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")

[8]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wradlib.plot_ppi(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f1d529f6ed0>

[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 >

[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>

[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>

[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>

[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>

[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f1d4a779990>

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>

[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wradlib.plot_ppi(proj={"latmin": 3e3}, fig=fig)

Mask some values¶
[19]:
swp["DBZH"] = swp["DBZH"].where(swp["DBZH"] >= 0)
swp["DBZH"].plot()
[19]:
<matplotlib.collections.QuadMesh at 0x7f1d48259d50>

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