# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
from astropy import units as u
from itur.models.itu1144 import bilinear_2D_interpolator
from itur.utils import prepare_input_array, prepare_quantity, load_data,\
prepare_output_array, dataset_dir
class __ITU453():
""" Implementation of the methods in Recommendation ITU-R P.453
"The radio refractive index: its formula and refractivity data"
Available versions:
* P.453-13 (12/17)
* P.453-12 (07/15)
TODO: Implement version P.453-13
Recommendation ITU-R P.453 provides methods to estimate the radio
refractive index and its behaviour for locations worldwide; describes both
surface and vertical profile characteristics; and provides global maps for
the distribution of refractivity parameters and their statistical
variation.
"""
# This is an abstract class that contains an instance to a version of the
# ITU-R P.453 recommendation.
def __init__(self, version=13):
if version == 13:
self.instance = _ITU453_13()
elif version == 12:
self.instance = _ITU453_12()
else:
raise ValueError(
'Version {0} is not implemented for the ITU-R P.453 model.'
.format(version))
@property
def __version__(self):
return self.instance.__version__
def wet_term_radio_refractivity(self, e, T):
return self.instance.wet_term_radio_refractivity(e, T)
def dry_term_radio_refractivity(self, Pd, T):
return self.instance.dry_term_radio_refractivity(Pd, T)
def radio_refractive_index(self, P, e, T):
return self.instance.radio_refractive_index(P, e, T)
def water_vapour_pressure(self, T, P, H, type_hydrometeor='water'):
return self.instance.water_vapour_pressure(
T, P, H, type_hydrometeor=type_hydrometeor)
def saturation_vapour_pressure(self, T, P, type_hydrometeor='water'):
return self.instance.saturation_vapour_pressure(
T, P, type_hydrometeor=type_hydrometeor)
def map_wet_term_radio_refractivity(self, lat, lon, p=50):
fcn = np.vectorize(self.instance.map_wet_term_radio_refractivity,
excluded=[0, 1], otypes=[np.ndarray])
return np.array(fcn(lat, lon, p).tolist())
def DN65(self, lat, lon, p):
fcn = np.vectorize(self.instance.DN65, excluded=[0, 1],
otypes=[np.ndarray])
return np.array(fcn(lat, lon, p).tolist())
def DN1(self, lat, lon, p):
fcn = np.vectorize(self.instance.DN1, excluded=[0, 1],
otypes=[np.ndarray])
return np.array(fcn(lat, lon, p).tolist())
class _ITU453_13():
def __init__(self):
self.__version__ = 13
self.year = 2017
self.month = 12
self.link = 'https://www.itu.int/rec/R-REC-P.453-13-201712-I/en'
self._N_wet = {}
self._DN65 = {}
self._DN1 = {}
def DN65(self, lat, lon, p):
if not self._DN65:
ps = [0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,
90, 95, 98, 99, 99.5, 99.8, 99.9]
d_dir = os.path.join(dataset_dir, '453/v12_DN65m_%02dd%02d_v1.txt')
lats = load_data(os.path.join(dataset_dir, '453/v12_lat0d75.txt'))
lons = load_data(os.path.join(dataset_dir, '453/v12_lon0d75.txt'))
for p_loads in ps:
int_p = p_loads // 1
frac_p = round((p_loads % 1.0) * 100)
vals = load_data(d_dir % (int_p, frac_p))
self._DN65[float(p_loads)] = bilinear_2D_interpolator(
lats, lons, vals)
return self._DN65[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def DN1(self, lat, lon, p):
if not self._DN1:
ps = [0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,
90, 95, 98, 99, 99.5, 99.8, 99.9]
d_dir = os.path.join(dataset_dir, '453/v12_DN_%02dd%02d_v1.txt')
lats = load_data(os.path.join(dataset_dir, '453/v12_lat0d75.txt'))
lons = load_data(os.path.join(dataset_dir, '453/v12_lon0d75.txt'))
for p_loads in ps:
int_p = p_loads // 1
frac_p = round((p_loads % 1.0) * 100)
vals = load_data(d_dir % (int_p, frac_p))
self._DN1[float(p_loads)] = bilinear_2D_interpolator(
lats, lons, vals)
return self._DN1[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def N_wet(self, lat, lon, p):
if not self._N_wet:
ps = [0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 30, 50, 60, 70, 80,
90, 95, 99]
d_dir = os.path.join(dataset_dir, '453/v13_NWET_Annual_%s.txt')
lats = load_data(os.path.join(dataset_dir, '453/v13_LAT_N.txt'))
lons = load_data(os.path.join(dataset_dir, '453/v13_LON_N.txt'))
for p_loads in ps:
vals = load_data(d_dir % (str(p_loads).replace('.', '')))
self._N_wet[float(p_loads)] = bilinear_2D_interpolator(
np.flipud(lats), lons, np.flipud(vals))
lon[lon > 180] = lon[lon > 180] - 360
return self._N_wet[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
@classmethod
def wet_term_radio_refractivity(self, e, T):
N_wet = (72 * e / (T + 273.15) + 3.75e5 * e / (T + 273.15)**2) * 1e-6
return N_wet
@classmethod
def dry_term_radio_refractivity(self, Pd, T):
N_dry = 77.6 * Pd / T # Eq. 3
return N_dry
@classmethod
def radio_refractive_index(self, P, e, T):
N = 77.6 * P / T - 5.6 * e / T + 3.75e5 * e / T**2 # Eq. 6 [N-units]
n = 1 + N * 1e-6 # Eq. 1
return n
@classmethod
def water_vapour_pressure(self, T, P, H, type_hydrometeor='water'):
e_s = self.saturation_vapour_pressure(T, P, type_hydrometeor)
return H * e_s / 100 # Eq. 8
@classmethod
def saturation_vapour_pressure(self, T, P, type_hydrometeor='water'):
if type_hydrometeor == 'water':
EF = 1 + 1e-4 * (7.2 + P * (0.00320 + 5.9e-6 * T**2))
a = 6.1121
b = 18.678
c = 257.14
d = 234.5
elif type_hydrometeor == 'ice':
EF = 1 + 1e-4 * (2.2 + P * (0.00382 + 6.4e-7 * T**2))
a = 6.11215
b = 23.036
c = 279.82
d = 333.7
e_s = EF * a * np.exp((b - T / d) * T / (T + c))
return e_s
def map_wet_term_radio_refractivity(self, lat, lon, p):
"""
"""
# Fix lon because the data-set is now indexed -180 to 180 instead
# of 0 to 360
lon[lon > 180] = lon[lon > 180] - 360
lat_f = lat.flatten()
lon_f = lon.flatten()
available_p = np.array([0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10,
20, 30, 50, 60, 70, 80, 90, 95, 99])
if p in available_p:
p_below = p_above = p
pExact = True
else:
pExact = False
idx = available_p.searchsorted(p, side='right') - 1
idx = np.clip(idx, 0, len(available_p) - 1)
p_below = available_p[idx]
idx = np.clip(idx + 1, 0, len(available_p) - 1)
p_above = available_p[idx]
R = -(lat_f - 90) // 0.75
C = (lon_f + 180) // 0.75
lats = np.array([90 - R * 0.75, 90 - (R + 1) * 0.75,
90 - R * 0.75, 90 - (R + 1) * 0.75])
lons = np.array([C * 0.75, C * 0.75,
(C + 1) * 0.75, (C + 1) * 0.75]) - 180
r = - (lat_f - 90) / 0.75
c = (lon_f + 180) / 0.75
N_wet_a = self.N_wet(lats, lons, p_above)
N_wet_a = (N_wet_a[0, :] * ((R + 1 - r) * (C + 1 - c)) +
N_wet_a[1, :] * ((r - R) * (C + 1 - c)) +
N_wet_a[2, :] * ((R + 1 - r) * (c - C)) +
N_wet_a[3, :] * ((r - R) * (c - C)))
if not pExact:
N_wet_b = self.N_wet(lats, lons, p_below)
N_wet_b = (N_wet_b[0, :] * ((R + 1 - r) * (C + 1 - c)) +
N_wet_b[1, :] * ((r - R) * (C + 1 - c)) +
N_wet_b[2, :] * ((R + 1 - r) * (c - C)) +
N_wet_b[3, :] * ((r - R) * (c - C)))
# Compute the values of Lred_a
if not pExact:
rho = N_wet_b + (N_wet_a - N_wet_b) * \
(np.log(p) - np.log(p_below)) / \
(np.log(p_above) - np.log(p_below))
return rho.reshape(lat.shape)
else:
return N_wet_a.reshape(lat.shape)
class _ITU453_12():
def __init__(self):
self.__version__ = 12
self.year = 2016
self.month = 9
self.link = 'https://www.itu.int/rec/R-REC-P.453-12-201609-I/en'
self._N_wet = {}
self._DN65 = {}
self._DN1 = {}
def DN65(self, lat, lon, p):
if not self._DN65:
ps = [0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,
90, 95, 98, 99, 99.5, 99.8, 99.9]
d_dir = os.path.join(dataset_dir, '453/v12_DN65m_%02dd%02d_v1.txt')
lats = load_data(os.path.join(dataset_dir, '453/v12_lat0d75.txt'))
lons = load_data(os.path.join(dataset_dir, '453/v12_lon0d75.txt'))
for p_loads in ps:
int_p = p_loads // 1
frac_p = round((p_loads % 1.0) * 100)
vals = load_data(d_dir % (int_p, frac_p))
self._DN65[float(p_loads)] = bilinear_2D_interpolator(
lats, lons, vals)
return self._DN65[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def DN1(self, lat, lon, p):
if not self._DN1:
ps = [0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,
90, 95, 98, 99, 99.5, 99.8, 99.9]
d_dir = os.path.join(dataset_dir, '453/v12_DN_%02dd%02d_v1.txt')
lats = load_data(os.path.join(dataset_dir, '453/v12_lat0d75.txt'))
lons = load_data(os.path.join(dataset_dir, '453/v12_lon0d75.txt'))
for p_loads in ps:
int_p = p_loads // 1
frac_p = round((p_loads % 1.0) * 100)
vals = load_data(d_dir % (int_p, frac_p))
self._DN1[float(p_loads)] = bilinear_2D_interpolator(
lats, lons, vals)
return self._DN1[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def N_wet(self, lat, lon):
if not self._N_wet:
vals = load_data(os.path.join(dataset_dir, '453/v12_ESANWET.txt'))
lats = load_data(os.path.join(dataset_dir, '453/v12_ESALAT.txt'))
lons = load_data(os.path.join(dataset_dir, '453/v12_ESALON.txt'))
self._N_wet = bilinear_2D_interpolator(lats, lons, vals)
return self._N_wet(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def wet_term_radio_refractivity(self, e, T):
return _ITU453_13.wet_term_radio_refractivity(e, T)
def dry_term_radio_refractivity(self, Pd, T):
return _ITU453_13.dry_term_radio_refractivity(Pd, T)
def radio_refractive_index(self, P, e, T):
return _ITU453_13.radio_refractive_index(P, e, T)
def water_vapour_pressure(self, T, P, H, type_hydrometeor='water'):
return _ITU453_13.water_vapour_pressure(T, P, H, type_hydrometeor)
def saturation_vapour_pressure(self, T, P, type_hydrometeor='water'):
return _ITU453_13.saturation_vapour_pressure(T, P, type_hydrometeor)
def map_wet_term_radio_refractivity(self, lat, lon, p):
return self.N_wet(lat, lon)
__model = __ITU453()
def change_version(new_version):
"""
Change the version of the ITU-R P.453 recommendation currently being used.
Parameters
----------
new_version : int
Number of the version to use.
Valid values are:
* P.453-12 (02/12) (Current version)
"""
global __model
__model = __ITU453(new_version)
def get_version():
"""
Obtain the version of the ITU-R P.453 recommendation currently being used.
"""
global __model
return __model.__version__
def wet_term_radio_refractivity(e, T):
"""
Method to determine the wet term of the radio refractivity
Parameters
----------
e : number or Quantity
Water vapour pressure (hPa)
T : number or Quantity
Absolute temperature (K)
Returns
-------
N_wet: Quantity
Wet term of the radio refractivity (-)
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
e = prepare_quantity(e, u.hPa, 'Water vapour pressure ')
T = prepare_quantity(T, u.K, 'Absolute temperature')
val = __model.wet_term_radio_refractivity(e, T)
return val * u.dimensionless_unscaled
def dry_term_radio_refractivity(Pd, T):
"""
Method to determine the dry term of the radio refractivity
Parameters
----------
Pd : number or Quantity
Dry atmospheric pressure (hPa)
T : number or Quantity
Absolute temperature (K)
Returns
-------
N_dry: Quantity
Dry term of the radio refractivity (-)
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
Pd = prepare_quantity(Pd, u.hPa, 'Dry atmospheric pressure')
T = prepare_quantity(T, u.K, 'Absolute temperature')
val = __model.dry_term_radio_refractivity(Pd, T)
return val * u.dimensionless_unscaled
def radio_refractive_index(P, e, T):
"""
Method to compute the radio refractive index
Parameters
----------
P : number or Quantity
Total atmospheric pressure (hPa)
e : number or Quantity
Water vapour pressure (hPa)
T : number or Quantity
Absolute temperature (K)
Returns
-------
n: Quantity
Radio refractive index (-)
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
P = prepare_quantity(P, u.hPa, 'Total atmospheric pressure')
e = prepare_quantity(e, u.hPa, 'Water vapour pressure ')
T = prepare_quantity(T, u.K, 'Absolute temperature')
val = __model.radio_refractive_index(P, e, T)
return val * u.dimensionless_unscaled
def water_vapour_pressure(T, P, H, type_hydrometeor='water'):
"""
Method to determine the water vapour pressure
Parameters
----------
T : number or Quantity
Absolute temperature (C)
P : number or Quantity
Total atmospheric pressure (hPa)
H : number or Quantity
Relative humidity (%)
type_hydrometeor : string
Type of hydrometeor. Valid strings are 'water' and 'ice'
Returns
-------
e: Quantity
Water vapour pressure (hPa)
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
T = prepare_quantity(T, u.deg_C, 'Absolute temperature')
P = prepare_quantity(P, u.hPa, 'Total atmospheric pressure')
H = prepare_quantity(H, u.percent, 'Total atmospheric pressure')
val = __model.water_vapour_pressure(T, P, H, type_hydrometeor)
return val * u.hPa
def saturation_vapour_pressure(T, P, type_hydrometeor='water'):
"""
Method to determine the saturation water vapour pressure
Parameters
----------
T : number or Quantity
Absolute temperature (C)
P : number or Quantity
Total atmospheric pressure (hPa)
type_hydrometeor : string
Type of hydrometeor. Valid strings are 'water' and 'ice'
Returns
-------
e_s: Quantity
Saturation water vapour pressure (hPa)
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
T = prepare_quantity(T, u.deg_C, 'Absolute temperature')
P = prepare_quantity(P, u.hPa, 'Total atmospheric pressure')
val = __model.saturation_vapour_pressure(T, P, type_hydrometeor)
return val * u.hPa
def map_wet_term_radio_refractivity(lat, lon, p=50):
"""
Method to determine the wet term of the radio refractivity
Parameters
----------
lat : number, sequence, or numpy.ndarray
Latitudes of the receiver points
lon : number, sequence, or numpy.ndarray
Longitudes of the receiver points
Returns
-------
N_wet: Quantity
Wet term of the radio refractivity (-)
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
type_output = type(lat)
lat = prepare_input_array(lat)
lon = prepare_input_array(lon)
lon = np.mod(lon, 360)
val = __model.map_wet_term_radio_refractivity(lat, lon, p)
return prepare_output_array(val, type_output) * u.g / u.m**3
def DN65(lat, lon, p):
"""
Method to determine the statistics of the vertical gradient of radio
refractivity in the lowest 65 m from the surface of the Earth.
Parameters
----------
lat : number, sequence, or numpy.ndarray
Latitudes of the receiver points
lon : number, sequence, or numpy.ndarray
Longitudes of the receiver points
p : number
Percentage of time exceeded for p% of the average year
Returns
-------
DN65_p: Quantity
Vertical gradient of radio refractivity in the lowest 65 m from the
surface of the Earth exceeded for p% of the average year
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
type_output = type(lat)
lat = prepare_input_array(lat)
lon = prepare_input_array(lon)
lon = np.mod(lon, 360)
val = __model.DN65(lat, lon, p)
return prepare_output_array(val, type_output) * u.one
def DN1(lat, lon, p):
"""
Method to determine the statistics of the vertical gradient of radio
refractivity over a 1 km layer from the surface.
Parameters
----------
lat : number, sequence, or numpy.ndarray
Latitudes of the receiver points
lon : number, sequence, or numpy.ndarray
Longitudes of the receiver points
p : number
Percentage of time exceeded for p% of the average year
Returns
-------
DN1_p: Quantity
Vertical gradient of radio refractivity over a 1 km layer from the
surface exceeded for p% of the average year
References
----------
[1] The radio refractive index: its formula and refractivity data
https://www.itu.int/rec/R-REC-P.453/en
"""
global __model
type_output = type(lat)
lat = prepare_input_array(lat)
lon = prepare_input_array(lon)
lon = np.mod(lon, 360)
val = __model.DN1(lat, lon, p)
return prepare_output_array(val, type_output) * u.one
