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 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 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 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 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 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 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 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 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)
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)
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)
