def acquire_sounding_wind_data(date, station):
"""
Get sounding data using siphon, extract necessary variables and return a
dictionary with heigth and wind data for pyart FourDD algorithm.
Parameters
----------
date: date of the sounding (datetime.datetime)
station: name of the METAR sounding station
Returns
-------
d: "dictionary" of sounding wind data
"""
sounding = WyomingUpperAir.request_data(date, station)
sounding = sounding.dropna(subset=('height', 'speed', 'direction',
'u_wind', 'v_wind'),
how='all')
height = sounding['height'].values
speed = sounding['speed'].values
direction = sounding['direction'].values
lat = sounding['latitude'].values
lon = sounding['longitude'].values
profile = HorizontalWindProfile(height,
speed,
direction,
latitude=lat,
longitude=lon)
return profile
def test_horizontalwindprofile_from_u_and_v():
height = np.arange(5)
u_wind = [0, -1, 0, 1, -np.sqrt(2)/2.]
v_wind = [-1, 0, 1, 0, -np.sqrt(2)/2.]
hprofile = HorizontalWindProfile.from_u_and_v(height, u_wind, v_wind)
assert_almost_equal(hprofile.height, [0, 1, 2, 3, 4])
assert_almost_equal(hprofile.speed, [1, 1, 1, 1, 1])
assert_almost_equal(hprofile.direction, [0, 90, 180, 270, 45])
assert_almost_equal(hprofile.u_wind, [0, -1, 0, 1, -0.707], 3)
assert_almost_equal(hprofile.v_wind, [-1, 0, 1, 0, -0.707], 3)
def test_horizontalwindprofile_class():
height = np.arange(5)
speed = np.ones(5)
direction = np.array([0, 90, 180, 270, 45])
hprofile = HorizontalWindProfile(height, speed, direction)
assert_almost_equal(hprofile.height, [0, 1, 2, 3, 4])
assert_almost_equal(hprofile.speed, [1, 1, 1, 1, 1])
assert_almost_equal(hprofile.direction, [0, 90, 180, 270, 45])
assert_almost_equal(hprofile.u_wind, [0, -1, 0, 1, -0.707], 3)
assert_almost_equal(hprofile.v_wind, [-1, 0, 1, 0, -0.707], 3)
def velocity_azimuth_display(radar,
velocity,
z_want=None,
valid_ray_min=16,
gatefilter=None,
window=2,
weight='equal'):
"""
Velocity azimuth display.
Note: This code uses only one sweep. Before using the
velocity_azimuth_display function, use, for example:
one_sweep_radar = radar.extract_sweeps([0])
Parameters
----------
radar : Radar
Radar object used.
velocity : string
Velocity field to use for VAD calculation.
Other Parameters
----------------
z_want : array
Array of desired heights to be sampled for the vad
calculation.
valid_ray_min : int
Amount of rays required to include that level in
the VAD calculation.
gatefilter : GateFilter
A GateFilter indicating radar gates that should be excluded when
from the import vad calculation.
window : int
Value to use for window when determing new values in the
_Averag1D function.
weight : string
A string to indicate weighting method to use. 'equal' for
equal weighting when interpolating or 'idw' for inverse
distribution squared weighting for interpolating.
Default is 'equal'.
Returns
-------
height : array
Heights in meters above sea level at which horizontal winds were
sampled.
speed : array
Horizontal wind speed in meters per second at each height.
direction : array
Horizontal wind direction in degrees at each height.
u_wind : array
U-wind mean in meters per second.
v_wind : array
V-wind mean in meters per second.
Reference
----------
K. A. Browning and R. Wexler, 1968: The Determination
of Kinematic Properties of a Wind Field Using Doppler
Radar. J. Appl. Meteor., 7, 105–113
"""
velocities = radar.fields[velocity]['data']
if gatefilter is not None:
velocities = np.ma.masked_where(gatefilter.gate_excluded, velocities)
azimuths = radar.azimuth['data'][:]
elevation = radar.fixed_angle['data'][0]
u_wind, v_wind = _vad_calculation(velocities, azimuths, elevation,
valid_ray_min)
bad = np.logical_or(np.isnan(u_wind), np.isnan(v_wind))
good_u_wind = u_wind[~bad]
good_v_wind = v_wind[~bad]
radar_height = radar.gate_z['data'][0]
good_height = radar_height[~bad]
if z_want is None:
z_want = np.linspace(0, 1000, 100)[:50]
else:
z_want
try:
print('max height', np.max(good_height), ' meters')
print('min height', np.min(good_height), ' meters')
except ValueError:
raise ValueError('Not enough data in this radar sweep ' \
'for a vad calculation.')
u_interp = _Average1D(good_height, good_u_wind,
z_want[1] - z_want[0] / window, weight)
v_interp = _Average1D(good_height, good_v_wind,
z_want[1] - z_want[0] / window, weight)
u_wanted = u_interp(z_want)
v_wanted = v_interp(z_want)
u_wanted = np.ma.masked_equal(u_wanted, 99999.)
v_wanted = np.ma.masked_equal(v_wanted, 99999.)
vad = HorizontalWindProfile.from_u_and_v(z_want, u_wanted, v_wanted)
return vad
def velocity_azimuth_display(radar, velocity,
z_want=None, gatefilter=None):
"""
Velocity azimuth display.
Parameters
----------
radar : Radar
Radar object used.
velocity : string
Velocity field to use for VAD calculation.
Other Parameters
----------------
z_want : array
Heights for where to sample vads from.
None will result in np.linespace(0, 10000, 100).
gatefilter : GateFilter
A GateFilter indicating radar gates that should be excluded when
from the import vad calculation.
Returns
-------
height : array
Heights in meters above sea level at which horizontal winds were
sampled.
speed : array
Horizontal wind speed in meters per second at each height.
direction : array
Horizontal wind direction in degrees at each height.
u_wind : array
U-wind mean in meters per second.
v_wind : array
V-wind mean in meters per second.
Reference
----------
Michelson, D. B., Andersson, T., Koistinen, J., Collier, C. G., Riedl, J.,
Szturc, J., Gjertsen, U., Nielsen, A. and Overgaard, S. (2000) BALTEX Radar
Data Centre Products and their Methodologies. In SMHI Reports. Meteorology
and Climatology. Swedish Meteorological and Hydrological Institute,
Norrkoping.
"""
speed = []
angle = []
heights = []
z_gate_data = radar.gate_z['data']
if z_want is None:
z_want = np.linspace(0, 10000, 100)
else:
z_want
for i in range(len(radar.sweep_start_ray_index['data'])):
index_start = radar.sweep_start_ray_index['data'][i]
index_end = radar.sweep_end_ray_index['data'][i]
if (index_end - index_start) % 2 == 0:
print("even, all good")
else:
index_end = index_end - 1
velocities = radar.fields[
velocity]['data'][index_start:index_end]
if gatefilter is not None:
velocities = np.ma.masked_where(
gatefilter.gate_excluded, velocities)
# mask=velocities.mask
# velocities[np.where(mask)]=np.nan
azimuth = radar.azimuth['data'][index_start:index_end]
elevation = radar.fixed_angle['data'][i]
one_level = _vad_calculation(velocities,
azimuth, elevation)
bad = (np.isnan(one_level['speed']))
print('max height', z_gate_data[index_start, :][~bad].max(),
' meters')
speed.append(one_level['speed'][~bad])
angle.append(one_level['angle'][~bad])
heights.append(z_gate_data[index_start, :][~bad])
speed_array = np.concatenate(speed)
angle_array = np.concatenate(angle)
height_array = np.concatenate(heights)
arg_order = height_array.argsort()
speed_ordered = speed_array[arg_order]
height_ordered = height_array[arg_order]
angle_ordered = angle_array[arg_order]
u_ordered, v_ordered = _sd_to_uv(speed_ordered, angle_ordered)
u_mean = _interval_mean(u_ordered, height_ordered, z_want)
v_mean = _interval_mean(v_ordered, height_ordered, z_want)
vad = HorizontalWindProfile.from_u_and_v(z_want, u_mean,
v_mean)
return vad
def velocity_azimuth_display(radar, velocity, z_want=None, gatefilter=None):
"""
Velocity azimuth display.
Parameters
----------
radar : Radar
Radar object used.
velocity : string
Velocity field to use for VAD calculation.
Other Parameters
----------------
z_want : array
Heights for where to sample vads from.
None will result in np.linespace(0, 10000, 100).
gatefilter : GateFilter
A GateFilter indicating radar gates that should be excluded when
from the import vad calculation.
Returns
-------
height : array
Heights in meters above sea level at which horizontal winds were
sampled.
speed : array
Horizontal wind speed in meters per second at each height.
direction : array
Horizontal wind direction in degrees at each height.
u_wind : array
U-wind mean in meters per second.
v_wind : array
V-wind mean in meters per second.
Reference
----------
Michelson, D. B., Andersson, T., Koistinen, J., Collier, C. G., Riedl, J.,
Szturc, J., Gjertsen, U., Nielsen, A. and Overgaard, S. (2000) BALTEX Radar
Data Centre Products and their Methodologies. In SMHI Reports. Meteorology
and Climatology. Swedish Meteorological and Hydrological Institute,
Norrkoping.
"""
speed = []
angle = []
heights = []
z_gate_data = radar.gate_z['data']
if z_want is None:
z_want = np.linspace(0, 10000, 100)
else:
z_want
for i in range(len(radar.sweep_start_ray_index['data'])):
index_start = radar.sweep_start_ray_index['data'][i]
index_end = radar.sweep_end_ray_index['data'][i]
if (index_end - index_start) % 2 == 0:
print("even, all good")
else:
index_end = index_end - 1
velocities = radar.fields[velocity]['data'][index_start:index_end]
if gatefilter is not None:
velocities = np.ma.masked_where(gatefilter.gate_excluded,
velocities)
# mask=velocities.mask
# velocities[np.where(mask)]=np.nan
azimuth = radar.azimuth['data'][index_start:index_end]
elevation = radar.fixed_angle['data'][i]
one_level = _vad_calculation(velocities, azimuth, elevation)
bad = (np.isnan(one_level['speed']))
print('max height', z_gate_data[index_start, :][~bad].max(), ' meters')
speed.append(one_level['speed'][~bad])
angle.append(one_level['angle'][~bad])
heights.append(z_gate_data[index_start, :][~bad])
speed_array = np.concatenate(speed)
angle_array = np.concatenate(angle)
height_array = np.concatenate(heights)
arg_order = height_array.argsort()
speed_ordered = speed_array[arg_order]
height_ordered = height_array[arg_order]
angle_ordered = angle_array[arg_order]
u_ordered, v_ordered = _sd_to_uv(speed_ordered, angle_ordered)
u_mean = _interval_mean(u_ordered, height_ordered, z_want)
v_mean = _interval_mean(v_ordered, height_ordered, z_want)
vad = HorizontalWindProfile.from_u_and_v(z_want, u_mean, v_mean)
return vad
def velocity_azimuth_display(
radar, velocity, z_want=None,
valid_ray_min=16, gatefilter=None, window=2,
weight='equal'):
"""
Velocity azimuth display.
Note: This code uses only one sweep. Before using the
velocity_azimuth_display function, use, for example:
one_sweep_radar = radar.extract_sweeps([0])
Parameters
----------
radar : Radar
Radar object used.
velocity : string
Velocity field to use for VAD calculation.
Other Parameters
----------------
z_want : array
Array of desired heights to be sampled for the vad
calculation.
valid_ray_min : int
Amount of rays required to include that level in
the VAD calculation.
gatefilter : GateFilter
A GateFilter indicating radar gates that should be excluded when
from the import vad calculation.
window : int
Value to use for window when determing new values in the
_Averag1D function.
weight : string
A string to indicate weighting method to use. 'equal' for
equal weighting when interpolating or 'idw' for inverse
distribution squared weighting for interpolating.
Default is 'equal'.
Returns
-------
height : array
Heights in meters above sea level at which horizontal winds were
sampled.
speed : array
Horizontal wind speed in meters per second at each height.
direction : array
Horizontal wind direction in degrees at each height.
u_wind : array
U-wind mean in meters per second.
v_wind : array
V-wind mean in meters per second.
Reference
----------
K. A. Browning and R. Wexler, 1968: The Determination
of Kinematic Properties of a Wind Field Using Doppler
Radar. J. Appl. Meteor., 7, 105–113
"""
velocities = radar.fields[velocity]['data']
if gatefilter is not None:
velocities = np.ma.masked_where(
gatefilter.gate_excluded, velocities)
azimuths = radar.azimuth['data'][:]
elevation = radar.fixed_angle['data'][0]
u_wind, v_wind = _vad_calculation(velocities, azimuths,
elevation, valid_ray_min)
bad = np.logical_or(np.isnan(u_wind), np.isnan(v_wind))
good_u_wind = u_wind[~bad]
good_v_wind = v_wind[~bad]
radar_height = radar.gate_z['data'][0]
good_height = radar_height[~bad]
if z_want is None:
z_want = np.linspace(0, 1000, 100)[:50]
else:
z_want
try:
print('max height', np.max(good_height), ' meters')
print('min height', np.min(good_height), ' meters')
except ValueError:
raise ValueError('Not enough data in this radar sweep ' \
'for a vad calculation.')
u_interp = _Average1D(good_height, good_u_wind,
z_want[1] - z_want[0] / window, weight)
v_interp = _Average1D(good_height, good_v_wind,
z_want[1] - z_want[0] / window, weight)
u_wanted = u_interp(z_want)
v_wanted = v_interp(z_want)
u_wanted = np.ma.masked_equal(u_wanted, 99999.)
v_wanted = np.ma.masked_equal(v_wanted, 99999.)
vad = HorizontalWindProfile.from_u_and_v(
z_want, u_wanted, v_wanted)
return vad