xarray ODIM backend

In this example, we read ODIM_H5 (HDF5) data files using the xarray odim backend.

[1]:
import glob
import os
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()
from wradlib.io import open_odim_dataset

Load ODIM_H5 Volume Data

[2]:
fpath = 'hdf5/knmi_polar_volume.h5'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_odim_dataset(f)

Inspect RadarVolume

[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 14)
Elevation(s): (0.3, 0.4, 0.8, 1.1, 2.0, 3.0, 4.5, 6.0, 8.0, 10.0, 12.0, 15.0, 20.0, 25.0)

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: 14)
Coordinates:
    time                 datetime64[ns] 2011-06-10T11:40:02
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 4.79
    altitude             float64 50.0
    latitude             float64 52.95
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 '2011-06-10T11:40:02Z'
    time_coverage_end    <U20 '2011-06-10T11:43:54Z'
    sweep_group_name     (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_13'
    sweep_fixed_angle    (sweep) float64 0.3 0.4 0.8 1.1 ... 12.0 15.0 20.0 25.0
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.30000001192092896

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: 320)
Coordinates:
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float32 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 0.3 0.3 0.3
    rtime       (azimuth) datetime64[ns] 2011-06-10T11:40:17.361118208 ... 20...
  * range       (range) float32 500.0 1.5e+03 2.5e+03 ... 3.185e+05 3.195e+05
    time        datetime64[ns] 2011-06-10T11:40:02
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 4.79
    latitude    float64 52.95
    altitude    float64 50.0
Data variables:
    DBZH        (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.30000001192092896

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_odim_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 0x7f29868cf700>
../../_images/notebooks_fileio_wradlib_odim_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: <cartopy.crs.AzimuthalEquidistant object at 0x7f297e4c8b80> >
../../_images/notebooks_fileio_wradlib_odim_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 0x7f297cc9ae80>
../../_images/notebooks_fileio_wradlib_odim_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 0x7f297cc2cfd0>
../../_images/notebooks_fileio_wradlib_odim_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 0x7f297c9e5ac0>
../../_images/notebooks_fileio_wradlib_odim_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 0x7f297c761100>
../../_images/notebooks_fileio_wradlib_odim_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f297c9c4e20>
../../_images/notebooks_fileio_wradlib_odim_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: 320)>
array([[ 22. ,  17. ,  -8. , ..., -31.5, -31.5, -31.5],
       [ 24. ,  24.5,  -9. , ..., -31.5, -31.5, -31.5],
       [ 35.5,  42. ,  12. , ..., -31.5, -31.5, -31.5],
       ...,
       [ 23. ,  14. , -13. , ..., -31.5, -31.5, -31.5],
       [ 23. ,  14. ,  -9. , ..., -31.5, -31.5, -31.5],
       [ 22. ,  18.5, -11.5, ..., -31.5, -31.5, -31.5]], dtype=float32)
Coordinates: (12/15)
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float32 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 0.3 0.3 0.3
    rtime       (azimuth) datetime64[ns] 2011-06-10T11:40:17.361118208 ... 20...
  * range       (range) float32 500.0 1.5e+03 2.5e+03 ... 3.185e+05 3.195e+05
    time        datetime64[ns] 2011-06-10T11:40:02
    sweep_mode  <U20 'azimuth_surveillance'
    ...          ...
    x           (azimuth, range) float32 4.363 13.09 ... -2.777e+03 -2.786e+03
    y           (azimuth, range) float32 500.0 1.5e+03 ... 3.183e+05 3.193e+05
    z           (azimuth, range) float32 53.0 58.0 64.0 ... 7.691e+03 7.734e+03
    gr          (azimuth, range) float32 500.0 1.5e+03 ... 3.183e+05 3.193e+05
    rays        (azimuth, range) float32 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
    bins        (azimuth, range) float32 500.0 1.5e+03 ... 3.185e+05 3.195e+05
Attributes:
    _Undetect:      [0.]
    units:          dBZ
    long_name:      Equivalent reflectivity factor H
    standard_name:  radar_equivalent_reflectivity_factor_h

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 0x7f297c924160>
../../_images/notebooks_fileio_wradlib_odim_backend_26_1.png
[18]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZH.wradlib.plot_ppi(proj={'latmin': 33e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_odim_backend_27_0.png

Mask some values

[19]:
swp['DBZH'] = swp['DBZH'].where(swp['DBZH'] >= 0)
swp['DBZH'].plot()
[19]:
<matplotlib.collections.QuadMesh at 0x7f297c8016a0>
../../_images/notebooks_fileio_wradlib_odim_backend_29_1.png

Export to ODIM and CfRadial2

[20]:
vol.to_odim('knmi_odim.h5')
vol.to_cfradial2('knmi_odim_as_cfradial.nc')

Import again

[21]:
vola = wrl.io.open_odim_dataset('knmi_odim.h5')
[22]:
volb = wrl.io.open_cfradial2_dataset('knmi_odim_as_cfradial.nc')

Check equality

[23]:
xr.testing.assert_allclose(vol.root, vola.root)
xr.testing.assert_equal(vol[0], vola[0])
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_equal(vola[0], volb[0])

More ODIM loading mechanisms

Use xr.open_dataset to retrieve explicit group

[24]:
swp = xr.open_dataset(f, engine="odim", group="dataset14")
display(swp)
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 240)
Coordinates:
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float32 25.0 25.0 25.0 25.0 ... 25.0 25.0 25.0 25.0
    rtime       (azimuth) datetime64[ns] 2011-06-10T11:43:48.763874560 ... 20...
  * range       (range) float32 250.0 750.0 1.25e+03 ... 1.192e+05 1.198e+05
    time        datetime64[ns] 2011-06-10T11:43:45
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 4.79
    latitude    float64 52.95
    altitude    float64 50.0
Data variables:
    DBZH        (azimuth, range) float32 -31.5 -0.5 0.0 ... -31.5 -31.5 -31.5
Attributes:
    fixed_angle:  25.0

Use xr.open_mfdataset to retrieve timeseries of explicit group

[25]:
fpath = os.path.join(wrl.util.get_wradlib_data_path(), "hdf5/71*.h5")
f = glob.glob(fpath)
ts = xr.open_mfdataset(f, engine="odim", concat_dim="time", combine="nested", group="dataset1")
display(ts)
<xarray.Dataset>
Dimensions:     (time: 2, azimuth: 360, range: 1200)
Coordinates:
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
    rtime       (time, azimuth) datetime64[ns] 2018-12-20T06:12:41.009703424 ...
  * range       (range) float32 125.0 375.0 625.0 ... 2.996e+05 2.999e+05
  * time        (time) datetime64[ns] 2018-12-20T06:12:28 2018-12-20T06:06:28
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 151.2
    latitude    float64 -33.7
    altitude    float64 195.0
Data variables:
    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    DBZH_CLEAN  (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADDH      (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    WRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    TH          (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    ZDR         (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    PHIDP       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    KDP         (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    SNRH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
Attributes:
    fixed_angle:  0.5

Use wrl.io.open_odim_mfdataset to retrieve volume timeseries

[26]:
fpath = os.path.join(wrl.util.get_wradlib_data_path(), "hdf5/71*.h5")
f = glob.glob(fpath)
ts = wrl.io.open_odim_mfdataset(f)
display(ts)
100%|██████████| 14/14 [00:02<00:00,  5.10it/s]
<wradlib.RadarVolume>
Dimension(s): (sweep: 14)
Elevation(s): (0.5, 0.9, 1.3, 1.8, 2.4, 3.1, 4.2, 5.6, 7.4, 10.0, 13.3, 17.9, 23.9, 32.0)
[27]:
display(ts[0])
<xarray.Dataset>
Dimensions:     (time: 2, azimuth: 360, range: 1200)
Coordinates:
  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
    rtime       (time, azimuth) datetime64[ns] 2018-12-20T06:12:41.009703424 ...
  * range       (range) float32 125.0 375.0 625.0 ... 2.996e+05 2.999e+05
  * time        (time) datetime64[ns] 2018-12-20T06:12:28 2018-12-20T06:06:28
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 151.2
    latitude    float64 -33.7
    altitude    float64 195.0
Data variables:
    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    DBZH_CLEAN  (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADDH      (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    WRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    TH          (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    ZDR         (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    PHIDP       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    KDP         (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    SNRH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
Attributes:
    fixed_angle:  0.5