import misc_functions as misc
import radar_functions as rf
import custom_vars as cv
import pydda_functions as pdf
# - Reading data
radar_1 = pdf.read_uf(cv.filenames_uf[0]) # SR
radar_2 = pdf.read_uf(cv.filenames_uf[1]) # FCTH
radar_3 = pdf.read_uf(cv.filenames_uf[2]) # XPOL
# - Gridding based on radar_2 (FCTH)
print('-- Gridding radars --')
grid_1 = rf.grid_radar(radar_1,
fields=['DT', 'VT'],
for_multidop=False,
origin=(radar_2.latitude['data'][0],
radar_2.longitude['data'][0]),
xlim=cv.grid_xlim,
ylim=cv.grid_ylim,
grid_shape=cv.grid_shape)
grid_2 = rf.grid_radar(radar_2,
fields=['DT', 'VT'],
for_multidop=False,
origin=(radar_2.latitude['data'][0],
radar_2.longitude['data'][0]),
xlim=cv.grid_xlim,
ylim=cv.grid_ylim,
grid_shape=cv.grid_shape)
grid_3 = rf.grid_radar(radar_3,
fields=['DT', 'VT'],
for_multidop=False,
origin=(radar_2.latitude['data'][0],
@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'$\degree\ km^{-1}$' grid.fields['differential_reflectivity']['units'] = 'dB' if cv.pt_br: grid.fields['cross_correlation_ratio']['units'] = 'adimensional' grid.fields['corrected_reflectivity']['standard_name'] = ( "Refletividade Corrigida") grid.fields['FH']['standard_name'] = ("IDs de Hidrometeoros") grid.fields['MW']['standard_name'] = ("Massa de Água Líquida") grid.fields['MI']['standard_name'] = ("Massa de Gelo") grid.fields['cross_correlation_ratio']['standard_name'] = (
):
"""
"""
# 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
# Selecting:
# - Area of interest
# - Z >= 40 dBZ
xgrid = xgrid.where((xgrid.lat > ylim_aoi[0])
return im
def open_select_im(
filepath_r, xlim_aoi, ylim_aoi, case, zero_height=4, forty_height=6,
):
"""
"""
# Reading radar + gridding + calculating mass + converting to xarray
radar = rf.read_radar(filepath_r)
radar = rf.calculate_radar_mw_mi(radar)
gradar = rf.grid_radar(
radar,
xlim=(-200000.0, 10000.0),
ylim=(-10000.0, 200000.0),
fields=["corrected_reflectivity", "MI"],
grid_shape=(20, 211, 211),
)
xgrid = gradar.to_xarray().squeeze()
del radar, gradar
# Selecting:
# - Area of interest
# - Z >= 40 dBZ
xgrid = xgrid.where(
(xgrid.lat > ylim_aoi[0])
& (xgrid.lat < ylim_aoi[1])
& (xgrid.lon > xlim_aoi[0])
& (xgrid.lon < xlim_aoi[1])
& (xgrid.corrected_reflectivity >= 35)