def hydrometeor_classification(radar, gatefilter, kdp_name, zdr_name, band, refl_name='DBZ_CORR',
rhohv_name='RHOHV_CORR',
temperature_name='temperature',
height_name='height'):
"""
Compute hydrometeo classification.
Parameters:
===========
radar:
Py-ART radar structure.
refl_name: str
Reflectivity field name.
zdr_name: str
ZDR field name.
kdp_name: str
KDP field name.
rhohv_name: str
RHOHV field name.
temperature_name: str
Sounding temperature field name.
height: str
Gate height field name.
Returns:
========
hydro_meta: dict
Hydrometeor classification.
"""
refl = radar.fields[refl_name]['data'].copy().filled(np.NaN)
zdr = radar.fields[zdr_name]['data'].copy().filled(np.NaN)
try:
kdp = radar.fields[kdp_name]['data'].copy().filled(np.NaN)
except AttributeError:
kdp = radar.fields[kdp_name]['data'].copy()
rhohv = radar.fields[rhohv_name]['data']
try:
radar_T = radar.fields[temperature_name]['data']
use_temperature = True
except Exception:
use_temperature = False
if use_temperature:
scores = csu_fhc.csu_fhc_summer(dz=refl, zdr=zdr, rho=rhohv, kdp=kdp, use_temp=True, band=band, T=radar_T)
else:
scores = csu_fhc.csu_fhc_summer(dz=refl, zdr=zdr, rho=rhohv, kdp=kdp, use_temp=False, band=band)
hydro = np.argmax(scores, axis=0) + 1
hydro[gatefilter.gate_excluded] = 0
hydro_data = np.ma.masked_equal(hydro.astype(np.int16), 0)
the_comments = "1: Drizzle; 2: Rain; 3: Ice Crystals; 4: Aggregates; " +\
"5: Wet Snow; 6: Vertical Ice; 7: LD Graupel; 8: HD Graupel; 9: Hail; 10: Big Drops"
hydro_meta = {'data': hydro_data, 'units': ' ', 'long_name': 'Hydrometeor classification', '_FillValue': np.int16(0),
'standard_name': 'Hydrometeor_ID', 'comments': the_comments}
return hydro_meta
def get_hid(self):
"""Calculate hydrometeror ID, add to radar object."""
_fhv = _FhcVars(self)
if not self.winter_flag:
bt = time.time()
# Cut down on processing missing/bad data
good = _fhv.dz != self.bad
if self.name_ld is not None:
_fhv.ld = _fhv.ld[good]
if _fhv.tt is not None:
_fhv.tt = _fhv.tt[good]
# Only run FHC if the scope is not completely blank
if np.size(_fhv.dz[good]) > 0:
scores = csu_fhc.csu_fhc_summer(
dz=_fhv.dz[good], zdr=_fhv.dr[good], rho=_fhv.rh[good],
kdp=_fhv.kd[good], ldr=_fhv.ld, use_temp=self.T_flag,
band=self.band, method=self.fhc_method, T=_fhv.tt,
verbose=self.verbose, temp_factor=self.T_factor,
weights=self.fhc_weights)
# Create and populate the FHC field
fh = np.zeros_like(_fhv.dz) + self.bad
fh[good] = np.argmax(scores, axis=0) + 1
fh = np.reshape(fh, _fhv.shp)
self.add_field_to_radar_object(fh, field_name=self.name_fhc)
print(time.time()-bt, 'seconds to do FHC')
else:
print('No good data for FHC, not performed')
else:
print('Winter HID not enabled yet, sorry!')
def get_hid(self):
"""Calculate hydrometeror ID, add to radar object."""
_fhv = _FhcVars(self)
if not self.winter_flag:
bt = time.time()
# Cut down on processing missing/bad data
good = _fhv.dz != self.bad
if self.name_ld is not None:
_fhv.ld = _fhv.ld[good]
if _fhv.tt is not None:
_fhv.tt = _fhv.tt[good]
# Only run FHC if the scope is not completely blank
if np.size(_fhv.dz[good]) > 0:
scores = csu_fhc.csu_fhc_summer(
dz=_fhv.dz[good], zdr=_fhv.dr[good], rho=_fhv.rh[good],
kdp=_fhv.kd[good], ldr=_fhv.ld, use_temp=self.T_flag,
band=self.band, method=self.fhc_method, T=_fhv.tt,
verbose=self.verbose, temp_factor=self.T_factor,
weights=self.fhc_weights)
# Create and populate the FHC field
fh = np.zeros_like(_fhv.dz) + self.bad
fh[good] = np.argmax(scores, axis=0) + 1
fh = np.reshape(fh, _fhv.shp)
self.add_field_to_radar_object(fh, field_name=self.name_fhc)
print(time.time()-bt, 'seconds to do FHC')
else:
print('No good data for FHC, not performed')
else:
print('Winter HID not enabled yet, sorry!')
def hydrometeor_classification(radar, refl_name='DBZ_CORR', zdr_name='ZDR_CORR',
kdp_name='KDP', rhohv_name='RHOHV_CORR',
temperature_name='sounding_temperature',
height_name='height'):
"""
Compute hydrometeo classification.
Parameters:
===========
radar:
Py-ART radar structure.
refl_name: str
Reflectivity field name.
zdr_name: str
ZDR field name.
kdp_name: str
KDP field name.
rhohv_name: str
RHOHV field name.
temperature_name: str
Sounding temperature field name.
height: str
Gate height field name.
Returns:
========
hydro_meta: dict
Hydrometeor classification.
"""
refl = radar.fields[refl_name]['data']
zdr = radar.fields[zdr_name]['data']
kdp = radar.fields[kdp_name]['data']
rhohv = radar.fields[rhohv_name]['data']
radar_T = radar.fields[temperature_name]['data']
radar_z = radar.fields[height_name]['data']
scores = csu_fhc.csu_fhc_summer(dz=refl, zdr=zdr, rho=rhohv, kdp=kdp, use_temp=True, band='C', T=radar_T)
hydro = np.argmax(scores, axis=0) + 1
fill_value = -32768
hydro_data = np.ma.asanyarray(hydro)
hydro_data.mask = hydro_data == fill_value
the_comments = "1: Drizzle; 2: Rain; 3: Ice Crystals; 4: Aggregates; " +\
"5: Wet Snow; 6: Vertical Ice; 7: LD Graupel; 8: HD Graupel; 9: Hail; 10: Big Drops"
hydro_meta = {'data': hydro_data, 'units': ' ', 'long_name': 'Hydrometeor classification',
'standard_name': 'Hydrometeor_ID', 'comments': the_comments }
return hydro_meta
def calculate_hidro_rain(radar,
sounding,
rain_field_dict=dict(field_name='rain',
units='mm h-1',
long_name='HIDRO Rainfall Rate',
standard_name='rain_hca'),
method_field_dict=dict(
field_name='method',
units='',
long_name='HIDRO Rainfall Method',
standard_name='rain_estimate_method')):
'''
Calculate rainfall using the hidro_rain, method
Parameters
----------
radar: pyart radar object from nexrad volume scan
sounding: SkewT sounding for the appropriate time and location
Returns
-------
radar: with new rain field added according to dict
'''
from csu_radartools import csu_fhc, csu_blended_rain
dz = extract_unmasked_data(radar, 'reflectivity')
dr = extract_unmasked_data(radar, 'differential_reflectivity')
dp = extract_unmasked_data(radar, 'differential_phase')
rh = extract_unmasked_data(radar, 'cross_correlation_ratio')
radar_T, radar_z = interpolate_sounding_to_radar(sounding, radar)
if 'kdp' not in radar.fields.keys():
radar = get_kdp(radar)
kd = extract_unmasked_data(radar, 'kdp')
scores = csu_fhc.csu_fhc_summer(dz=dz,
zdr=dr,
rho=rh,
kdp=kd,
band='S',
use_temp=True,
T=radar_T)
fh = np.argmax(scores, axis=0) + 1
rain, method = csu_blended_rain.csu_hidro_rain(dz=dz,
zdr=dr,
kdp=kd,
fhc=fh)
radar = add_field_to_radar_object(rain, radar, **rain_field_dict)
radar = add_field_to_radar_object(method, radar, **method_field_dict)
return radar
def get_hid(self):
"""Calculate hydrometeror ID, add to radar object."""
dz = self.radar.fields[self.name_dz]['data']
dr = self.radar.fields[self.name_dr]['data']
kd = self.radar.fields[self.name_kd]['data']
rh = self.radar.fields[self.name_rh]['data']
if self.name_ld is not None:
ld = self.radar.fields[self.name_ld]['data']
else:
ld = None
if not self.winter_flag:
scores = csu_fhc.csu_fhc_summer(
dz=dz, zdr=dr, rho=rh, kdp=kd,
ldr=ld, use_temp=self.T_flag, band=self.band,
method=self.fhc_method, T=self.radar_T,
verbose=self.verbose, temp_factor=self.T_factor,
weights=self.fhc_weights)
fh = np.argmax(scores, axis=0) + 1
self.add_field_to_radar_object(fh, field_name=self.name_fhc)
else:
print('Winter HID not enabled yet, sorry!')
def calculate_radar_hid(radar, sounding_names, radar_band="S"):
"""
Use radar and sounding data to calculate:
- Temperature and height profiles
- Hydrometeor classification with CSU_RadarTools
- Liquid and ice water masses, ice fraction
Parameters
----------
radar: Py-ART radar data
sounding_names: list of sounding data filenames
radar_band: radar band
Returns
-------
file: radar data with HID and water masses
"""
# Getting date for sounding - optional
# radar_date = pd.to_datetime(radar.time['units'][14:])
# Interpolating with sounding
soundings = sounding(sounding_names)
# - optional: sounding_names.loc[str(radar_date.date())].item()
radar_T, radar_z = interpolate_sounding_to_radar(soundings, radar)
# Extracting necessary variables
z_corrected = radar.fields["corrected_reflectivity"]["data"]
zdr = radar.fields["differential_reflectivity"]["data"]
kdp = radar.fields["specific_differential_phase"]["data"]
rho_hv = radar.fields["cross_correlation_ratio"]["data"]
# Classifying
scores = csu_fhc.csu_fhc_summer(
weights={
"DZ": 1,
"DR": 1,
"KD": 1,
"RH": 1,
"LD": 1,
"T": 1
},
dz=z_corrected,
zdr=zdr,
kdp=kdp,
rho=rho_hv,
use_temp=True,
T=radar_T,
band=radar_band,
verbose=True,
method="hybrid",
)
fh = np.argmax(scores, axis=0) + 1
# - Adding to radar file
radar = add_field_to_radar_object(fh,
radar,
standard_name="Hydrometeor ID")
# - Calculating liquid and ice mass
mw, mi = csu_liquid_ice_mass.calc_liquid_ice_mass(z_corrected,
zdr,
radar_z / 1000.0,
T=radar_T)
# - Adding to radar file
file = add_field_to_radar_object(
mw,
radar,
field_name="MW",
units=r"$g\ m^{-3}$",
long_name="Liquid Water Mass",
standard_name="Liquid Water Mass",
)
file = add_field_to_radar_object(
mi,
file,
field_name="MI",
units=r"$g\ m^{-3}$",
long_name="Ice Water Mass",
standard_name="Ice Water Mass",
)
return file
sounding = SkewT.Sounding(filepath + sndfile)
# Extract relevant radar fields
dz = radar.fields['ZC']['data']
dr = radar.fields['ZD']['data']
kd = radar.fields['KD']['data']
rh = radar.fields['RH']['data']
# interpolate the sounding to radar grid
radar_T, radar_z = interpolate_sounding_to_radar(sounding, radar)
# run the classification
scores = csu_fhc.csu_fhc_summer(dz=dz,
zdr=dr,
rho=rh,
kdp=kd,
use_temp=True,
band='C',
T=radar_T)
fh = np.argmax(scores, axis=0) + 1
# add classification field to radar object
radar = add_field_to_radar_object(fh, radar)
# plot the result
hid_colors = [
'White', 'LightBlue', 'MediumBlue', 'DarkOrange', 'LightPink', 'Cyan',
'DarkGray', 'Lime', 'Yellow', 'Red', 'Fuchsia'
]
cmaphid = colors.ListedColormap(hid_colors)
cmapmeth = colors.ListedColormap(hid_colors[0:6])
def hydrometeor_classification(
radar,
gatefilter,
kdp_name,
zdr_name,
refl_name="DBZ_CORR",
rhohv_name="RHOHV_CORR",
temperature_name="temperature",
height_name="height",
):
"""
Compute hydrometeo classification.
Parameters:
===========
radar:
Py-ART radar structure.
refl_name: str
Reflectivity field name.
zdr_name: str
ZDR field name.
kdp_name: str
KDP field name.
rhohv_name: str
RHOHV field name.
temperature_name: str
Sounding temperature field name.
height: str
Gate height field name.
Returns:
========
hydro_meta: dict
Hydrometeor classification.
"""
refl = radar.fields[refl_name]["data"].copy().filled(np.NaN)
zdr = radar.fields[zdr_name]["data"].copy().filled(np.NaN)
try:
kdp = radar.fields[kdp_name]["data"].copy().filled(np.NaN)
except AttributeError:
kdp = radar.fields[kdp_name]["data"].copy()
rhohv = radar.fields[rhohv_name]["data"]
try:
radar_T = radar.fields[temperature_name]["data"]
use_temperature = True
except Exception:
use_temperature = False
if use_temperature:
hydro = csu_fhc.csu_fhc_summer(dz=refl,
zdr=zdr,
rho=rhohv,
kdp=kdp,
use_temp=True,
band="C",
T=radar_T)
else:
hydro = csu_fhc.csu_fhc_summer(dz=refl,
zdr=zdr,
rho=rhohv,
kdp=kdp,
use_temp=False,
band="C")
hydro[gatefilter.gate_excluded] = 0
hydro_data = np.ma.masked_equal(hydro.astype(np.int16), 0)
the_comments = (
"1: Drizzle; 2: Rain; 3: Ice Crystals; 4: Aggregates; " +
"5: Wet Snow; 6: Vertical Ice; 7: LD Graupel; 8: HD Graupel; 9: Hail; 10: Big Drops"
)
hydro_meta = {
"data": hydro_data,
"units": " ",
"long_name": "Hydrometeor classification",
"_FillValue": np.int16(0),
"standard_name": "Hydrometeor_ID",
"comments": the_comments,
}
return hydro_meta
# hid_identifier = 'zdr'
##Adjustment of ZDR, RHO
# set_weights = {'DZ': 1.5, 'DR': 1.2, 'KD': 0.8, 'RH': 0.6, 'LD': 0.0, 'T': 0.4}
# hid_identifier = 'zdrrho'
##2nd Adjustment of ZDR, RHO
# set_weights = {'DZ': 1.5, 'DR': 1.2, 'KD': 0.8, 'RH': 0.4, 'LD': 0.0, 'T': 0.4}
# hid_identifier = 'zdrrho2'
##Adjustment of ZDR, RHO, KDP
# set_weights = {'DZ': 1.5, 'DR': 1.2, 'KD': 1.0, 'RH': 0.4, 'LD': 0.0, 'T': 0.4}
# hid_identifier = 'zrk'
#determine HID scores
scores = csu_fhc.csu_fhc_summer(dz=dz, zdr=dr, rho=rh, kdp=kd, use_temp=True, band='S',
T=grid_T, weights = set_weights)
fh = np.argmax(scores, axis=0) + 1
# sys.exit()
#add HID results to grid
grid = add_field_to_grid_object(fh, grid, field_name = 'FH', units = 'unitless', long_name = 'Hydrometeor ID', standard_name = 'Hydrometeor ID')
#==============================================================================
#Mass calculations
#==============================================================================
#determine mass of water and ice
mw, mi = csu_liquid_ice_mass.calc_liquid_ice_mass(dz, dr, grid_z/1000.0, T=grid_T, method=None, fit_a=None, fit_b=None)
#add mass calculation results to grid
grid = add_field_to_grid_object(mw, grid, field_name = 'Mw', units = 'g/m^3', long_name = 'Liquid Water Mass', standard_name = 'Liquid Water Mass')
grid = add_field_to_grid_object(mi, grid, field_name = 'Mi', units = 'g/m^3', long_name = 'Ice Water Mass', standard_name = 'Ice Water Mass')
#==============================================================================
