xarray GAMIC backend

In this example, we read GAMIC (HDF5) data files using the xarray gamic backend.

[1]:
import glob
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().magic("matplotlib inline")
except:
    pl.ion()
/home/runner/micromamba-root/envs/wradlib-notebooks/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 ODIM_H5 Volume Data

[2]:
fpath = "hdf5/DWD-Vol-2_99999_20180601054047_00.h5"
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_gamic_dataset(f)

Inspect RadarVolume

[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 10)
Elevation(s): (28.0, 18.0, 14.0, 11.0, 8.2, 6.0, 4.5, 3.1, 1.7, 0.6)

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: 10)
Coordinates:
    time                 datetime64[ns] 2018-06-01T05:40:47.040999936
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 6.457
    altitude             float64 310.0
    latitude             float64 50.93
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 '2018-06-01T05:40:47Z'
    time_coverage_end    <U20 '2018-06-01T05:44:16Z'
    sweep_group_name     (sweep) <U7 'sweep_0' 'sweep_1' ... 'sweep_8' 'sweep_9'
    sweep_fixed_angle    (sweep) float64 28.0 18.0 14.0 11.0 ... 4.5 3.1 1.7 0.6
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      28.0

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: 360, range: 360)
Coordinates:
  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 50.0 150.0 250.0 ... 3.585e+04 3.595e+04
    elevation   (azimuth) float64 ...
    rtime       (azimuth) datetime64[ns] 2018-06-01T05:40:57.362999808 ... 20...
    time        datetime64[ns] 2018-06-01T05:40:47.040999936
    sweep_mode  <U20 ...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
Data variables:
    DBZH        (azimuth, range) float32 ...
    DBZV        (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    DBTH        (azimuth, range) float32 ...
    DBTV        (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    VRADV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    WRADV       (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
Attributes:
    fixed_angle:  28.0

Goereferencing

[6]:
swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)

Plotting

[7]:
swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")
../../_images/notebooks_fileio_wradlib_gamic_backend_14_0.png
[8]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f1d529f6ed0>
../../_images/notebooks_fileio_wradlib_gamic_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.DBZH.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)
< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=6.4569489 +lat_0=50.9287272 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
../../_images/notebooks_fileio_wradlib_gamic_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.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7f1d4a90a210>
../../_images/notebooks_fileio_wradlib_gamic_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)

DBZH = swp.DBZH
pm = DBZH.where(DBZH > 0).wradlib.plot_ppi(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7f1d4a636fd0>
../../_images/notebooks_fileio_wradlib_gamic_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.DBZH.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7f1d4a69d450>
../../_images/notebooks_fileio_wradlib_gamic_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.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7f1d4837a810>
../../_images/notebooks_fileio_wradlib_gamic_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f1d4a779990>
../../_images/notebooks_fileio_wradlib_gamic_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 ODIM_H5 standard.

[16]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 360, range: 360)>
array([[13.177166, 11.671261, 19.200787, ...,       nan,       nan,       nan],
       [11.169292, 11.671261, 17.192913, ...,       nan,       nan,       nan],
       [12.173229, 11.671261, 19.702755, ...,       nan,       nan,       nan],
       ...,
       [10.165356, 11.169292, 19.702755, ...,       nan,       nan,       nan],
       [11.169292, 11.671261, 16.188976, ...,       nan,       nan,       nan],
       [12.173229, 12.675198, 19.200787, ...,       nan,       nan,       nan]],
      dtype=float32)
Coordinates: (12/15)
  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 50.0 150.0 250.0 ... 3.585e+04 3.595e+04
    elevation   (azimuth) float64 28.0 28.0 28.0 28.0 ... 28.0 28.0 28.0 28.0
    rtime       (azimuth) datetime64[ns] 2018-06-01T05:40:57.362999808 ... 20...
    time        datetime64[ns] 2018-06-01T05:40:47.040999936
    sweep_mode  <U20 'azimuth_surveillance'
    ...          ...
    x           (azimuth, range) float64 0.3852 1.156 1.926 ... -275.7 -276.4
    y           (azimuth, range) float64 44.14 132.4 ... 3.159e+04 3.168e+04
    z           (azimuth, range) float64 333.5 380.4 ... 1.72e+04 1.725e+04
    gr          (azimuth, range) float64 44.15 132.4 ... 3.159e+04 3.168e+04
    rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
    bins        (azimuth, range) float32 50.0 150.0 ... 3.585e+04 3.595e+04
Attributes:
    format:         UV8
    is_dft:         0
    unit:           dBZ
    units:          dBZ
    standard_name:  radar_equivalent_reflectivity_factor_h
    long_name:      Equivalent reflectivity factor H
    _Undetect:      0.0

Create simple plot

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

[17]:
swp.DBZH.sortby("rtime").plot(x="range", y="rtime", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7f1d483e8710>
../../_images/notebooks_fileio_wradlib_gamic_backend_26_1.png
[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_gamic_backend_27_0.png

Mask some values

[19]:
swp["DBZH"] = swp["DBZH"].where(swp["DBZH"] >= 0)
swp["DBZH"].plot()
[19]:
<matplotlib.collections.QuadMesh at 0x7f1d48259d50>
../../_images/notebooks_fileio_wradlib_gamic_backend_29_1.png

Export to ODIM and CfRadial2

[20]:
vol.to_odim("gamic_as_odim.h5")
vol.to_cfradial2("gamic_as_cfradial2.nc")

Import again

[21]:
vola = wrl.io.open_odim_dataset("gamic_as_odim.h5")
[22]:
volb = wrl.io.open_cfradial2_dataset("gamic_as_cfradial2.nc")

Check equality

We have to drop the time variable when checking equality since GAMIC has millisecond resolution, ODIM has seconds.

[23]:
xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(
    vol[0].drop(["rtime", "time"]), vola[0].drop(["rtime", "time"])
)
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
xr.testing.assert_equal(vol[0].drop("time"), volb[0].drop("time"))
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop("rtime"), volb[0].drop("rtime"))

More GAMIC loading mechanisms

Use xr.open_dataset to retrieve explicit group

Warning

Since \(\omega radlib\) version 1.18 the xarray backend engines for polar radar data have been renamed and prepended with wradlib- (eg. gamic -> wradlib-gamic). This was necessary to avoid clashes with the new xradar-package, which will eventually replace the wradlib engines. Users have to make sure to check which engine to use for their use-case when using xarray.open_dataset. Users might install and test xradar, and check if it is already robust enough for their use-cases (by using xradar’s engine="gamic".

[24]:
swp = xr.open_dataset(f, engine="wradlib-gamic", group="scan9")
display(swp)
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 1000)
Coordinates:
  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * range       (range) float32 75.0 225.0 375.0 ... 1.498e+05 1.499e+05
    elevation   (azimuth) float64 ...
    rtime       (azimuth) datetime64[ns] ...
    time        datetime64[ns] ...
    sweep_mode  <U20 ...
    longitude   float64 ...
    latitude    float64 ...
    altitude    float64 ...
Data variables:
    DBZH        (azimuth, range) float32 ...
    DBZV        (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    DBTH        (azimuth, range) float32 ...
    DBTV        (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    VRADV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    WRADV       (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.6