# (C) Crown Copyright, Met Office. All rights reserved.
#
# This file is part of 'IMPROVER' and is released under the BSD 3-Clause license.
# See LICENSE in the root of the repository for full licensing details.
"""Module to contain CloudCondensationLevel plugin."""
from typing import Tuple, Union
import numpy as np
from iris import Constraint
from iris.cube import Cube, CubeList
from iris.exceptions import ConstraintMismatchError, CoordinateNotFoundError
from scipy.optimize import newton
from improver import PostProcessingPlugin
from improver.metadata.utilities import (
create_new_diagnostic_cube,
generate_mandatory_attributes,
)
from improver.psychrometric_calculations.psychrometric_calculations import (
HumidityMixingRatio,
dry_adiabatic_temperature,
saturated_humidity,
)
from improver.utilities.common_input_handle import as_cubelist
[docs]
class CloudCondensationLevel(PostProcessingPlugin):
"""
Derives the temperature and pressure of the convective cloud condensation
level from near-surface values of temperature, pressure and humidity mixing
ratio.
"""
[docs]
def __init__(self, model_id_attr: str = None):
"""
Set up class
Args:
model_id_attr:
Name of model ID attribute to be copied from source cubes to output cube
"""
self.model_id_attr = model_id_attr
self.temperature, self.pressure, self.humidity = None, None, None
[docs]
def _make_ccl_cube(self, data: np.ndarray, is_temperature: bool) -> Cube:
"""Puts the data array into a CF-compliant cube"""
attributes = {}
if self.model_id_attr:
attributes[self.model_id_attr] = self.temperature.attributes[
self.model_id_attr
]
if is_temperature:
name = "air_temperature_at_condensation_level"
units = "K"
else:
name = "air_pressure_at_condensation_level"
units = "Pa"
cube = create_new_diagnostic_cube(
name,
units,
self.temperature,
mandatory_attributes=generate_mandatory_attributes(
[self.temperature, self.pressure, self.humidity]
),
optional_attributes=attributes,
data=data,
)
# The template cube may have had a height coord describing it as screen-level.
# This needs removing:
try:
cube.remove_coord("height")
except CoordinateNotFoundError:
pass
return cube
[docs]
def _iterate_to_ccl(self) -> Tuple[np.ndarray, np.ndarray]:
"""Uses a Newton iterator to find the pressure level where the
adiabatically-adjusted temperature equals the saturation temperature.
Returns pressure and temperature arrays."""
def humidity_delta(p2, p, t, q):
"""For a target pressure guess, p2, and origin p, t and q, return the
difference between q and q_sat(t2, p2)"""
t2 = dry_adiabatic_temperature(t, p, p2)
return q - saturated_humidity(t2, p2)
ccl_pressure = newton(
humidity_delta,
self.pressure.data.copy(),
args=(self.pressure.data, self.temperature.data, self.humidity.data),
tol=100,
maxiter=20,
disp=False,
).astype(np.float32)
ccl_temperature = dry_adiabatic_temperature(
self.temperature.data, self.pressure.data, ccl_pressure
).astype(np.float32)
return ccl_pressure, ccl_temperature
[docs]
def process(self, *cubes: Union[Cube, CubeList]) -> CubeList[Cube, Cube]:
"""
Calculates the cloud condensation level from the near-surface inputs.
Values will be limited to the surface values where the calculated pressure is greater than
the original pressure, which can occur in super-saturated conditions.
Args:
cubes:
Cubes of temperature (K), pressure (Pa)
and humidity mixing ratio (kg kg-1)
Returns:
Cubes of air_temperature_at_cloud_condensation_level and
air_pressure_at_cloud_condensation_level
"""
cubes = as_cubelist(cubes)
names_to_extract = ("air_temperature", "air_pressure", "humidity_mixing_ratio")
extracted_cubes = []
try:
for name in names_to_extract:
name_in_cube = lambda cube: True if name in cube.name() else False
extracted_cubes.append(
cubes.extract_cube(Constraint(cube_func=name_in_cube))
)
except ConstraintMismatchError as err:
raise ValueError(f"No cube with name {name} found") from err
self.temperature, self.pressure, self.humidity = extracted_cubes
ccl_pressure, ccl_temperature = self._iterate_to_ccl()
# Limit values so they are no greater than the original pressure.
# This occurs in super-saturated conditions.
mask = ccl_pressure > self.pressure.data
ccl_pressure = np.where(mask, self.pressure.data, ccl_pressure)
ccl_temperature = np.where(mask, self.temperature.data, ccl_temperature)
return CubeList(
[
self._make_ccl_cube(ccl_temperature, is_temperature=True),
self._make_ccl_cube(ccl_pressure, is_temperature=False),
]
)
