import radar_functions as rf
import custom_vars as cv
import pyart
import matplotlib.pyplot as plt
radar = rf.read_radar(
"Data/RADAR/SR/level_0/2017-03-14/SRO-250--2017-03-14--18-20-23.mvol")
# radar = pyart.io.read_uf("Data/RADAR/CTH/level_0_hdf5/2017-03-14/PNOVA2-20170314195729.HDF5")
#radar = rf.read_radar(cv.filename)
print(radar.info())
# display = pyart.graph.RadarDisplay(radar)
# fig = plt.figure()
# display.plot('cross_correlation_ratio', 0)
# plt.show()
def read_dealise_region(filename,
dbz_field='corrected_reflectivity',
vel_field='corrected_velocity'):
"""
Reading radar data with radar_funs and:
- Dealise data with pyart region-based algorithm
- Add a mising_value (and _FillValue if not available) field inside
reflectivity (DT) and velocity (VT) fields
Parameters
----------
filename: .mvol or .HDF5 file
dbz_field: name of the reflectivity field to be used
vel_field: name of the velocity field to be used
Returns
-------
radar: dealised Py-ART radar data
"""
# Reading
radar = read_radar(filename)
# Dealising
cp = deepcopy(radar.fields[dbz_field]['data'])
radar.add_field_like(dbz_field, 'DT', cp, replace_existing=True)
gatefilter = pyart.correct.GateFilter(radar)
gatefilter.exclude_masked(dbz_field)
corr_vel = pyart.correct.dealias_region_based(radar,
vel_field=vel_field,
keep_original=False,
gatefilter=gatefilter,
centered=True)
radar.add_field('VT', corr_vel, replace_existing=True)
# Adding missing_value
try:
radar.fields['DT']['missing_value'] = [
1.0 * radar.fields['DT']['_FillValue']
]
radar.fields['VT']['missing_value'] = [
1.0 * radar.fields['VT']['_FillValue']
]
except KeyError:
radar.fields['DT']['_FillValue'] = (
radar.fields['DT']['data'].fill_value)
radar.fields['DT']['missing_value'] = [
1.0 * radar.fields['DT']['_FillValue']
]
radar.fields['VT']['_FillValue'] = (
radar.fields['VT']['data'].fill_value)
radar.fields['VT']['missing_value'] = [
1.0 * radar.fields['VT']['_FillValue']
]
# Defining original and corrected velocity fieds
if vel_field == 'corrected_velocity':
radar.add_field_like(vel_field,
'velocity',
deepcopy(radar.fields[vel_field]['data']),
replace_existing=True)
radar.add_field_like('VT',
vel_field,
deepcopy(radar.fields['VT']['data']),
replace_existing=True)
else:
radar.add_field_like('VT',
'corrected_velocity',
deepcopy(radar.fields['VT']['data']),
replace_existing=True)
return radar
xlim_aoi,
ylim_aoi,
case,
zero_height=cv.zerodeg_height,
forty_height=cv.fortydeg_height,
):
"""
"""
# Reading merged radar + converting to xarray
grid = misc.open_object(filepath_m)
xgrid = grid.to_xarray().squeeze()
del grid
# Reading radar + gridding + calculating mass + converting to xarray
radar = rf.read_radar(filepath_r)
radar = rf.calculate_radar_hid(radar, sounding)
gradar = rf.grid_radar(
radar,
xlim=cv.grid_xlim,
ylim=cv.grid_ylim,
fields=["MI"],
grid_shape=cv.grid_shape,
)
xgradar = gradar.to_xarray().squeeze()
del radar, gradar
# Merging files
xgrid = xgrid.assign({"MI": xgradar.MI})
xgrid = xgrid.swap_dims({"x": "lon", "y": "lat"})
del xgradar
def read_dealise_4dd(filename,
date,
station,
dbz_field='corrected_reflectivity',
vel_field='corrected_velocity'):
"""
Reading radar data with radar_funs and:
- Dealise data with pyart FourDD algorithm
- Add a mising_value (and _FillValue if not available) field inside
reflectivity (DT) and velocity (VT) fields
Parameters
----------
filename: .mvol or .HDF5 file
date: date of the sounding (datetime.datetime)
station: name of the METAR sounding station
dbz_field: name of the reflectivity field to be used
vel_field: name of the velocity field to be used
Returns
-------
radar: dealised Py-ART radar data
"""
# Reading
radar = read_radar(filename)
# Getting sounding data
sounding = acquire_sounding_wind_data(date, station)
# Dealising
cp = deepcopy(radar.fields[dbz_field]['data'])
radar.add_field_like(dbz_field, 'DT', cp, replace_existing=True)
gatefilter = pyart.correct.GateFilter(radar)
gatefilter.exclude_transition()
gatefilter.exclude_invalid(vel_field)
gatefilter.exclude_invalid(dbz_field)
gatefilter.exclude_outside(dbz_field, 0, 80)
corr_vel = pyart.correct.dealias_fourdd(radar,
sonde_profile=sounding,
gatefilter=gatefilter,
vel_field=vel_field)
radar.add_field('VT', corr_vel, replace_existing=True)
# missing_value
try:
radar.fields['DT']['missing_value'] = [
1.0 * radar.fields['DT']['_FillValue']
]
radar.fields['VT']['missing_value'] = [
1.0 * radar.fields['VT']['_FillValue']
]
except KeyError:
radar.fields['DT']['_FillValue'] = (
radar.fields['DT']['data'].fill_value)
radar.fields['DT']['missing_value'] = [
1.0 * radar.fields['DT']['_FillValue']
]
radar.fields['VT']['_FillValue'] = (
radar.fields['VT']['data'].fill_value)
radar.fields['VT']['missing_value'] = [
1.0 * radar.fields['VT']['_FillValue']
]
# Defining original and corrected velocity fieds
if vel_field == 'corrected_velocity':
radar.add_field_like(vel_field,
'velocity',
deepcopy(radar.fields[vel_field]['data']),
replace_existing=True)
radar.add_field_like('VT',
vel_field,
deepcopy(radar.fields['VT']['data']),
replace_existing=True)
else:
radar.add_field_like('VT',
'corrected_velocity',
deepcopy(radar.fields['VT']['data']),
replace_existing=True)
return radar
- Classifying into 10 hydrometeor types (Drizzle, Rain, Ice Crystals,
Aggregates, Wet/Melting Snow, Vertically Aligned Ice, Low-Density Graupel,
High-Density Graupel, Hail and Big Drops)
- Calculating liquid and ice water mass
- Plotting data
Based on CSU_RadarTools Demonstration Notebook by Timothy Lang.
@author: Camila Lopes ([email protected])
"""
import radar_functions as rf
import custom_vars as cv
import custom_cbars
radar = rf.read_radar(cv.filename)
# radar = pyart.io.read_uf(cv.filename)
radar = rf.calculate_radar_hid(radar, cv.sounding_name, "S")
grid = rf.grid_radar(radar,
fields=[
'corrected_reflectivity', 'FH', 'MW', 'MI',
'cross_correlation_ratio',
'differential_reflectivity',
'specific_differential_phase'
],
origin=(radar.latitude['data'][0],
radar.longitude['data'][0]),
xlim=cv.grid_xlim,
ylim=cv.grid_ylim,
grid_shape=cv.grid_shape)
grid.fields['specific_differential_phase']['units'] = r'$\degree\ km^{-1}$'