def mid_latitude_pressure_winter(self, h):
"""Section 3.2 of Recommendation ITU-R P.835-6
"""
P10 = standard_pressure(10).to(u.hPa).value
P72 = standard_pressure(72).to(u.hPa).value
return np.where(np.logical_and((0 <= h), (h <= 10)),
1018.8627 - 124.2954 * h + 4.8307 * h**2,
np.where(np.logical_and((10 < h), (h <= 72)),
P10 * np.exp(-0.147 * (h - 10.)),
np.where(np.logical_and((72 < h), (h <= 100)),
P72 * np.exp(-0.155 * (h - 72.)), np.nan)))
def high_latitude_pressure_summer(self, h):
"""Section 4.1 of Recommendation ITU-R P.835-5
"""
P10 = standard_pressure([10]).to(u.hPa).value
P72 = standard_pressure([72]).to(u.hPa).value
return np.where(np.logical_and((0 <= h), (h <= 10)),
1008.0278 - 113.2494 * h + 3.9408 * h**2,
np.where(np.logical_and((10 < h), (h <= 72)),
P10 * np.exp(-0.140 * (h - 10.)),
np.where(np.logical_and((72 < h), (h <= 100)),
P72 * np.exp(-0.165 * (h - 72.)), np.nan)))
def high_latitude_pressure_winter(self, h):
"""Section 4.2 of Recommendation ITU-R P.835-5
"""
P10 = standard_pressure([10]).to(u.hPa).value
P72 = standard_pressure([72]).to(u.hPa).value
return np.where(np.logical_and((0 <= h), (h <= 10)),
1010.8828 - 122.2411 * h + 4.554 * h**2,
np.where(np.logical_and((10 < h), (h <= 72)),
P10 * np.exp(-0.147 * (h - 10.)),
np.where(np.logical_and((72 < h), (h <= 100)),
P72 * np.exp(-0.150 * (h - 72.)), np.nan)))
def low_latitude_pressure(self, h):
"""Section 2 of Recommendation ITU-R P.835-5
"""
P10 = standard_pressure([10]).to(u.hPa).value
P72 = standard_pressure([72]).to(u.hPa).value
return np.where(np.logical_and((0 <= h), (h <= 10)),
1012.0306 - 109.0338 * h + 3.6316 * h**2,
np.where(np.logical_and((10 < h), (h <= 72)),
P10 * np.exp(-0.147 * (h - 10.)),
np.where(np.logical_and((72 < h), (h <= 100)),
P72 * np.exp(-0.165 * (h - 72.)), np.nan)))
def mid_latitude_pressure_summer(self, h):
"""Section 3.1 of Recommendation ITU-R P.835-5
"""
P10 = standard_pressure([10]).to(u.hPa).value
P72 = standard_pressure([72]).to(u.hPa).value
return np.where(np.logical_and((0 <= h), (h <= 10)),
1012.8186 - 111.5569 * h + 3.8646 * h**2,
np.where(np.logical_and((10 < h), (h <= 72)),
P10 * np.exp(-0.147 * (h - 10.)),
np.where(np.logical_and((72 < h), (h <= 100)),
P72 * np.exp(-0.165 * (h - 72.)),
np.nan)))
def atmospheric_attenuation_slant_path(lat,
lon,
f,
el,
p,
D,
tau,
eta,
hs=None,
rho=None,
R001=None,
T=None,
H=None,
P=None,
hL=1e3,
V_t=None,
mode='approx',
return_contributions=False,
include_rain=True,
include_gas=True,
include_scintillation=True,
include_clouds=True):
""" Calculation of long-term atmospheric attenuation statistics.
The following procedure provides estimates of the long-term statistics of
the slant-path atmospheric attenuation at a given location for
frequencies up to 55 GHz and percentages of time 0.001 % < p < 50 %.
Parameters
-------------
- lat : number, sequence, or numpy.ndarray
Latitudes of the receiver points
- lon : number, sequence, or numpy.ndarray
Longitudes of the receiver points
- f : number or Quantity
Frequency (GHz)
- el : sequence, number or Quantity
Elevation angle (degrees)
- p : number
Percetage of the time the rain attenuation value is exceeded.
- D: number or Quantity
Physical diameter of the earth-station antenna (m)
- tau : number
Polarization tilt angle relative to the horizontal (degrees)
(tau = 45 deg for circular polarization).
- eta: number
Antenna efficiency.
- hs : number, sequence, or numpy.ndarray, optional
Heigh above mean sea level of the earth station (km). If local data for
the earth station height above mean sea level is not available, an
estimate is obtained from the maps of topographic altitude
given in Recommendation ITU-R P.1511.
- rho : number or Quantity, optional
Water vapor density (g/m3). If not provided, an estimate is obtained
from Recommendation Recommendation ITU-R P.836.
- R001: number or Quantity, optional
Point rainfall rate for the location for 0.01% of an average year \
(mm/h). If not provided, an estimate is obtained from Recommendation
ITU-R P.837. Some useful values:
* 0.25 mm/h : Drizzle
* 2.5 mm/h : Light rain
* 12.5 mm/h : Medium rain
* 25.0 mm/h : Heavy rain
* 50.0 mm/h : Downpour
* 100 mm/h : Tropical
* 150 mm/h : Monsoon
- T: number, sequence, or numpy.ndarray, optional
Average surface ambient temperature (°C) at the site. If None, uses the
ITU-R P.453 to estimate the wet term of the radio refractivity.
- H: number, sequence, or numpy.ndarray, optional
Average surface relative humidity (%) at the site. If None, uses the
ITU-R P.453 to estimate the wet term of the radio refractivity.
- P: number, sequence, or numpy.ndarray, optional
Average surface pressure (hPa) at the site. If None, uses the
ITU-R P.453 to estimate the wet term of the radio refractivity.
- hL : number, optional
Height of the turbulent layer (m). Default value 1000 m
- V_t : number or Quantity, optional
Integrated water vapour content along the path (kg/m2 or mm).
If not provided this value is estimated using Recommendation
ITU-R P.836. Default value None
- mode : string, optional
Mode for the calculation of gaseous attenuation. Valid values are
'approx', 'exact'. If 'approx' Uses the method in Annex 2 of
Recommendation ITU-R P.676, else uses the method described in
Section 1. Default, 'approx'
- return_contributions: bool, optional
Determines whether individual contributions from gases, rain, clouds
and scintillation are returned in addition ot the total attenuation
(True), or just the total atmospheric attenuation (False).
Default is False
- include_rain: bool, optional
Determines whether to include the rain contribution in the total
atmospheric attenuation calculation or not. Default is True
- include_gas: bool, optional
Determines whether to include the gaseous contribution in the total
atmospheric attenuation calculation or not. Default is True
- include_scintillation: bool, optional
Determines whether to include the scintillation contribution in the
total atmospheric attenuation calculation or not. Default is True
- include_clouds: bool, optional
Determines whether to include the clouds contribution in the total
atmospheric attenuation calculation or not. Default is True
Returns
---------
- A : Quantity
Total atmospheric attenuation (dB)
- Ag, Ac, Ar, As, A : tuple
Gaseous, Cloud, Rain, Scintillation contributions to total attenuation,
and total attenuation (dB)
References
-------------
[1] Propagation data and prediction methods required for the design of
Earth-space telecommunication systems:
https://www.p.int/dms_pubrec/itu-r/rec/p/R-REC-P.618-12-201507-I!!PDF-E.pdf
"""
if np.logical_or(p < 0.001, p > 50).any():
warnings.warn(
RuntimeWarning(
'The method to compute the total '
'atmospheric attenuation in recommendation ITU-P 618-13 '
'is only recommended for unavailabilities (p) between '
'0.001 % and 50 %'))
# This takes account of the fact that a large part of the cloud attenuation
# and gaseous attenuation is already included in the rain attenuation
# prediction for time percentages below 1%. Eq. 61 and Eq. 62 in
# Recommendation ITU 618-13
p_c_g = np.maximum(1, p)
# Estimate the ground station altitude
if hs is None:
hs = topographic_altitude(lat, lon)
# Surface mean temperature
if T is None:
T = surface_mean_temperature(lat, lon)
# Estimate the surface Pressure
if P is None:
P = standard_pressure(hs)
# Estimate the surface Pressure
if V_t is None:
V_t = total_water_vapour_content(lat, lon, p_c_g, alt=hs)
# Estimate the surface water vapour density
if rho is None:
rho = surface_water_vapour_density(lat, lon, p_c_g, alt=hs)
# Compute the attenuation components
if include_rain:
Ar = rain_attenuation(lat, lon, f, el, p, tau, hs=hs, R001=R001)
else:
Ar = 0 * u.dB
if include_gas:
Ag = gaseous_attenuation_slant_path(f,
el,
rho,
P,
T,
V_t=V_t,
h=hs,
mode=mode)
else:
Ag = 0 * u.dB
if include_clouds:
Ac = cloud_attenuation(lat, lon, el, f, p_c_g)
else:
Ac = 0 * u.dB
if include_scintillation:
As = scintillation_attenuation(lat,
lon,
f,
el,
p,
D,
eta,
T=T,
H=H,
P=P,
hL=hL)
else:
As = 0 * u.dB
# Compute the total attenuation according to
A = Ag + np.sqrt((Ar + Ac)**2 + As**2)
if return_contributions:
return Ag, Ac, Ar, As, A
else:
return A
def total_attenuation_synthesis(self,
lat,
lon,
f,
el,
p,
D,
Ns,
tau,
eta,
Ts=1,
hs=None,
rho=None,
H=None,
P=None,
hL=1000,
return_contributions=False):
t_disc = int(5e6)
# Step A Correlation coefficients:
C_RC = 1
C_CV = 0.8
# Step B Scintillation polynomials
def a_Fade(p):
return -0.061 * np.log10(p)**3 + 0.072 * \
np.log10(p)**2 - 1.71 * np.log10(p) + 3
def a_Enhanc(p):
return -0.0597 * np.log10(p)**3 - 0.0835 * \
np.log10(p)**2 - 1.258 * np.log10(p) + 2.672
# Step C1-C3:
n_R = np.random.normal(0, 1, int((Ns * Ts + t_disc)))
n_L0 = np.random.normal(0, 1, int((Ns * Ts + t_disc)))
n_V0 = np.random.normal(0, 1, int((Ns * Ts + t_disc)))
# Step C4-C5:
n_L = C_RC * n_R + np.sqrt(1 - C_RC**2) * n_L0
n_V = C_CV * n_L + np.sqrt(1 - C_CV**2) * n_V0
# Step C6: Compute the rain attenuation time series
if hs is None:
hs = topographic_altitude(lat, lon)
Ar = rain_attenuation_synthesis(lat,
lon,
f,
el,
tau,
Ns,
hs=hs,
Ts=1,
n=n_R).value
Ar = Ar[t_disc:]
# Step C7: Compute the cloud integrated liquid water content time
# series
L = cloud_liquid_water_synthesis(lat, lon, Ns, Ts=1, n=n_L).value
L = L[t_disc:]
Ac = L * \
specific_attenuation_coefficients(f, T=0) / np.sin(np.deg2rad(el))
Ac = Ac.flatten()
# Step C9: Identify time stamps where A_R > 0 L > 1
idx = np.where(np.logical_and(Ar > 0, L > 1))[0]
idx_no = np.where(np.logical_not(np.logical_and(Ar > 0, L > 1)))[0]
# Step C10: Discard the previous values of Ac and re-compute them by
# linear interpolation vs. time starting from the non-discarded cloud
# attenuations values
Ac[idx] = np.interp(idx, idx_no, Ac[idx_no])
# Step C11: Compute the integrated water vapour content time series
V = integrated_water_vapour_synthesis(lat, lon, Ns, Ts=1, n=n_V).value
V = V[t_disc:]
# Step C12: Convert the integrated water vapour content time series
# V into water vapour attenuation time series AV(kTs)
Av = zenith_water_vapour_attenuation(lat, lon, p, f, V_t=V).value
# Step C13: Compute the mean annual temperature Tm for the location of
# interest using experimental values if available.
Tm = surface_mean_temperature(lat, lon).value
# Step C14: Convert the mean annual temperature Tm into mean annual
# oxygen attenuation AO following the method recommended in
# Recommendation ITU-R P.676.
if P is None:
P = standard_pressure(hs).value
if rho is None:
rho = standard_water_vapour_density(hs).value
e = Tm * rho / 216.7
go = gamma0_exact(f, P, rho, Tm).value
ho, hw = slant_inclined_path_equivalent_height(f, P).value
Ao = ho * go * np.ones_like(Ar)
# Step C15: Synthesize unit variance scintillation time series
sci_0 = scintillation_attenuation_synthesis(Ns, Ts=1).value
# Step C16: Compute the correction coefficient time series Cx(kTs) in
# order to distinguish between scintillation fades and enhancements:
Q_sci = 100 * stats.norm.sf(sci_0)
C_x = np.where(sci_0 > 0, a_Fade(Q_sci) / a_Enhanc(Q_sci), 1)
# Step C17: Transform the integrated water vapour content time series
# V(kTs) into the Gamma distributed time series Z(kTs) as follows:
kappa, lambd = integrated_water_vapour_coefficients(lat, lon, None)
Z = stats.gamma.ppf(np.exp(-(V / lambd)**kappa), 10, scale=0.1)
# Step C18: Compute the scintillation standard deviation σ following
# the method recommended in Recommendation ITU-R P.618.
sigma = scintillation_attenuation_sigma(lat, lon, f, el, D, eta, Tm, H,
P, hL).value
# Step C19: Compute the scintillation time series sci:
As = np.where(Ar > 1, sigma * sci_0 * C_x * Z * Ar**(5 / 12),
sigma * sci_0 * C_x * Z)
# Step C20: Compute total tropospheric attenuation time series A(kTs)
# as follows:
A = Ar + Ac + Av + Ao + As
if return_contributions:
return (Ao + Av)[::Ts], Ac[::Ts], Ar[::Ts], As[::Ts], A[::Ts]
else:
return A[::Ts]
def test_gaseous_attenuation(self):
# Define atmospheric parameters
rho_wet = 7.5 * u.g / u.m**3
rho_dry = 0 * u.g / u.m**3
P = 1013.25 * u.hPa
T = 15 * u.deg_C
# Define frequency logspace parameters
N_freq = 1000
fs = np.linspace(0, 1000, N_freq)
# Compute the attenuation values
att_wet = gammaw_exact(fs, P, rho_wet, T)
att_dry = gamma0_exact(fs, P, rho_dry, T)
# Plot the results
plt.figure()
plt.plot(fs, att_wet.value, 'b--', label='Wet atmosphere')
plt.plot(fs, att_dry.value, 'r', label='Dry atmosphere')
plt.xlabel('Frequency [GHz]')
plt.ylabel('Specific attenuation [dB/km]')
plt.yscale('log')
plt.xscale('linear')
plt.xlim(0, 1000)
plt.ylim(1e-3, 1e5)
plt.legend()
plt.grid(which='both',
linestyle=':',
color='gray',
linewidth=0.3,
alpha=0.5)
plt.grid(which='major', linestyle=':', color='black')
plt.title('FIGURE 1. - Specific attenuation due to atmospheric gases,'
'\ncalculated at 1 GHz intervals, including line centres')
plt.tight_layout()
#######################################################################
# Specific attenuation at different altitudes #
#######################################################################
# Define atmospheric parameters
hs = np.array([0, 5, 10, 15, 20]) * u.km
# Define frequency logspace parameters
N_freq = 2001
fs = np.linspace(50, 70, N_freq)
# Plot the results
plt.figure()
# Loop over heights and compute values
for h in hs:
rho = standard_water_vapour_density(h)
P = standard_pressure(h)
T = standard_temperature(h)
atts_dry = gamma0_exact(fs * u.GHz, P, rho, T).value
atts_wet = gammaw_exact(fs * u.GHz, P, rho, T).value
atts = atts_dry + atts_wet
plt.plot(fs, atts, label='Altitude {0} km'.format(h.value))
plt.xlabel('Frequency [GHz]')
plt.ylabel('Specific attenuation [dB/km]')
plt.yscale('log')
plt.xscale('linear')
plt.xlim(50, 70)
plt.ylim(1e-3, 1e2)
plt.legend()
plt.grid(which='both',
linestyle=':',
color='gray',
linewidth=0.3,
alpha=0.5)
plt.grid(which='major', linestyle=':', color='black')
plt.title('FIGURE 2. - Specific attenuation in the range 50-70 GHz'
' at the\n altitudes indicated, calculated at intervals of'
' 10 MHz\nincluding line centers (0, 5, 10 15, 20) km')
plt.tight_layout()
#######################################################################
# Comparison of line-by-line and approximate method #
#######################################################################
# Define atmospheric parameters
el = 90
rho = 7.5 * u.g / u.m**3
P = 1013.25 * u.hPa
T = 15 * u.deg_C
e = rho.value * T.value / 216.7 # Water vapour partial pressure
p = P.value - e # Dry Air Pressure
# Define frequency logspace parameters
N_freq = 350
fs = np.linspace(1, 350, N_freq) # GHz
# Initialize result vectors
atts_approx = []
atts_exact = []
# Loop over frequencies and compute values
atts_approx = gaseous_attenuation_slant_path(fs,
el,
rho,
p,
T,
mode='approx')
atts_exact = gaseous_attenuation_slant_path(fs,
el,
rho,
p,
T,
h=0,
mode='exact')
# Plot the results
plt.figure()
plt.plot(fs,
atts_approx.value,
'b--',
label='Approximate method Annex 2')
plt.plot(fs, atts_exact.value, 'r', label='Exact line-by-line method')
plt.xlabel('Frequency [GHz]')
plt.ylabel('Attenuation [dB]')
plt.yscale('log')
plt.xscale('log')
plt.legend()
plt.grid(which='both',
linestyle=':',
color='gray',
linewidth=0.3,
alpha=0.5)
plt.grid(which='major', linestyle=':', color='black')
plt.title('Comparison of line-by-line method to approximate method')
plt.tight_layout()
