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radarmet.py
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radarmet.py
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import os
import re
import glob
import datetime as dt
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import xarray as xr
import wradlib as wrl
import scipy
import h5py
from osgeo import gdal, osr
from scipy.spatial import cKDTree
def get_xpol_path(path=None, start_time=dt.datetime.today(), loc='boxpol'):
"""Create path of BoXPol/JuXPol radar data files.
Parameter
---------
path : str
Path to root folder of radar data,
defaults to None (/automount/radar/scans or /automount/radar-archiv/scans)
start_time : datetime.datetime
datetime - object to select correct folder
loc : str
"boxpol" or "juxpol" (not case sensitive)
Return
------
radar_path : str
Path to radar data
"""
loc = "" if loc.lower()[0:2] == "bo" else "_juelich"
if path is None:
ins = "-archiv" if start_time < dt.datetime(2015, 1, 1) else ""
path = f"/automount/radar{ins}/scans{loc}"
if not os.path.exists(path):
path = os.environ["RADARMET_DATA"]
radar_path = os.path.join(path, "{0}/{0}-{1:02}/{0}-{1:02d}-{2:02d}")
return radar_path.format(start_time.year, start_time.month, start_time.day)
def get_file_date_regex(filename):
"""Get regex from filename.
"""
# regex for ""%Y-%m-%d--%H:%M:%S"
reg0 = r"\d{4}.\d{2}.\d{2}..\d{2}.\d{2}.\d{2}"
# regex for "%Y%m%d%H%M%S"
reg1 = r"\d{14}"
match = re.search(reg0, os.path.basename(filename))
return reg1 if match is None else reg0
def get_datetime_from_filename(filename, regex):
"""Get datetime from filename.
"""
fmt = "%Y%m%d%H%M%S"
match = re.search(regex, os.path.basename(filename))
match = "".join(re.findall(r"[0-9]+", match.group()))
return dt.datetime.strptime(match, fmt)
def create_filelist(path_glob, starttime, endtime):
"""Create filelist from path_glob and filename dates
"""
file_names = sorted(glob.glob(path_glob))
regex = get_file_date_regex(file_names[0])
for fname in file_names:
time = get_datetime_from_filename(fname, regex)
if time >= starttime and time < endtime:
yield fname
def get_discrete_cmap(ticks, colors, bad="white", over=None, under=None):
"""Create discrete colormap.
Parameters
----------
ticks : int | sequence
number of ticks or sequence of ticks
colors : colormap | sequence
colormap or sequence of colors
bad : color
over : color
under : color
Returns
-------
matplotlib.colors.ListedColormap
"""
ticks = ticks if isinstance(ticks, int) else len(ticks)
if isinstance(colors, (str, mpl.colors.Colormap)):
cmap = mpl.cm.get_cmap(colors)
colors = cmap(np.linspace(0, 1, ticks + 1))
cmap = mpl.colors.ListedColormap(colors[1:-1])
if over is None:
over = colors[-1]
if under is None:
under = colors[0]
cmap.set_under(under)
cmap.set_over(over)
cmap.set_bad(color=bad)
return cmap
def get_discrete_norm(ticks):
"""Return discrete boundary norm.
Parameters
----------
ticks : sequence
sequence of ticks
Returns
-------
matplotlib.colors.BoundaryNorm
"""
return mpl.colors.BoundaryNorm(ticks, len(ticks) - 1)
def plot_reference_colorbar(ticks, cmap, ax, **kwargs):
"""Plot reference colorbar
Parameters
----------
ticks : sequence
sequence of ticks
cmap : cmap instance
ax : axes instance
Keyword Arguments
-----------------
Additonal Keywords for colorbar
Returns
-------
matplotlib.colorbar instance
"""
vmin = ticks[0] - np.diff(ticks)[0]
vmax = ticks[-1] + np.diff(ticks)[-1]
vmax2 = ticks[-1] + 2 * np.diff(ticks)[-1]
y = [vmin] + list(ticks) + [vmax] + [vmax2]
x = np.arange(2)
data = np.repeat(np.array(y, dtype=np.float), 2).reshape(-1, 2)
data[-2, :] = np.nan
norm = get_discrete_norm(ticks)
pm = ax.pcolormesh(x, y, data,
cmap=cmap, norm=norm,
)
cb = plt.colorbar(pm, ax=ax,
ticks=ticks,
**kwargs,
)
return cb
colors_prabhakar = np.array([[0.00, 1.00, 1.00],
[0.00, 0.70, 0.93],
[0.00, 0.00, 1.00],
[0.50, 1.00, 0.00],
[0.40, 0.80, 0.00],
[0.27, 0.55, 0.00],
[1.00, 1.00, 0.00],
[0.80, 0.80, 0.00],
[1.00, 0.65, 0.00],
[1.00, 0.27, 0.00],
[0.80, 0.22, 0.00],
[0.55, 0.15, 0.00],
[1.00, 0.00, 1.00],
[0.58, 0.44, 0.86]])
cmap_prabhakar = mpl.colors.ListedColormap(colors_prabhakar)
try: # try to register in case it is not there
mpl.cm.register_cmap("miub2", cmap=cmap_prabhakar)
except ValueError:
pass
visdict14 = dict(ZH=dict(ticks=np.arange(-10,55,5),
contours=[0, 5, 10, 15, 20, 25, 30, 35],
cmap=cmap_prabhakar,
name=r'Horizontal Reflectivity (dBz)',
short='$\mathrm{\mathsf{Z_{H}}}$'),
DBZH=dict(ticks=np.arange(-10,55,5),
contours=[0, 5, 10, 15, 20, 25, 30, 35],
cmap=cmap_prabhakar,
name=r'Horizontal Reflectivity (dBz)',
short='$\mathrm{\mathsf{Z_{H}}}$'),
TH=dict(ticks=np.arange(-10,55,5),
contours=[0, 5, 10, 15, 20, 25, 30, 35],
cmap=cmap_prabhakar,
name=r'Total Reflectivity (dBz)',
short='$\mathrm{\mathsf{Z_{H}}}$'),
DBTH=dict(ticks=np.arange(-10,55,5),
contours=[0, 5, 10, 15, 20, 25, 30, 35],
cmap=cmap_prabhakar,
name=r'Total Reflectivity (dBz)',
short='$\mathrm{\mathsf{Z_{H}}}$'),
ZDR=dict(ticks=np.array([-1., -0.1, 0, 0.1, 0.2, 0.3, 0.4, .5, 0.6, 0.8, 1.0, 2., 3.0]),
contours=np.array([-1, -0.3, 0]),
cmap=cmap_prabhakar,
name=r'Differential Reflectivity (dB)',
short='$\mathrm{\mathsf{Z_{DR}}}$'),
ZDR_OC=dict(ticks=np.array([-1., -0.1, 0, 0.1, 0.2, 0.3, 0.4, .5, 0.6, 0.8, 1.0, 2., 3.0]),
contours=np.array([-1, -0.3, 0]),
cmap=cmap_prabhakar,
name=r'Differential Reflectivity (offset corrected) (dB)',
short='$\mathrm{\mathsf{Z_{DR}}}$'),
ZDR_OC_AC=dict(ticks=np.array([-1., -0.1, 0, 0.1, 0.2, 0.3, 0.4, .5, 0.6, 0.8, 1.0, 2., 3.0]),
contours=np.array([-1, -0.3, 0]),
cmap=cmap_prabhakar,
name=r'Differential Reflectivity (offset+attenuation corrected) (dB)',
short='$\mathrm{\mathsf{Z_{DR}}}$'),
ZDR_AC=dict(ticks=np.array([-1., -0.1, 0, 0.1, 0.2, 0.3, 0.4, .5, 0.6, 0.8, 1.0, 2., 3.0]),
contours=np.array([-1, -0.3, 0]),
cmap=cmap_prabhakar,
name=r'Differential Reflectivity (attenuation corrected) (dB)',
short='$\mathrm{\mathsf{Z_{DR}}}$'),
RHOHV=dict(ticks=np.array([.7, .8, .85, .9, .92, .94, .95, .96, .97, .98, .99, .995, .998]),
cmap=cmap_prabhakar,
name=r'Crosscorrelation Coefficient ()',
short='$\mathrm{\mathsf{RHO_{HV}}}$'),
RHOHV_NC=dict(ticks=np.array([.7, .8, .85, .9, .92, .94, .95, .96, .97, .98, .99, .995, .998]),
cmap=cmap_prabhakar,
name=r'Crosscorrelation Coefficient (noise corrected) ()',
short='$\mathrm{\mathsf{RHO_{HV}}}$'),
KDP=dict(ticks=np.array([-0.5, -0.1, 0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 2.0, 3.0]),
cmap=cmap_prabhakar,
name=r'Specific Differental Phase (°/km)',
short='$\mathrm{\mathsf{K_{DP}}}$'),
KDP_ML_corrected=dict(ticks=np.array([-0.5, -0.1, 0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 2.0, 3.0]),
cmap=cmap_prabhakar,
name=r'Specific Differental Phase (°/km)',
short='$\mathrm{\mathsf{K_{DP}}}$'),
KDP_CONV=dict(ticks=np.array([-0.5, -0.1, 0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 2.0, 3.0]),
cmap=cmap_prabhakar,
name=r'Specific Differental Phase (°/km)',
short='$\mathrm{\mathsf{K_{DP}}}$'),
SQIH=dict(ticks=np.linspace(0,1,13),
cmap=cmap_prabhakar,
name=r'Signal Quality H ',
short='$\mathrm{\mathsf{SQIH}}$'),
TEMP=dict(contours=[5, 0, -5, -10, -15, -20, -25, -30, -35, -40]),
PHI=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
PHIDP=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
UPHIDP=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
UPHIDP_OC=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
UPHIDP_OC_SMOOTH=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
UPHIDP_OC_MASKED=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
PHIDP_OC=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
PHIDP_OC_MASKED=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
PHIDP_OC_SMOOTH=dict(ticks=np.array([0,5,10,15,20,25,30,40,50,60,70,80,90]),
cmap=cmap_prabhakar,
name=r'Differental Phase (°)',
short='$\mathrm{\mathsf{\Phi_{DP}}}$'),
VRADH=dict(ticks=np.array(np.linspace(-15, 15, 31)),
cmap="RdBu_r",
name=r'Radial velocity of scatterers away from instrument H (m/s)',
short='$\mathrm{\mathsf{WRADH}}$'),
HMC=dict(norm=mpl.colors.BoundaryNorm(np.arange(-0.5, 11 + 0.6, 1), 12),
ticks=np.arange(0, 11 + 1),
bounds=np.arange(-0.5, 11 + 0.6, 1),
cmap=mpl.colors.ListedColormap(['LightBlue', 'Blue', 'Lime', 'Black', 'Red','Yellow', \
'Fuchsia', 'LightPink', 'Cyan', 'Gray', 'DarkOrange','White']),
name=r'HMC - Zrnic et al. 2001 ',
short=r'$\HMC_{Z}$',
long_name=r'Hydrometeorclass',
labels=['Light Rain', 'Moderate Rain', 'Heavy Rain', 'Large Drops', 'Hail', 'Rain/Hail', \
'Graupel/Hail', 'Dry Snow', 'Wet Snow', 'H Crystals','V Crystals','No Rain'],
labels_short=['LR', 'MR', 'HR', 'LD', 'HL', 'RH', 'GH', 'DS', 'WS', 'HC', 'VC', 'NR'])
)
def plot_moment(mom, ticks, fig=None, ax=None, cmap=None, norm=None, cbar_kwargs=None):
xlabel = 'X-Range [m]'
ylabel = 'Y-Range [m]'
if not ax.is_last_row():
xlabel = ''
if not ax.is_first_col():
ylabel = ''
# colorbar kwargs
cbarkwargs = dict(extend="both",
extendrect=False,
extendfrac='auto',
pad=0.05,
fraction=0.1,
)
if cbar_kwargs is not None:
cbarkwargs.update(cbar_kwargs)
cbar_extend = cbarkwargs.get("extend", None)
# get norm
if norm is None:
norm = get_discrete_norm(ticks)
# define cmap
if cmap is None:
cmap = get_discrete_cmap(ticks, colors_prabhakar)
if mom.sweep_mode == "rhi":
xstr = "gr"
ystr = "z"
else:
xstr = "x"
ystr = "y"
# create plot
im = mom.plot(x=xstr, y=ystr, ax=ax,
norm=norm,
cmap=cmap,
add_colorbar=True,
cbar_kwargs=cbarkwargs,
add_labels=True,
extend=cbar_extend,
)
im.colorbar.ax.tick_params(labelsize=16)
plt.setp(im.colorbar.ax.yaxis.label, fontsize=16)
# set_tick_params(labelsize=16)
ax.xaxis.set_tick_params(labelsize=16)
ax.yaxis.set_tick_params(labelsize=16)
ax.set_title("{}".format(mom.attrs['long_name']), fontsize=16)
ax.set_xlabel(xlabel, fontsize=16)
ax.set_ylabel(ylabel, fontsize=16)
if mom.sweep_mode != "rhi":
ax.set_aspect(1)
return im
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for _, sp in ax.spines.items():
sp.set_visible(False)
def set_spine_direction(ax, direction):
if direction in ["right", "left"]:
ax.yaxis.set_ticks_position(direction)
ax.yaxis.set_label_position(direction)
elif direction in ["top", "bottom"]:
ax.xaxis.set_ticks_position(direction)
ax.xaxis.set_label_position(direction)
else:
raise ValueError("Unknown Direction: %s" % (direction,))
ax.spines[direction].set_visible(True)
def create_lineplot(fig, subplot=111, xlabel=None, ylabel=None):
if xlabel is None:
xlabel = 'Range Bins'
if ylabel is None:
ylabel = ''
host = fig.add_subplot(subplot)
ax = host
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
return ax
def add_axis(host, ylabel=None, pos=1.0):
if ylabel is None:
ylabel=''
ax = host.twinx()
host.spines["right"].set_visible(False)
ax.spines["right"].set_position(("axes", pos))
make_patch_spines_invisible(ax)
set_spine_direction(ax, "right")
ax.set_ylabel(ylabel)
return ax
# phase processing
def filter_data(data, medwin):
data.values = scipy.signal.medfilt2d(data.values, [1, medwin])
return data
def filter_data_np(data, medwin):
data = scipy.signal.medfilt2d(data, [1, medwin])
return data
def gauss_kernel(width, sigma):
dirac = np.zeros(width)
dirac[int(width / 2)] = 1
return scipy.ndimage.gaussian_filter1d(dirac, sigma=sigma)
def convolve(data, kernel, mode='same'):
mask = np.isnan(data)
out = np.convolve(np.where(mask, 0, data), kernel, mode=mode) / np.convolve(~mask, kernel, mode=mode)
return out
def get_peaks(da, height=0):
def process_peaks(arr, height=0):
# Apply find_peaks
arr = arr.copy()
peaks, _ = scipy.signal.find_peaks(arr, height=height)
try:
peak = peaks[0]
except IndexError:
peak=0
return peak
return xr.apply_ufunc(process_peaks,
da,
input_core_dims=[["PHIDP_bin"]],
output_core_dims=[[]],
#output_sizes={"peaks": len(da)},
output_dtypes=((int)),
dask='parallelized',
vectorize=True,
kwargs=dict(height=height),
dask_gufunc_kwargs=dict(allow_rechunk=True),
)
def smooth_data(data, kernel):
res = data.copy()
try: # in case data is xarray
for i, dat in enumerate(data.values):
res[i] = convolve(dat, kernel)
except AttributeError: # in case data is numpy array
for i, dat in enumerate(data):
res[i] = convolve(dat, kernel)
return res
def phase_offset(phioff, method=None, rng=3000.0, npix=None, **kwargs):
"""Calculate Phase offset.
Parameter
---------
phioff : xarray.DataArray
differential phase DataArray
Keyword Arguments
-----------------
method : str
aggregation method, defaults to 'median'
rng : float
range in m to calculate system phase offset
Return
------
phidp_offset : xarray.Dataset
Dataset with PhiDP offset and start/stop ranges
"""
range_step = np.diff(phioff.range)[0]
nprec = int(rng / range_step)
if not nprec % 2:
nprec += 1
if npix is None:
npix = nprec // 2 + 1
# create binary array
phib = xr.where(np.isnan(phioff), 0, 1)
# take nprec range bins and calculate sum
phib_sum = phib.rolling(range=nprec, **kwargs).sum(skipna=True)
# find at least N pixels in
# phib_sum_N = phib_sum.where(phib_sum >= npix)
phib_sum_N = xr.where(phib_sum <= npix, phib_sum, npix)
# get start range of first N consecutive precip bins
start_range = (
phib_sum_N.idxmax(dim="range") - nprec // 2 * np.diff(phib_sum.range)[0]
)
start_range = xr.where(start_range < 0, 0, start_range)
# get stop range
stop_range = start_range + rng
# get phase values in specified range
off = phioff.where(
(phioff.range >= start_range) & (phioff.range <= stop_range), drop=False
)
# calculate nan median over range
if method is None:
method = "median"
func = getattr(off, method)
off_func = func(dim="range", skipna=True)
return xr.Dataset(
dict(
PHIDP_OFFSET=off_func,
start_range=start_range,
stop_range=stop_range,
phib_sum=phib_sum,
phib=phib,
)
)
def kdp_from_phidp(da, winlen, min_periods=2):
"""Derive KDP from PHIDP (based on convolution filter).
Parameter
---------
da : xarray.DataArray
array with differential phase data
winlen : int
size of window in range dimension
Keyword Arguments
-----------------
min_periods : int
minimum number of valid bins
Return
------
kdp : xarray.DataArray
DataArray with specific differential phase values
"""
dr = da.range.diff('range').median('range').values / 1000.
print("range res [km]:", dr)
print("processing window [km]:", dr * winlen)
return xr.apply_ufunc(wrl.dp.kdp_from_phidp,
da,
input_core_dims=[["range"]],
output_core_dims=[["range"]],
dask='parallelized',
kwargs=dict(winlen=winlen, dr=dr, min_periods=min_periods),
dask_gufunc_kwargs=dict(allow_rechunk=True),
)
def phidp_from_kdp(da, winlen):
"""Derive PHIDP from KDP.
Parameter
---------
da : xarray.DataArray
array with specific differential phase data
winlen : int
size of window in range dimension
Return
------
phi : xarray.DataArray
DataArray with differential phase values
"""
dr = da.range.diff('range').median('range').values / 1000.
print("range res [km]:", dr)
print("processing window [km]:", dr * winlen)
return xr.apply_ufunc(scipy.integrate.cumtrapz,
da,
input_core_dims=[["range"]],
output_core_dims=[["range"]],
dask='parallelized',
kwargs=dict(dx=dr, initial=0.0, axis=-1),
) * 2
def kdp_phidp_vulpiani(da, winlen, min_periods=2):
"""Derive KDP from PHIDP (based on Vulpiani).
ParameterRHOHV_NC
---------
da : xarray.DataArray
array with differential phase data
winlen : int
size of window in range dimension
Keyword Arguments
-----------------
min_periods : int
minimum number of valid bins
Return
------
kdp : xarray.DataArray
DataArray with specific differential phase values
"""
dr = da.range.diff('range').median('range').values / 1000.
print("range res [km]:", dr)
print("processing window [km]:", dr * winlen)
return xr.apply_ufunc(wrl.dp.phidp_kdp_vulpiani,
da,
input_core_dims=[["range"]],
output_core_dims=[["range"], ["range"]],
dask='parallelized',
kwargs=dict(winlen=winlen, dr=dr,
min_periods=min_periods),
dask_gufunc_kwargs=dict(allow_rechunk=True),
)
def xr_rolling(da, window, window2=None, method="mean", min_periods=2, rangepad="fill", **kwargs):
"""Apply rolling function `method` to 2D datasets
Parameter
---------
da : xarray.DataArray
array with data to apply rolling function
window : int
size of window in range dimension
Keyword Arguments
-----------------
window2 : int
size of window in azimuth dimension
method : str
function name to apply
min_periods : int
minimum number of valid bins
rangepad : string
Padding method for the edges of the range dimension. "fill" will fill the
nan values resulting from not enough bins by stretching the closest value.
"reflect" will extend the original array by reflecting around the edges
so there is enough bins for the calculation
**kwargs : dict
kwargs to feed to rolling function
Return
------
da_new : xarray.DataArray
DataArray with applied rolling function
"""
prng = window // 2
srng = slice(prng, -prng)
if rangepad == "reflect":
da_new = da.pad(range=prng, mode='reflect', reflect_type='odd')
isel = dict(range=srng)
else:
da_new = da
isel = dict()
dim = dict(range=window)
if window2 is not None:
paz = window2 // 2
saz = slice(paz, -paz)
da_new = da_new.pad(azimuth=paz, mode="wrap")
dim.update(dict(azimuth=window2))
isel.update(dict(azimuth=saz))
rolling = da_new.rolling(dim=dim, center=True, min_periods=min_periods)
da_new = getattr(rolling, method)(**kwargs)
da_new = da_new.isel(**isel)
if rangepad == "fill":
da_new = da_new.bfill("range").ffill("range")
return da_new
'''
# THIS FUNCTION FOR PHIDP PROCESSING NEEDS PY-ART. NOT IMPLEMENTED ATM!!!
def phidp_giangrande(radar, gatefilter, refl_field='DBZH', ncp_field='NCP',
rhv_field='RHOHV_CORR', phidp_field='PHIDP'):
"""
Phase processing using the LP method in Py-ART. A LP solver is required,
Parameters:
===========
radar:
Py-ART radar structure.
gatefilter:
Gate filter.
refl_field: str
Reflectivity field label.
ncp_field: str
Normalised coherent power field label.
rhv_field: str
Cross correlation ration field label.
phidp_field: str
Differential phase label.
Returns:
========
phidp_gg: dict
Field dictionary containing processed differential phase shifts.
kdp_gg: dict
Field dictionary containing recalculated differential phases.
"""
unfphidic = pyart.correct.dealias_unwrap_phase(radar,
gatefilter=gatefilter,
skip_checks=True,
vel_field=phidp_field,
nyquist_vel=90)
radar.add_field_like(phidp_field, 'PHITMP', unfphidic['data'])
phidp_gg, kdp_gg = pyart.correct.phase_proc_lp(radar, 0.0,
LP_solver='cylp',
ncp_field=ncp_field,
refl_field=refl_field,
rhv_field=rhv_field,
phidp_field='PHITMP')
phidp_gg['data'], kdp_gg['data'] = _fix_phidp_from_kdp(phidp_gg['data'],
kdp_gg['data'],
radar.range['data'],
gatefilter)
try:
# Remove temp variables.
radar.fields.pop('PHITMP')
except Exception:
pass
phidp_gg['data'] = phidp_gg['data'].astype(np.float32)
phidp_gg['_Least_significant_digit'] = 4
kdp_gg['data'] = kdp_gg['data'].astype(np.float32)
kdp_gg['_Least_significant_digit'] = 4
return phidp_gg, kdp_gg
'''
# Hydrometeor Classification
def msf_index_indep_xarray(msf_ds, obs):
def wrap_digitize(data, bins=None):
return np.digitize(data, bins)
idp = msf_ds.idp.values
bins = np.append(idp, idp[-1] + (idp[-1] - idp[-2]))
idx = xr.apply_ufunc(wrap_digitize,
obs,
dask='parallelized',
kwargs=dict(bins=bins),
output_dtypes=['i4']) - 1
# select bins
idx = xr.where((idx >= 0) & (idx < bins.shape[0] - 2), idx, 0)
return msf_ds.isel(idp=idx)
def trapezoid(msf, obs):
ones = ((obs >= msf[..., 1]) & (obs <= msf[..., 2]))
zeros = ((obs < msf[..., 0]) | (obs > msf[..., 3]))
lower = ((obs >= msf[..., 0]) & (obs < msf[..., 1]))
higher = ((obs > msf[..., 2]) & (obs <= msf[..., 3]))
obs_lower = obs - msf[..., 0]
msf_lower = msf[..., 1] - msf[..., 0]
low = (obs_lower / msf_lower)
obs_higher = obs - msf[..., 3]
msf_higher = msf[..., 2] - msf[..., 3]
high = (obs_higher / msf_higher)
ret = xr.zeros_like(obs) # * np.nan
# ret = ret.where(zeros, 0).where(ones, 1).where(lower, low).where(higher, high)
ret = xr.where(ones, 1, ret)
ret = xr.where(lower, low, ret)
ret = xr.where(higher, high, ret)
return ret # .where(ret == np.nan, 0)
def fuzzify(msf_ds, hmc_ds, msf_obs_mapping):
fuzz_ds = xr.Dataset()
for mf, hm in msf_obs_mapping.items():
obs = hmc_ds[hm]
msf = msf_ds[mf]
fuzz_ds = fuzz_ds.assign({mf: trapezoid(msf, obs)})
return fuzz_ds.transpose("hmc", ...)
def probability(data, weights):
"""Calculate probability of hmc-class for every data bin.
Parameters
----------
data : xarray.Dataset
Dataset containing containing the membership probability values.
weights : :class:`numpy:numpy.ndarray`
Array of length (observables) containing the weights for
each observable.
Returns
-------
out : xarray.DataArray
Array containing weighted hmc-membership probabilities.
"""
out = data.to_array(dim="obs")
w = weights.to_array(dim="obs")
out = (out * w).sum("obs") / w.sum("obs")
return out # .transpose("hmc", ...)
def classify(data, threshold=0.0):
"""Calculate probability of hmc-class for every data bin.
Parameters
----------
data : np.ndarray
Array which is of size (hmc-class, data.shape), containing the
weighted hmc-membership probability values.
Keyword Arguments
-----------------
threshold : float
Threshold value where probability is considered no precip,
defaults to 0
Returns
-------
out : xr.DataArray
DataArray containing containing probability scores.
No precip is added on the top.
"""
# handle no precipitation
nop = xr.where(data.sum("hmc") / len(data.hmc) <= threshold, 1, 0)
nop = nop.assign_coords({"hmc": "NP"}).expand_dims(dim="hmc", axis=-1)
return xr.concat([data, nop], dim="hmc")
####### Satellite ###################
def convert_gpmrefl_grband_dfr(refl_gpm, radar_band=None):
"""
Convert GPM reflectivity to ground radar band using the DFR relationship
found in Louf et al. (2019) paper.
Parameters:
===========
refl_gpm:
Satellite reflectivity field.
radar_band: str
Possible values are 'S', 'C', or 'X'
Return:
=======
refl:
Reflectivity conversion from Ku-band to ground radar band
"""
if radar_band == "S":
cof = np.array(
[2.01236803e-07, -6.50694273e-06, 1.10885533e-03, -6.47985914e-02,
-7.46518423e-02])
dfr = np.poly1d(cof)
elif radar_band == "C":
cof = np.array(
[1.21547932e-06, -1.23266138e-04, 6.38562875e-03, -1.52248868e-01,
5.33556919e-01])
dfr = np.poly1d(cof)
elif radar_band == "X":
# Use of C band DFR relationship multiply by ratio
cof = np.array(
[1.21547932e-06, -1.23266138e-04, 6.38562875e-03, -1.52248868e-01,
5.33556919e-01])
dfr = 3.2 / 5.5 * np.poly1d(cof)
else:
raise ValueError(f"Radar reflectivity band ({radar_band}) not supported.")
return refl_gpm + dfr(refl_gpm)
import copy
import numpy as np
def convert_sat_refl_to_gr_band(refp, zp, zbb, bbwidth, radar_band='S'):
"""
Convert the satellite reflectivity to S, C, or X-band using the Cao et al.
(2013) method.
Parameters
==========
refp:
Satellite reflectivity field.
zp:
Altitude.
zbb:
Bright band height.
bbwidth:
Bright band width.
radar_band: str
Possible values are 'S', 'C', or 'X'
Return
======
refp_ss:
Stratiform reflectivity conversion from Ku-band to S-band
refp_sh:
Convective reflectivity conversion from Ku-band to S-band
"""
# Set coefficients for conversion from Ku-band to S-band
# Rain 90% 80% 70% 60% 50% 40% 30% 20% 10% Snow
as0 = [4.78e-2, 4.12e-2, 8.12e-2, 1.59e-1, 2.87e-1, 4.93e-1, 8.16e-1, 1.31e+0,
2.01e+0, 2.82e+0, 1.74e-1]
as1 = [1.23e-2, 3.66e-3, 2.00e-3, 9.42e-4, 5.29e-4, 5.96e-4, 1.22e-3, 2.11e-3,
3.34e-3, 5.33e-3, 1.35e-2]
as2 = [-3.50e-4, 1.17e-3, 1.04e-3, 8.16e-4, 6.59e-4, 5.85e-4, 6.13e-4, 7.01e-4,
8.24e-4, 1.01e-3, -1.38e-3]
as3 = [-3.30e-5, -8.08e-5, -6.44e-5, -4.97e-5, -4.15e-5, -3.89e-5, -4.15e-5,
-4.58e-5, -5.06e-5, -5.78e-5, 4.74e-5]
as4 = [4.27e-7, 9.25e-7, 7.41e-7, 6.13e-7, 5.80e-7, 6.16e-7, 7.12e-7, 8.22e-7,
9.39e-7, 1.10e-6, 0]
# Rain 90% 80% 70% 60% 50% 40% 30% 20% 10% Hail
ah0 = [4.78e-2, 1.80e-1, 1.95e-1, 1.88e-1, 2.36e-1, 2.70e-1, 2.98e-1, 2.85e-1,
1.75e-1, 4.30e-2, 8.80e-2]
ah1 = [1.23e-2, -3.73e-2, -3.83e-2, -3.29e-2, -3.46e-2, -2.94e-2, -2.10e-2,
-9.96e-3, -8.05e-3, -8.27e-3, 5.39e-2]
ah2 = [-3.50e-4, 4.08e-3, 4.14e-3, 3.75e-3, 3.71e-3, 3.22e-3, 2.44e-3, 1.45e-3,
1.21e-3, 1.66e-3, -2.99e-4]
ah3 = [-3.30e-5, -1.59e-4, -1.54e-4, -1.39e-4, -1.30e-4, -1.12e-4, -8.56e-5,
-5.33e-5, -4.66e-5, -7.19e-5, 1.90e-5]
ah4 = [4.27e-7, 1.59e-6, 1.51e-6, 1.37e-6, 1.29e-6, 1.15e-6, 9.40e-7, 6.71e-7,
6.33e-7, 9.52e-7, 0]
refp_ss = np.zeros(refp.shape) + np.NaN # snow
refp_sh = np.zeros(refp.shape) + np.NaN # hail
zmlt = zbb + bbwidth / 2. # APPROXIMATION!
zmlb = zbb - bbwidth / 2. # APPROXIMATION!
ratio = (zp - zmlb) / (zmlt - zmlb)
iax, iay = np.where(ratio >= 1)
# above melting layer
if len(iax) > 0:
dfrs = as0[10] + as1[10] * refp[iax, iay] + as2[10] * refp[iax, iay] ** 2 + as3[
10] * refp[iax, iay] ** 3 + as4[10] * refp[iax, iay] ** 4
dfrh = ah0[10] + ah1[10] * refp[iax, iay] + ah2[10] * refp[iax, iay] ** 2 + ah3[
10] * refp[iax, iay] ** 3 + ah4[10] * refp[iax, iay] ** 4
refp_ss[iax, iay] = refp[iax, iay] + dfrs
refp_sh[iax, iay] = refp[iax, iay] + dfrh
ibx, iby = np.where(ratio <= 0)
if len(ibx) > 0: # below the melting layer
dfrs = as0[0] + as1[0] * refp[ibx, iby] + as2[0] * refp[ibx, iby] ** 2 + as3[
0] * refp[ibx, iby] ** 3 + as4[0] * refp[ibx, iby] ** 4
dfrh = ah0[0] + ah1[0] * refp[ibx, iby] + ah2[0] * refp[ibx, iby] ** 2 + ah3[
0] * refp[ibx, iby] ** 3 + ah4[0] * refp[ibx, iby] ** 4
refp_ss[ibx, iby] = refp[ibx, iby] + dfrs
refp_sh[ibx, iby] = refp[ibx, iby] + dfrh
imx, imy = np.where((ratio > 0) & (ratio < 1))
if len(imx) > 0: # within the melting layer
ind = np.round(ratio[imx, imy]).astype(int)[0]
dfrs = as0[ind] + as1[ind] * refp[imx, imy] + as2[ind] * refp[imx, imy] ** 2 + \
as3[ind] * refp[imx, imy] ** 3 + as4[ind] * refp[imx, imy] ** 4
dfrh = ah0[ind] + ah1[ind] * refp[imx, imy] + ah2[ind] * refp[imx, imy] ** 2 + \
ah3[ind] * refp[imx, imy] ** 3 + ah4[ind] * refp[imx, imy] ** 4
refp_ss[imx, imy] = refp[imx, imy] + dfrs
refp_sh[imx, imy] = refp[imx, imy] + dfrh
# Jackson Tan's fix for C-band
if radar_band == 'C':
deltas = 5.3 / 10.0 * (refp_ss - refp)
refp_ss = refp + deltas
deltah = 5.3 / 10.0 * (refp_sh - refp)
refp_sh = refp + deltah
elif radar_band == 'X':
deltas = 3.2 / 10.0 * (refp_ss - refp)
refp_ss = refp + deltas
deltah = 3.2 / 10.0 * (refp_sh - refp)
refp_sh = refp + deltah
return refp_ss, refp_sh
def convert_to_Ku(refg, zg, zbb, radar_band='S'):
'''
From Liao and Meneghini (2009)
Parameters
==========
refg:
Ground radar reflectivity field.
zg:
Altitude.
zbb:
Bright band height.
bbwidth:
Bright band width.
radar_band: str
Possible values are 'S', 'C', or 'X'
Returns