xarray Rainbow5 backend#

In this example, we read Rainbow5 data files using the xradar rainbow xarray backend.

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

warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
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:
    plt.ion()
/home/runner/micromamba/envs/wradlib-tests/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "

Load Rainbow5 Volume Data#

[2]:
fpath = "rainbow/2013051000000600dBZ.vol"
f = wrl.util.get_wradlib_data_file(fpath)
vol = xd.io.open_rainbow_datatree(f, reindex_angle=False)
Downloading file 'rainbow/2013051000000600dBZ.vol' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/rainbow/2013051000000600dBZ.vol' 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 '2013-05-10T00:00:06Z'
    time_coverage_end    <U20 '2013-05-10T00:03:14Z'
    longitude            float64 6.38
    altitude             float64 116.7
    latitude             float64 50.86
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 '2013-05-10T00:00:06Z'
    time_coverage_end    <U20 '2013-05-10T00:03:14Z'
    longitude            float64 6.38
    altitude             float64 116.7
    latitude             float64 50.86
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: 400)
Coordinates:
    elevation          (azimuth) float64 ...
  * range              (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time               (azimuth) datetime64[ns] 2013-05-10T00:00:15.500000 .....
    longitude          float64 ...
    latitude           float64 ...
    altitude           float64 ...
  * azimuth            (azimuth) float64 0.5055 1.549 2.505 ... 358.5 359.5
Data variables:
    DBZH               (azimuth, range) float32 ...
    sweep_mode         <U20 ...
    sweep_number       int64 ...
    prt_mode           <U7 ...
    follow_mode        <U7 ...
    sweep_fixed_angle  float64 ...

Georeferencing#

[6]:
swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.wrl.georef.georeference()

Inspect radar moments#

The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset.

[7]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 361, range: 400)>
[144400 values with dtype=float32]
Coordinates: (12/15)
    elevation   (azimuth) float64 0.6 0.6 0.6 0.6 0.6 ... 0.6 0.6 0.6 0.6 0.6
  * range       (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time        (azimuth) datetime64[ns] 2013-05-10T00:00:15.500000 ... 2013-...
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 6.38
    latitude    float64 50.86
    ...          ...
    y           (azimuth, range) float64 125.0 375.0 ... 9.96e+04 9.985e+04
    z           (azimuth, range) float64 118.0 120.6 ... 1.744e+03 1.75e+03
    gr          (azimuth, range) float64 124.8 374.7 ... 9.96e+04 9.985e+04
    rays        (azimuth, range) float64 0.5055 0.5055 0.5055 ... 359.5 359.5
    bins        (azimuth, range) float32 125.0 375.0 ... 9.962e+04 9.988e+04
    crs_wkt     int64 0
Attributes:
    long_name:      Equivalent reflectivity factor H
    units:          dBZ
    standard_name:  radar_equivalent_reflectivity_factor_h

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.

For more details on plotting radar data see under Visualization.

[8]:
swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
[8]:
<matplotlib.collections.QuadMesh at 0x7f2be051e590>
../../../_images/notebooks_fileio_backends_rainbow_backend_16_1.png
[9]:
fig = plt.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(crs={"latmin": 3e3}, fig=fig)
../../../_images/notebooks_fileio_backends_rainbow_backend_17_0.png

Retrieve explicit group#

[10]:
swp_b = xr.open_dataset(
    f, engine="rainbow", group="sweep_5", backend_kwargs=dict(reindex_angle=False)
)
display(swp_b)
<xarray.Dataset>
Dimensions:            (azimuth: 361, range: 400)
Coordinates:
    elevation          (azimuth) float64 ...
  * range              (range) float32 125.0 375.0 625.0 ... 9.962e+04 9.988e+04
    time               (azimuth) datetime64[ns] ...
    longitude          float64 ...
    latitude           float64 ...
    altitude           float64 ...
  * azimuth            (azimuth) float64 0.522 1.505 2.516 ... 357.5 358.5 359.5
Data variables:
    DBZH               (azimuth, range) float32 ...
    sweep_mode         <U20 ...
    sweep_number       int64 ...
    prt_mode           <U7 ...
    follow_mode        <U7 ...
    sweep_fixed_angle  float64 ...