xarray CfRadial2 backend#

In this example, we read CfRadial2 data files using the xarray cfradial2 backend.

[1]:

import wradlib as wrl
import warnings

warnings.filterwarnings("ignore")
import matplotlib.pyplot as pl
import numpy as np
import xarray as xr

try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    pl.ion()

/home/runner/micromamba-root/envs/wradlib-tests/lib/python3.11/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Load CfRadial2 Volume Data#

[2]:

fpath = "netcdf/cfrad.20080604_002217_000_SPOL_v36_SUR_cfradial2.nc"
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_cfradial2_dataset(f)

Downloading file 'netcdf/cfrad.20080604_002217_000_SPOL_v36_SUR_cfradial2.nc' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/netcdf/cfrad.20080604_002217_000_SPOL_v36_SUR_cfradial2.nc' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

Inspect RadarVolume#

[3]:

display(vol)

<wradlib.RadarVolume>
Dimension(s): (sweep: 9)
Elevation(s): (0.5, 1.1, 1.8, 2.6, 3.6, 4.7, 6.5, 9.1, 12.8)

Inspect root group#

The sweep dimension contains the number of scans in this radar volume. Further the dataset consists of variables (location coordinates, time_coverage) and attributes (Conventions, metadata).

[4]:

vol.root

[4]:

<xarray.Dataset>
Dimensions:              (sweep: 9)
Dimensions without coordinates: sweep
Data variables:
    volume_number        int64 0
    platform_type        <U5 'fixed'
    instrument_type      <U5 'radar'
    primary_axis         <U6 'axis_z'
    time_coverage_start  <U20 '2008-06-04T00:15:03Z'
    time_coverage_end    <U20 '2008-06-04 00:22:16Z'
    latitude             float64 ...
    longitude            float64 ...
    altitude             float64 ...
    sweep_group_name     (sweep) <U7 'sweep_0' 'sweep_1' ... 'sweep_7' 'sweep_8'
    sweep_fixed_angle    (sweep) float32 0.4999 1.099 1.802 ... 6.498 9.102 12.8
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.5

Inspect sweep group(s)#

The sweep-groups can be accessed via their respective keys. The dimensions consist of range and time with added coordinates azimuth, elevation, range and time. There will be variables like radar moments (DBZH etc.) and sweep-dependend metadata (like fixed_angle, sweep_mode etc.).

[5]:

display(vol[0])

<xarray.Dataset>
Dimensions:             (azimuth: 480, range: 996)
Coordinates:
    sweep_mode          <U20 ...
    rtime               (azimuth) datetime64[ns] 2008-06-04T00:15:34 ... 2008...
  * range               (range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
  * azimuth             (azimuth) float32 0.0 0.75 1.5 ... 357.8 358.5 359.2
    elevation           (azimuth) float32 ...
    longitude           float64 ...
    latitude            float64 ...
    altitude            float64 ...
    time                datetime64[ns] 2008-06-04T00:15:03
Data variables: (12/16)
    sweep_number        int32 ...
    polarization_mode   |S32 ...
    prt_mode            |S32 ...
    follow_mode         |S32 ...
    sweep_fixed_angle   float32 0.4999
    target_scan_rate    float32 ...
    ...                  ...
    antenna_transition  (azimuth) int8 ...
    n_samples           (azimuth) int32 ...
    r_calib_index       (azimuth) int8 ...
    scan_rate           (azimuth) float32 ...
    DBZ                 (azimuth, range) float32 ...
    VR                  (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.5

Goereferencing#

[6]:

swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)

Plotting#

[7]:

swp.DBZ.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")
../../_images/notebooks_fileio_wradlib_cfradial2_backend_14_0.png 
[8]:

fig = pl.figure(figsize=(10, 10))
swp.DBZ.wradlib.plot_ppi(proj="cg", fig=fig)

[8]:

<matplotlib.collections.QuadMesh at 0x7f8a26c99590>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_15_1.png 
[9]:

import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature

map_trans = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)

[10]:

map_proj = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)
pm = swp.DBZ.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)

< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=120.43350219726562 +lat_0=22.52669906616211 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
../../_images/notebooks_fileio_wradlib_cfradial2_backend_17_1.png 
[11]:

map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection=map_proj)
pm = swp.DBZ.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)

[11]:

<cartopy.mpl.gridliner.Gridliner at 0x7f8a24ba6450>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_18_1.png 
[12]:

import cartopy.feature as cfeature


def plot_borders(ax):
    borders = cfeature.NaturalEarthFeature(
        category="physical", name="coastline", scale="10m", facecolor="none"
    )
    ax.add_feature(borders, edgecolor="black", lw=2, zorder=4)


map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection=map_proj)

DBZ = swp.DBZ
pm = DBZ.where(DBZ > 0).wradlib.plot_ppi(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)

[12]:

<cartopy.mpl.gridliner.Gridliner at 0x7f8a26d2f990>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_19_1.png 
[13]:

import matplotlib.path as mpath

theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)

map_proj = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values,
    central_longitude=swp.longitude.values,
)
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection=map_proj)
ax.set_boundary(circle, transform=ax.transAxes)

pm = swp.DBZ.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)

[13]:

<cartopy.mpl.gridliner.Gridliner at 0x7f8a24ac5cd0>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_20_1.png 
[14]:

fig = pl.figure(figsize=(10, 8))
proj = ccrs.AzimuthalEquidistant(
    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
)
ax = fig.add_subplot(111, projection=proj)
pm = swp.DBZ.wradlib.plot_ppi(ax=ax)
ax.gridlines()

[14]:

<cartopy.mpl.gridliner.Gridliner at 0x7f8a24bacf50>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_21_1.png 
[15]:

swp.DBZ.wradlib.plot_ppi()

[15]:

<matplotlib.collections.QuadMesh at 0x7f8a24ab2750>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_22_1.png

Inspect radar moments#

The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset. There are attributes connected which are defined by Cf/Radial standard.

[16]:

display(swp.DBZ)

<xarray.DataArray 'DBZ' (azimuth: 480, range: 996)>
array([[ 20.699957,  39.96934 ,  29.650644, ...,  -2.799595,  -3.549335,
         -1.650112],
       [ 13.829709,  35.710747,   8.869345, ..., -18.780428,  -3.080303,
         -4.519378],
       [ -9.129745,  14.810412,   4.539685, ...,   0.179822,  -0.550375,
         -3.519132],
       ...,
       [  5.889927,  26.049406,  32.379555, ...,  -2.550866,  -1.060269,
         -1.900617],
       [  0.959765,  23.579884,   9.29929 , ...,  -8.680257,  -5.039932,
         -2.410512],
       [ 20.079912,  39.15031 ,  13.190121, ...,  -4.91912 ,  -3.160252,
         -1.319658]], dtype=float32)
Coordinates: (12/15)
    sweep_mode  <U20 'azimuth_surveillance'
    rtime       (azimuth) datetime64[ns] 2008-06-04T00:15:34 ... 2008-06-04T0...
  * range       (range) float32 150.0 300.0 450.0 ... 1.492e+05 1.494e+05
  * azimuth     (azimuth) float32 0.0 0.75 1.5 2.25 ... 357.0 357.8 358.5 359.2
    elevation   (azimuth) float32 0.5164 0.5219 0.5164 ... 0.5219 0.5219 0.5219
    longitude   float64 120.4
    ...          ...
    x           (azimuth, range) float32 -6.556e-06 -1.311e-05 ... -1.955e+03
    y           (azimuth, range) float32 150.0 300.0 ... 1.492e+05 1.493e+05
    z           (azimuth, range) float32 46.0 47.0 48.0 ... 2.714e+03 2.718e+03
    gr          (azimuth, range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
    rays        (azimuth, range) float32 0.0 0.0 0.0 0.0 ... 359.2 359.2 359.2
    bins        (azimuth, range) float32 150.0 300.0 ... 1.492e+05 1.494e+05
Attributes:
    long_name:             Computed Horizontal Co-polar Reflectivit
    standard_name:         equivalent_reflectivity_factor
    units:                 dBZ
    threshold_field_name:
    threshold_value:       -9999.0
    sampling_ratio:        1.0
    grid_mapping:          grid_mapping

Create simple plot#

Using xarray features a simple plot can be created like this. Note the sortby('time') method, which sorts the radials by time.

[17]:

swp.DBZ.sortby("rtime").plot(x="range", y="rtime", add_labels=False)

[17]:

<matplotlib.collections.QuadMesh at 0x7f8a2478b050>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_26_1.png 
[18]:

fig = pl.figure(figsize=(5, 5))
pm = swp.DBZ.wradlib.plot_ppi(proj={"latmin": 33e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_cfradial2_backend_27_0.png

Mask some values#

[19]:

swp["DBZ"] = swp["DBZ"].where(swp["DBZ"] >= 0)
swp["DBZ"].plot()

[19]:

<matplotlib.collections.QuadMesh at 0x7f8a247f7550>
../../_images/notebooks_fileio_wradlib_cfradial2_backend_29_1.png

Export to ODIM and CfRadial2#

[20]:

vol.to_odim("cfradial2_as_odim.h5")
vol.to_cfradial2("cfradial2_as_cfradial2.nc")

Import again#

[21]:

vola = wrl.io.open_odim_dataset(
    "cfradial2_as_odim.h5",
    decode_coords=True,
    backend_kwargs=dict(keep_azimuth=True, keep_elevation=True, reindex_angle=False),
)

[22]:

volb = wrl.io.open_cfradial2_dataset("cfradial2_as_cfradial2.nc")

Check equality#

Some variables need to be dropped, since they are not exported to the other standards or differ slightly (eg. re-indexed ray times).

[23]:

drop = set(vol[0]) ^ set(vola[0]) | set({"rtime"})
xr.testing.assert_allclose(vol.root, vola.root)
xr.testing.assert_allclose(
    vol[0].drop_vars(drop), vola[0].drop_vars(drop, errors="ignore")
)
xr.testing.assert_allclose(vol.root, volb.root)
xr.testing.assert_equal(vol[0], volb[0])
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(
    vola[0].drop_vars(drop, errors="ignore"), volb[0].drop_vars(drop, errors="ignore")
)