def test_reduced_grid_a_priori():
tropopause_mask = TropopauseMask()
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
covariance = Diagonal(2.0, And(temperature_mask, tropopause_mask))
covariance_new = Diagonal(2.0 * np.ones(10))
data_provider = DataProvider()
t = data_provider.get_temperature()
a_priori = FixedAPriori("temperature", t, covariance)
a_priori = ReducedVerticalGrid(a_priori, np.logspace(3, 5, 10)[::-1])
data_provider.add(a_priori)
t_xa = data_provider.get_temperature_xa()
assert (t_xa.size == 10)
covmat = data_provider.get_temperature_covariance()
assert (covmat.shape == (10, 10))
data_provider = DataProvider()
a_priori = ReducedVerticalGrid(a_priori,
np.logspace(3, 5, 10)[::-1],
covariance=covariance_new)
data_provider.add(a_priori)
covmat = data_provider.get_temperature_covariance()
assert (np.all(covmat.diagonal() == 2.0))
def test_data_provider_a_priori():
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
tropopause_mask = TropopauseMask()
covariance = Diagonal(2.0, And(temperature_mask, tropopause_mask))
data_provider = DataProvider()
data_provider.add(DataProviderAPriori("temperature", covariance))
t = data_provider.get_temperature()
assert (np.all(data_provider.get_temperature_xa() == t))
def test_functional_a_priori():
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
tropopause_mask = TropopauseMask()
covariance = Diagonal(2.0, And(temperature_mask, tropopause_mask))
covariance_new = Diagonal(2.0 * np.ones(10))
data_provider = DataProvider()
t = data_provider.get_temperature()
f = lambda x: x**2
a_priori = FunctionalAPriori("temperature", "temperature", f, covariance)
data_provider.add(a_priori)
assert (np.all(np.isclose(t**2, data_provider.get_temperature_xa())))
def test_masks():
data_provider = DataProvider()
temperature = data_provider.get_temperature()
temperature_mask = TemperatureMask(lower_limit=230.0, upper_limit=280.0)
mask = temperature_mask(data_provider)
assert (np.all(temperature[mask] >= 230))
assert (np.all(temperature[mask] < 280))
tropopause_mask = TropopauseMask()
mask = tropopause_mask(data_provider)
assert (np.all(temperature[mask]) <= 225)
mask = And(temperature_mask, tropopause_mask)(data_provider)
assert (np.all(temperature[mask] >= 230))
assert (np.all(temperature[mask] < 280))
assert (np.all(temperature[mask]) <= 225)
"""
Common function used for LIRAS a priori.
"""
import numpy as np
import artssat
from artssat.jacobian import Atanh
from artssat.retrieval.a_priori import (And, TropopauseMask, TemperatureMask,
FreezingLevel, Dilate)
"""
Mask limiting retrieval of ice to between 280 K isotherm and the tropopause.
"""
ice_mask = Dilate(And(TropopauseMask(),
FreezingLevel(lower_inclusive=True, invert=False)),
1)
# TemperatureMask(0.0, 273.15, lower_inclusive = True))
"""
Mask limiting retrieval of rain to between surface and 264 K isotherm.
"""
rain_mask = Dilate(FreezingLevel(lower_inclusive = False, invert = True), 1)
#TemperatureMask(273.15, 340.0, upper_inclusive = True)
def n0_a_priori(t):
"""
Functional relation for of the a priori mean of :math:`N_0^*`
as described in Cazenave et al. 2019.
Args:
t: Array containing the temperature profile.