num(local_time)),
dtype=local_time_coord_standards.dtype,
)
output_cube.add_aux_coord(
AuxCoord(
timezone_points,
long_name="time_in_local_timezone",
units=local_time_units,
)
)
output_cube.add_aux_coord(
AuxCoord(
self.time_points,
bounds=self.time_bounds,
standard_name="time",
units=self.time_units,
),
[n + output_cube.ndim for n in [-2, -1]],
)
return output_cube
def _create_output_cube(self, template, data, points, bounds):
"""
Populates a template cube with data from the integration
Args:
template (iris.cube.Cube):
Copy of upper or lower bounds cube, based on direction of
integration
data (list or numpy.ndarray):
Integrated data
points (list or numpy.ndarray):
Points values for the integrated coordinate. These will not
match the template cube if any slices were skipped in the
integration, and therefore are used to slice the template cube
to match the data array.
bounds (list or numpy.ndarray):
Bounds values for the integrated coordinate
Returns:
iris.cube.Cube
"""
# extract required slices from template cube
template = template.extract(
iris.Constraint(coord_values={
self.coord_name_to_integrate: lambda x: x in points
}))
# re-promote integrated coord to dimension coord if need be
aux_coord_names = [coord.name() for coord in template.aux_coords]
if self.coord_name_to_integrate in aux_coord_names:
template = iris.util.new_axis(template,
self.coord_name_to_integrate)
# order dimensions on the template cube so that the integrated
# coordinate is first (as this is the leading dimension on the
# data array)
enforce_coordinate_ordering(template, self.coord_name_to_integrate)
# generate appropriate metadata for new cube
attributes = generate_mandatory_attributes([template])
coord_dtype = template.coord(self.coord_name_to_integrate).dtype
name, units = self._generate_output_name_and_units()
# create new cube from template
integrated_cube = create_new_diagnostic_cube(name,
units,
template,
attributes,
data=np.array(data))
integrated_cube.coord(self.coord_name_to_integrate).bounds = np.array(
bounds).astype(coord_dtype)
# re-order cube to match dimensions of input cube
ordered_dimensions = get_dim_coord_names(self.input_cube)
enforce_coordinate_ordering(integrated_cube, ordered_dimensions)
return integrated_cube
def setUp(self):
"""Set up the plugin and cubes for testing."""
super().setUp()
frt_dt = datetime.datetime(2017, 11, 10, 0, 0)
time_dt = datetime.datetime(2017, 11, 10, 4, 0)
data = np.ones((3, 3), dtype=np.float32)
self.historic_forecast = _create_historic_forecasts(
data, time_dt, frt_dt,
).merge_cube()
data_with_realizations = np.ones((3, 3, 3), dtype=np.float32)
self.historic_forecast_with_realizations = _create_historic_forecasts(
data_with_realizations, time_dt, frt_dt, realizations=[0, 1, 2],
).merge_cube()
self.optimised_coeffs = np.array([0, 1, 2, 3], np.int32)
self.distribution = "norm"
self.desired_units = "degreesC"
self.predictor = "mean"
self.plugin = Plugin(
distribution=self.distribution,
desired_units=self.desired_units,
predictor=self.predictor,
)
self.expected_frt = (
self.historic_forecast.coord("forecast_reference_time").cell(-1).point
)
self.expected_x_coord_points = np.median(
self.historic_forecast.coord(axis="x").points
)
self.historic_forecast.coord(axis="x").guess_bounds()
self.expected_x_coord_bounds = np.array(
[
[
np.min(self.historic_forecast.coord(axis="x").bounds),
np.max(self.historic_forecast.coord(axis="x").bounds),
]
]
)
self.expected_y_coord_points = np.median(
self.historic_forecast.coord(axis="y").points
)
self.historic_forecast.coord(axis="y").guess_bounds()
self.expected_y_coord_bounds = np.array(
[
[
np.min(self.historic_forecast.coord(axis="y").bounds),
np.max(self.historic_forecast.coord(axis="y").bounds),
]
]
)
self.attributes = generate_mandatory_attributes([self.historic_forecast])
self.attributes["diagnostic_standard_name"] = self.historic_forecast.name()
self.attributes["distribution"] = self.distribution
self.attributes["title"] = "Ensemble Model Output Statistics coefficients"
def _create_solar_time_cube(
self,
solar_time_data: ndarray,
target_grid: Cube,
time: datetime,
new_title: Optional[str],
) -> Cube:
"""Create solar time cube for the specified valid time.
Args:
solar_time_data:
Solar time data.
target_grid:
Cube containing spatial grid over which the solar time has been
calculated.
time:
Time associated with the local solar time.
new_title:
New title for the output cube attributes. If None, this attribute is
left out since it has no prescribed standard.
Returns:
Solar time data as an iris cube.
"""
X_coord = target_grid.coord(axis="X")
Y_coord = target_grid.coord(axis="Y")
time_coord = AuxCoord(
np.array(time.replace(tzinfo=timezone.utc).timestamp(),
dtype=np.int64),
standard_name="time",
units=cf_units.Unit(
"seconds since 1970-01-01 00:00:00 UTC",
calendar=cf_units.CALENDAR_STANDARD,
),
)
attrs = generate_mandatory_attributes([target_grid])
attrs["source"] = "IMPROVER"
if new_title is not None:
attrs["title"] = new_title
else:
attrs.pop("title", None)
solar_time_cube = Cube(
solar_time_data.astype(np.float32),
long_name=SOLAR_TIME_CF_NAME,
units="hours",
dim_coords_and_dims=[(Y_coord, 0), (X_coord, 1)],
aux_coords_and_dims=[(time_coord, None)],
attributes=attrs,
)
return solar_time_cube
def process(self, cube):
"""
Calculate the convective ratio either for the underlying field e.g.
precipitation rate, or using the differences between adjacent grid
squares.
If the difference between adjacent grid squares is used, firstly the
absolute differences are calculated, and then the difference cubes are
thresholded using a high and low threshold. The thresholded difference
cubes are then summed in order to put these cubes back onto the grid
of the original cube. The convective ratio is then calculated by
applying neighbourhood processing to the resulting cubes by dividing
the high threshold cube by the low threshold cube.
Args:
cube (iris.cube.Cube):
The cube from which the convective ratio will be calculated.
Returns:
iris.cube.Cube:
Cube containing the convective ratio defined as the ratio
between a cube with a high threshold applied and a cube with a
low threshold applied.
"""
cubelist = iris.cube.CubeList([])
threshold_list = [self.lower_threshold, self.higher_threshold]
if self.use_adjacent_grid_square_differences:
for threshold in threshold_list:
diff_cubelist = self.absolute_differences_between_adjacent_grid_squares(
cube)
thresholded_cubes = self.iterate_over_threshold(
diff_cubelist, threshold)
cubelist.append(
self.sum_differences_between_adjacent_grid_squares(
cube, thresholded_cubes))
else:
for threshold in threshold_list:
cubelist.extend(self.iterate_over_threshold([cube], threshold))
convective_ratios = self._calculate_convective_ratio(
cubelist, threshold_list)
attributes = generate_mandatory_attributes([cube])
output_cube = create_new_diagnostic_cube("convective_ratio",
"1",
cube,
attributes,
data=convective_ratios)
return output_cube
def process(self, cubes: Union[CubeList, List[Cube]]) -> Cube:
"""
Derives the probability of a precipitation phase at the surface. If
the snow-sleet falling-level is supplied, this is the probability of
snow at (or below) the surface. If the sleet-rain falling-level is
supplied, this is the probability of rain at (or above) the surface.
If the hail-rain falling-level is supplied, this is the probability
of rain from hail at (or above) the surface.
Args:
cubes:
Contains cubes of the altitude of the phase-change level (this
can be snow->sleet, hail->rain or sleet->rain) and the altitude
of the orography.
Returns:
Cube containing the probability of a specific precipitation phase
reaching the surface orography. If the falling_level_cube was
snow->sleet, then this will be the probability of snow at the
surface. If the falling_level_cube was sleet->rain, then this
will be the probability of rain from sleet at the surface.
If the falling_level_cube was hail->rain, then this
will be the probability of rain from hail at the surface.
The probabilities are categorical (1 or 0) allowing
precipitation to be divided uniquely between snow, sleet and
rain phases.
"""
self._extract_input_cubes(cubes)
processed_falling_level = iris.util.squeeze(
self.get_discriminating_percentile(self.falling_level_cube))
result_data = np.where(
self.comparator(self.orography_cube.data,
processed_falling_level.data),
1,
0,
).astype(np.int8)
mandatory_attributes = generate_mandatory_attributes(
[self.falling_level_cube])
cube = create_new_diagnostic_cube(
f"probability_of_{self.param}_at_surface",
Unit("1"),
self.falling_level_cube,
mandatory_attributes,
data=result_data,
)
return cube
def _define_metadata(forecast_slice):
"""
Define metadata that is specifically required for reliability table
cubes, whilst ensuring any mandatory attributes are also populated.
Args:
forecast_slice (iris.cube.Cube):
The source cube from which to get pre-existing metadata of use.
Returns:
dict:
A dictionary of attributes that are appropriate for the
reliability table cube.
"""
attributes = generate_mandatory_attributes([forecast_slice])
attributes["title"] = "Reliability calibration data table"
return attributes
def _make_updraught_cube(self, data: np.ndarray) -> Cube:
"""Puts the data array into a CF-compliant cube"""
attributes = {}
if self.model_id_attr:
attributes[self.model_id_attr] = self.precip.attributes[
self.model_id_attr]
cube = create_new_diagnostic_cube(
"maximum_vertical_updraught",
"m s-1",
self.precip,
mandatory_attributes=generate_mandatory_attributes(
[self.precip, self.cape]),
optional_attributes=attributes,
data=data,
)
return cube
def _create_output_cube(self, orogenh_data, reference_cube):
"""Creates a cube containing orographic enhancement values in SI units.
Args:
orogenh_data (numpy.ndarray):
Orographic enhancement value in mm h-1
reference_cube (iris.cube.Cube):
Cube with the correct time and forecast period coordinates on
the UK standard grid
Returns:
iris.cube.Cube:
Orographic enhancement cube (m s-1)
"""
# create cube containing high resolution data in mm/h
x_coord = self.topography.coord(axis="x")
y_coord = self.topography.coord(axis="y")
for coord in [x_coord, y_coord]:
coord.points = coord.points.astype(np.float32)
if coord.bounds is not None:
coord.bounds = coord.bounds.astype(np.float32)
aux_coords = []
for coord in ["time", "forecast_reference_time", "forecast_period"]:
aux_coords.append((reference_cube.coord(coord), None))
attributes = generate_mandatory_attributes([reference_cube])
attributes["title"] = "unknown" # remove possible wrong grid info.
for key in MOSG_GRID_ATTRIBUTES:
try:
attributes[key] = self.topography.attributes[key]
except KeyError:
pass
orog_enhance_cube = iris.cube.Cube(
orogenh_data,
long_name="orographic_enhancement",
units="mm h-1",
attributes=attributes,
dim_coords_and_dims=[(y_coord, 0), (x_coord, 1)],
aux_coords_and_dims=aux_coords,
)
orog_enhance_cube.convert_units("m s-1")
return orog_enhance_cube
def create_phase_change_level_cube(self, wbt, phase_change_level):
"""
Populate output cube with phase change data
Args:
wbt (iris.cube.Cube):
Wet bulb temperature cube on height levels
phase_change_level (numpy.ndarray):
Calculated phase change level in metres
Returns:
iris.cube.Cube
"""
name = "altitude_of_{}_level".format(self.phase_change_name)
attributes = generate_mandatory_attributes([wbt])
template = next(wbt.slices_over(["height"])).copy()
template.remove_coord("height")
return create_new_diagnostic_cube(
name, "m", template, attributes, data=phase_change_level
)
def _create_daynight_mask(self, cube):
"""
Create blank daynight mask cube
Args:
cube (iris.cube.Cube):
cube with the times and coordinates required for mask
Returns:
iris.cube.Cube:
Blank daynight mask cube. The resulting cube will be the
same shape as the time, y, and x coordinate, other coordinates
will be ignored although they might appear as attributes
on the cube as it is extracted from the first slice.
"""
slice_coords = [cube.coord(axis="y"), cube.coord(axis="x")]
if cube.coord("time") in cube.coords(dim_coords=True):
slice_coords.insert(0, cube.coord("time"))
template = next(cube.slices(slice_coords))
demoted_coords = [
crd
for crd in cube.coords(dim_coords=True)
if crd not in template.coords(dim_coords=True)
]
for crd in demoted_coords:
template.remove_coord(crd)
attributes = generate_mandatory_attributes([template])
title_attribute = {"title": "Day-Night mask"}
data = np.full(template.data.shape, self.night, dtype=np.int32)
daynight_mask = create_new_diagnostic_cube(
"day_night_mask",
1,
template,
attributes,
optional_attributes=title_attribute,
data=data,
dtype=np.int32,
)
return daynight_mask
def calculate_sleet_probability(prob_of_snow: Cube,
prob_of_rain: Cube) -> Cube:
"""
This calculates the probability of sleet using the calculation:
prob(sleet) = 1 - (prob(snow) + prob(rain))
Args:
prob_of_snow:
Cube of the probability of snow. This can be a fraction (0 <= x <= 1) or
categorical (0 or 1)
prob_of_rain:
Cube of the probability of rain. This can be a fraction (0 <= x <= 1) or
categorical (0 or 1)
Returns:
Cube of the probability of sleet. This will be fractional or categorical,
matching the highest precision of the inputs.
Raises:
ValueError: If the cube contains negative values for the the
probability of sleet.
"""
sleet_prob = 1 - (prob_of_snow.data + prob_of_rain.data)
if np.any(sleet_prob < 0):
msg = "Negative values of sleet probability have been calculated."
raise ValueError(msg)
# Copy all of the attributes from the prob_of_snow cube
mandatory_attributes = generate_mandatory_attributes(
[prob_of_rain, prob_of_snow])
probability_of_sleet = create_new_diagnostic_cube("probability_of_sleet",
"1",
prob_of_snow,
mandatory_attributes,
data=sleet_prob)
return probability_of_sleet
def _calculate_freezing_rain_probability(self) -> Cube:
"""Calculate the probability of freezing rain from the probabilities
of rain and sleet rates or accumulations, and the provided probabilities
of temperature being below the freezing point of water.
(probability of rain + probability of sleet) x (probability T < 0C)
Returns:
Cube of freezing rain probabilities.
"""
freezing_rain_prob = (self.rain.data +
self.sleet.data) * self.temperature.data
diagnostic_name = self.sleet.name().replace("sleet", "freezing_rain")
threshold_name = (self.sleet.coord(
var_name="threshold").name().replace("sleet", "freezing_rain"))
mandatory_attributes = generate_mandatory_attributes(
CubeList([self.rain, self.sleet]))
optional_attributes = {}
if self.model_id_attr:
# Rain and sleet will always be derived from the same model, but temperature
# may be diagnosed from a different model when creating a nowcast forecast.
# The output in such a case is fundamentally a nowcast product, so we exclude
# the temperature diagnostic when determining the model_id_attr.
optional_attributes = update_model_id_attr_attribute(
CubeList([self.rain, self.sleet]), self.model_id_attr)
freezing_rain_cube = create_new_diagnostic_cube(
diagnostic_name,
"1",
template_cube=self.sleet,
mandatory_attributes=mandatory_attributes,
optional_attributes=optional_attributes,
data=freezing_rain_prob,
)
freezing_rain_cube.coord(var_name="threshold").rename(threshold_name)
freezing_rain_cube.coord(threshold_name).var_name = "threshold"
return freezing_rain_cube