def N_wet(self, lat, lon):
if not self._N_wet:
vals = load_data(os.path.join(dataset_dir, 'p453/v12_ESANWET.txt'))
lats = load_data(os.path.join(dataset_dir, 'p453/v12_ESALAT.txt'))
lons = load_data(os.path.join(dataset_dir, 'p453/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 Pclw(self, lat, lon):
if not self._Pclw:
vals = load_data(os.path.join(dataset_dir, 'p840/v7_Pclw.txt'))
lats = load_data(os.path.join(dataset_dir, 'p840/v7_Lat.txt'))
lons = load_data(os.path.join(dataset_dir, 'p840/v7_Lon.txt'))
self._Pclw = bilinear_2D_interpolator(lats, lons, vals)
return self._Pclw(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def sigma(self, lat, lon):
if not self._sigma:
vals = load_data(
os.path.join(dataset_dir, 'p840/v4_WRED_LOGNORMAL_STDEV.txt'))
lats = load_data(os.path.join(dataset_dir, 'p840/v4_Lat.txt'))
lons = load_data(os.path.join(dataset_dir, 'p840/v4_Lon.txt'))
self._sigma = bilinear_2D_interpolator(lats, lons, vals)
return self._sigma(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def temperature(self, lat, lon):
if not self._temperature:
vals = load_data(os.path.join(dataset_dir, 'p1510/v0_Temp.txt'))
lats = load_data(os.path.join(dataset_dir, 'p1510/v0_Lat.txt'))
lons = load_data(os.path.join(dataset_dir, 'p1510/v0_Lon.txt'))
self._temperature = bicubic_2D_interpolator(np.flipud(lats), lons,
np.flipud(vals))
return self._temperature(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def isotherm_0(self, lat, lon):
if not self._zero_isotherm_data:
vals = load_data(
os.path.join(dataset_dir, 'p839/v3_ESA0HEIGHT.txt'))
lats = load_data(os.path.join(dataset_dir, 'p839/v3_ESALAT.txt'))
lons = load_data(os.path.join(dataset_dir, 'p839/v3_ESALON.txt'))
self._zero_isotherm_data = bilinear_2D_interpolator(
lats, lons, vals)
return self._zero_isotherm_data(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def topo_alt(self, lat, lon):
if self._topo_alt is None:
d_dir = os.path.join(dataset_dir, 'p836/v6_TOPO_0DOT5.txt')
lats = load_data(os.path.join(dataset_dir, 'p836/v6_TOPOLAT.txt'))
lons = load_data(os.path.join(dataset_dir, 'p836/v6_TOPOLON.txt'))
vals = load_data(d_dir)
self._topo_alt = bicubic_2D_interpolator(np.flipud(lats), lons,
np.flipud(vals))
return self._topo_alt(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def temperature(self, lat, lon):
if not self._temperature:
vals = load_data(os.path.join(dataset_dir, 'p1510/v1_T_Annual.h5'))
lats = load_data(os.path.join(dataset_dir, 'p1510/v1_Lat.h5'))
lons = load_data(os.path.join(dataset_dir, 'p1510/v1_Lon.h5'))
self._temperature = bilinear_2D_interpolator(
np.flipud(lats), lons, np.flipud(vals))
lon[lon > 180] = lon[lon > 180] - 360
return self._temperature(
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def altitude(self, lat, lon):
if not self._altitude:
vals = load_data(
os.path.join(dataset_dir, 'p1511/v2_TOPO_0DOT08.h5'))
lats = load_data(os.path.join(dataset_dir, 'p1511/v2_Lat.h5'))
lons = load_data(os.path.join(dataset_dir, 'p1511/v2_Lon.h5'))
self._altitude = bicubic_2D_interpolator(np.flipud(lats), lons,
np.flipud(vals))
return self._altitude(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def Beta(self, lat, lon):
if not self._Beta:
vals = load_data(
os.path.join(dataset_dir, 'p837/ESARAIN_BETA_v5.txt'))
lats = load_data(
os.path.join(dataset_dir, 'p837/ESARAIN_LAT_v5.txt'))
lons = load_data(
os.path.join(dataset_dir, 'p837/ESARAIN_LON_v5.txt'))
self._Beta = bilinear_2D_interpolator(lats, lons, vals)
return self._Beta(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def pressure(self, lat, lon):
if not self._pressure:
vals = load_data(os.path.join(dataset_dir, 'p1853/v2_P_Annual.h5'))
lats = load_data(os.path.join(dataset_dir, 'p1853/v2_Lat.h5'))
lons = load_data(os.path.join(dataset_dir, 'p1853/v2_Lon.h5'))
self._pressure = bilinear_2D_interpolator(np.flipud(lats), lons,
np.flipud(vals))
# In this recommendation the longitude is encoded with format -180 to
# 180 whereas we always use 0 - 360 encoding
lon[lon > 180] = lon[lon > 180] - 360
return self._pressure(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def VSCH(self, lat, lon, p):
if not self._VSCH:
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, 'p836/v4_VSCH_%s.txt')
lats = load_data(os.path.join(dataset_dir, 'p836/v4_Lat.txt'))
lons = load_data(os.path.join(dataset_dir, 'p836/v4_Lon.txt'))
for p_loads in ps:
vals = load_data(d_dir % (str(p_loads).replace('.', '')))
self._VSCH[float(p_loads)] =\
bilinear_2D_interpolator(lats, lons, vals)
return self._VSCH[float(p)](
np.array([lat.ravel(), lon.ravel()]).T).reshape(lat.shape)
def R001(self, lat, lon):
if not self._R001:
lats = load_data(os.path.join(dataset_dir, 'p837/v7_LAT_R001.h5'))
lons = load_data(os.path.join(dataset_dir, 'p837/v7_LON_R001.h5'))
vals = load_data(os.path.join(dataset_dir, 'p837/v7_R001.h5'))
self._R001 = bilinear_2D_interpolator(np.flipud(lats), lons,
np.flipud(vals))
# In this recommendation the longitude is encoded with format -180 to
# 180 whereas we always use 0 - 360 encoding
lon = np.array(lon)
lon[lon > 180] = lon[lon > 180] - 360
return self._R001(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def month_temperature(self, lat, lon, m):
if not self._month_temperature:
lats = load_data(os.path.join(dataset_dir, 'p1510/v1_Lat.h5'))
lons = load_data(os.path.join(dataset_dir, 'p1510/v1_Lon.h5'))
for _m in self.__months:
vals = load_data(
os.path.join(dataset_dir,
'p1510/v1_T_Month{0:02d}.h5').format(_m))
self._month_temperature[_m] = bilinear_2D_interpolator(
np.flipud(lats), lons, np.flipud(vals))
lon[lon > 180] = lon[lon > 180] - 360
return self._month_temperature[m](np.array([lat.ravel(),
lon.ravel()
]).T).reshape(lat.shape)
class _ITU676_11():
tmp = load_data(os.path.join(dataset_dir,
'p676//v11_lines_water_vapour.txt'),
skip_header=1)
f_wv = tmp[:, 0]
b1 = tmp[:, 1]
b2 = tmp[:, 2]
b3 = tmp[:, 3]
b4 = tmp[:, 4]
b5 = tmp[:, 5]
b6 = tmp[:, 6]
idx_approx = np.zeros_like(b1, dtype=bool).squeeze()
asterisk_rows = [0, 3, 4, 5, 7, 12, 20, 24, 34]
idx_approx[np.array(asterisk_rows)] = True
def __init__(self):
self.__version__ = 11
self.year = 2017
self.month = 12
self.link = 'https://www.itu.int/rec/R-REC-P.676-11-201712-S/en'
def gammaw_exact(self, f, P, rho, T):
return __gammaw_exact__676_9_11__(self, f, P, rho, T)
def s_a(self, lat, lon):
""" Standard deviation of terrain heights (m) within a 110 km × 110 km
area with a 30 s resolution (e.g. the Globe “gtopo30” data).
The value for the mid-path may be obtained from an area roughness
with 0.5 × 0.5 degree resolution of geographical coordinates
using bi-linear interpolation.
"""
if not self._s_a:
vals = load_data(os.path.join(dataset_dir,
'p530/v16_gtopo_30.txt'))
lats = load_data(os.path.join(dataset_dir, 'p530/v16_lat.txt'))
lons = load_data(os.path.join(dataset_dir, 'p530/v16_lon.txt'))
self._Pr6 = bilinear_2D_interpolator(lats, lons, vals)
return self._Pr6(np.array([lat.ravel(),
lon.ravel()]).T).reshape(lat.shape)
def Mt(self, lat, lon, m):
if not self._Mt:
lats = load_data(os.path.join(dataset_dir, 'p837/v7_LAT_MT.h5'))
lons = load_data(os.path.join(dataset_dir, 'p837/v7_LON_MT.h5'))
for _m in self.months:
vals = load_data(
os.path.join(dataset_dir,
'p837/v7_MT_Month{0:02d}.h5').format(_m))
self._Mt[_m] = bilinear_2D_interpolator(
np.flipud(lats), lons, np.flipud(vals))
# In this recommendation the longitude is encoded with format -180 to
# 180 whereas we always use 0 - 360 encoding
lon = np.array(lon)
lon[lon > 180] = lon[lon > 180] - 360
return self._Mt[m](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, 'p453/v13_NWET_Annual_%s.txt')
lats = load_data(os.path.join(dataset_dir, 'p453/v13_LAT_N.txt'))
lons = load_data(os.path.join(dataset_dir, 'p453/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)
class _ITU676_11():
tmp = load_data(os.path.join(dataset_dir,
'p676//v11_lines_water_vapour.txt'),
skip_header=1)
f_wv = tmp[:, 0]
b1 = tmp[:, 1]
b2 = tmp[:, 2]
b3 = tmp[:, 3]
b4 = tmp[:, 4]
b5 = tmp[:, 5]
b6 = tmp[:, 6]
idx_approx = np.zeros_like(b1, dtype=bool).squeeze()
asterisk_rows = [0, 3, 4, 5, 7, 12, 20, 24, 34]
idx_approx[np.array(asterisk_rows)] = True
def __init__(self):
self.__version__ = 11
self.year = 2017
self.month = 12
self.link = 'https://www.itu.int/rec/R-REC-P.676-11-201712-S/en'
def gammaw_approx(self, f, P, rho, T):
# T in Kelvin
# e : water vapour partial pressure in hPa (total barometric pressure
# ptot = p + e)
theta = 300 / T
e = rho * T / 216.7 # water vapour partial pressure
p = P - e # Dry air pressure
f_wv = self.f_wv[self.idx_approx]
b1 = self.b1[self.idx_approx]
b2 = self.b2[self.idx_approx]
b3 = self.b3[self.idx_approx]
b4 = self.b4[self.idx_approx]
b5 = self.b5[self.idx_approx]
b6 = self.b6[self.idx_approx]
N_pp_wv = 0
for ii in np.arange(np.size(f_wv)):
D_f_wv = b3[ii] * 1e-4 * (p * theta ** b4[ii] +
b5[ii] * e * theta ** b6[ii])
F_i_wv = f / f_wv[ii] * ((D_f_wv) / ((f_wv[ii] - f)**2 + D_f_wv**2) +
(D_f_wv) / ((f_wv[ii] + f)**2 + D_f_wv**2))
Si_wv = b1[ii] * 1e-1 * e * theta**3.5 * np.exp(b2[ii] * (1 - theta))
N_pp_wv = N_pp_wv + Si_wv * F_i_wv
gamma = 0.1820 * f * N_pp_wv # Eq. 1 [dB/km]
return gamma
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,
'p453/v12_DN65m_%02dd%02d_v1.txt')
lats = load_data(os.path.join(dataset_dir, 'p453/v12_lat0d75.txt'))
lons = load_data(os.path.join(dataset_dir, 'p453/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)
class _ITU676_10():
tmp = load_data(os.path.join(dataset_dir, 'p676/v10_lines_oxygen.txt'),
skip_header=1)
f_ox = tmp[:, 0]
a1 = tmp[:, 1]
a2 = tmp[:, 2]
a3 = tmp[:, 3]
a4 = tmp[:, 4]
a5 = tmp[:, 5]
a6 = tmp[:, 6]
def __init__(self):
self.__version__ = 10
self.year = 2013
self.month = 9
self.link = 'https://www.itu.int/rec/R-REC-P.676-10-201309-S/en'
def gamma0_exact(self, f, P, rho, T):
return __gamma0_exact__676_9_11__(self, f, P, rho, T)
class _ITU676_10():
tmp = load_data(os.path.join(dataset_dir,
'p676//v10_lines_water_vapour.txt'),
skip_header=1)
f_wv = tmp[:, 0]
b1 = tmp[:, 1]
b2 = tmp[:, 2]
b3 = tmp[:, 3]
b4 = tmp[:, 4]
b5 = tmp[:, 5]
b6 = tmp[:, 6]
def __init__(self):
self.__version__ = 10
self.year = 2013
self.month = 9
self.link = 'https://www.itu.int/rec/R-REC-P.676-10-201309-S/en'
def gammaw_exact(self, f, P, rho, T):
return __gammaw_exact__676_9_11__(self, f, P, rho, T)
class _ITU676_9():
tmp = load_data(os.path.join(dataset_dir, 'p676//v9_lines_oxygen.txt'),
skip_header=1)
f_ox = tmp[:, 0]
a1 = tmp[:, 1]
a2 = tmp[:, 2]
a3 = tmp[:, 3]
a4 = tmp[:, 4]
a5 = tmp[:, 5]
a6 = tmp[:, 6]
def __init__(self):
self.__version__ = 9
self.year = 2012
self.month = 2
self.link = 'https://www.itu.int/rec/R-REC-P.676-9-201202-S/en'
# Recommendation ITU-P R.676-9 has most of the methods similar to those
# in Recommendation ITU-P R.676-10.
def gamma0_exact(self, f, P, rho, T):
return __gamma0_exact__676_9_11__(self, f, P, rho, T)
