xarray GAMIC backend#

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

[1]:
import glob
import wradlib as wrl
import warnings

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

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

Load ODIM_H5 Volume Data#

[2]:
fpath = "hdf5/DWD-Vol-2_99999_20180601054047_00.h5"
f = wrl.util.get_wradlib_data_file(fpath)
vol = xd.io.open_gamic_datatree(f)
Downloading file 'hdf5/DWD-Vol-2_99999_20180601054047_00.h5' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/hdf5/DWD-Vol-2_99999_20180601054047_00.h5' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

Inspect RadarVolume#

[3]:
display(vol)
<xarray.DatasetView>
Dimensions:              ()
Data variables:
    volume_number        int64 0
    platform_type        <U5 'fixed'
    instrument_type      <U5 'radar'
    time_coverage_start  <U20 '2018-06-01T05:40:47Z'
    time_coverage_end    <U20 '2018-06-01T05:44:16Z'
    longitude            float64 6.457
    altitude             float64 310.0
    latitude             float64 50.93
Attributes:
    Conventions:      None
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using xradar
    instrument_name:  None

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.DatasetView>
Dimensions:              ()
Data variables:
    volume_number        int64 0
    platform_type        <U5 'fixed'
    instrument_type      <U5 'radar'
    time_coverage_start  <U20 '2018-06-01T05:40:47Z'
    time_coverage_end    <U20 '2018-06-01T05:44:16Z'
    longitude            float64 6.457
    altitude             float64 310.0
    latitude             float64 50.93
Attributes:
    Conventions:      None
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using xradar
    instrument_name:  None

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["sweep_0"])
<xarray.DatasetView>
Dimensions:            (azimuth: 361, range: 360)
Coordinates:
  * azimuth            (azimuth) float64 0.5219 1.519 2.53 ... 357.5 358.5 359.5
    elevation          (azimuth) float64 ...
    time               (azimuth) datetime64[ns] 2018-06-01T05:40:57.362999808...
  * range              (range) float32 50.0 150.0 250.0 ... 3.585e+04 3.595e+04
    longitude          float64 ...
    latitude           float64 ...
    altitude           float64 ...
Data variables: (12/17)
    DBZH               (azimuth, range) float32 ...
    DBZV               (azimuth, range) float32 ...
    KDP                (azimuth, range) float32 ...
    RHOHV              (azimuth, range) float32 ...
    DBTH               (azimuth, range) float32 ...
    DBTV               (azimuth, range) float32 ...
    ...                 ...
    PHIDP              (azimuth, range) float32 ...
    sweep_mode         <U20 ...
    sweep_number       int64 ...
    prt_mode           <U7 ...
    follow_mode        <U7 ...
    sweep_fixed_angle  float64 ...

Goereferencing#

[6]:
swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.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.wrl.vis.plot(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f40dda9f3d0>
../../_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.wrl.vis.plot(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.wrl.vis.plot(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7f40dd884750>
../../_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).wrl.vis.plot(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7f40dd9ed0d0>
../../_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.wrl.vis.plot(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7f40dd8d2a50>
../../_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.wrl.vis.plot(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7f40ddba9510>
../../_images/notebooks_fileio_wradlib_gamic_backend_21_1.png
[15]:
swp.DBZH.wrl.vis.plot()
[15]:
<matplotlib.collections.QuadMesh at 0x7f40dd584790>
../../_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: 361, 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: (12/14)
  * azimuth     (azimuth) float64 0.5219 1.519 2.53 3.527 ... 357.5 358.5 359.5
    elevation   (azimuth) float64 28.0 28.0 28.0 28.0 ... 28.0 28.0 28.0 28.0
    time        (azimuth) datetime64[ns] 2018-06-01T05:40:57.362999808 ... 20...
  * range       (range) float32 50.0 150.0 250.0 ... 3.585e+04 3.595e+04
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 6.457
    ...          ...
    x           (azimuth, range) float64 0.4021 1.206 2.01 ... -266.5 -267.3
    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 44.11 132.4 ... 3.159e+04 3.168e+04
    rays        (azimuth, range) float64 0.5219 0.5219 0.5219 ... 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
    long_name:      Equivalent reflectivity factor H
    standard_name:  radar_equivalent_reflectivity_factor_h
    units:          dBZ
    _Undetect:      0.0
    coordinates:    elevation azimuth range latitude longitude altitude time ...

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.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7f40ddb4a8d0>
../../_images/notebooks_fileio_wradlib_gamic_backend_26_1.png
[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(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 0x7f40dd512990>
../../_images/notebooks_fileio_wradlib_gamic_backend_29_1.png

Export to ODIM and CfRadial2#

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

Import again#

[21]:
vola = xd.io.open_odim_datatree("gamic_as_odim.h5")
display(vola)
<xarray.DatasetView>
Dimensions:              ()
Data variables:
    volume_number        int64 0
    platform_type        <U5 'fixed'
    instrument_type      <U5 'radar'
    time_coverage_start  <U20 '2018-06-01T05:40:47Z'
    time_coverage_end    <U20 '2018-06-01T05:44:16Z'
    longitude            float64 6.457
    altitude             float64 310.0
    latitude             float64 50.93
Attributes:
    Conventions:      ODIM_H5/V2_2
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using xradar
    instrument_name:  None