xarray furuno backend

In this example, we read scn/scnx (furuno) data files using the wradlib furuno xarray backend.

Furuno Weather Radars generate binary files. The binary version depend on the radar type. This reader is able to consume SCN (format version 3) and SCNX (format version 10) files.

Uncompressed files are read via numpy.memmap with lazy-loading mechanism. Gzip compressed files are are opened, read into memory and processed using numpy.frombuffer.

Radar moments are read as packed data with 16-bit resolution and output as 32bit-floating point data.

[1]:
import glob
import gzip
import io
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()

Load furuno scn Data

Data provided by University of Graz, Austria.

[2]:
fpath = 'furuno/0080_20210730_160000_01_02.scn.gz'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_furuno_dataset(f, reindex_angle=False)

Inspect RadarVolume

[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 1)
Elevation(s): (7.8)

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: 1)
Coordinates:
    time                 datetime64[ns] 2021-07-30T16:00:00
    longitude            float64 15.45
    altitude             float64 407.9
    sweep_mode           <U20 'azimuth_surveillance'
    latitude             float64 47.08
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 '2021-07-30T16:00:00Z'
    time_coverage_end    <U20 '2021-07-30T16:00:14Z'
    sweep_group_name     (sweep) <U7 'sweep_0'
    sweep_fixed_angle    (sweep) float64 7.8
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      7.8

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: 1376, range: 602)
Coordinates:
  * azimuth     (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9
    elevation   (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8
  * range       (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
    time        datetime64[ns] 2021-07-30T16:00:00
    rtime       (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20...
    longitude   float64 15.45
    latitude    float64 47.08
    altitude    float64 407.9
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  7.8

Goereferencing

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

Plotting

Currently the data dynamic range is left as read from the file. That way the difference between shortpulse and longpulse can be clearly seen.

[7]:
swp.DBZH.plot.pcolormesh(x='x', y='y')
pl.gca().set_aspect('equal')
../../_images/notebooks_fileio_wradlib_furuno_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 0x7fc3d5681c00>
../../_images/notebooks_fileio_wradlib_furuno_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=15.44729 +lat_0=47.07734000000001 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
../../_images/notebooks_fileio_wradlib_furuno_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 0x7fc3d5336b00>
../../_images/notebooks_fileio_wradlib_furuno_backend_18_1.png
[12]:
import cartopy.feature as cfeature
def plot_rivers(ax):
    rivers = cfeature.NaturalEarthFeature(category="physical",
                                           name="rivers_lake_centerlines",
                                           scale="10m",
                                           facecolor='none')
    ax.add_feature(rivers, edgecolor='blue', 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_rivers(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc3d51d3a60>
../../_images/notebooks_fileio_wradlib_furuno_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 0x7fc3d4d9e740>
../../_images/notebooks_fileio_wradlib_furuno_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 0x7fc3d530afe0>
../../_images/notebooks_fileio_wradlib_furuno_backend_21_1.png
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fc3d52377f0>
../../_images/notebooks_fileio_wradlib_furuno_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.

[16]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 1376, range: 602)>
array([[       nan,        nan,        nan, ..., -70.70001 , -70.19    ,
        -70.07001 ],
       [       nan,        nan,        nan, ..., -70.98999 , -70.28    ,
        -70.26999 ],
       [       nan,        nan,        nan, ..., -70.78    , -70.26001 ,
        -70.31    ],
       ...,
       [       nan,        nan,        nan, ..., -70.04001 , -70.389984,
        -69.369995],
       [       nan,        nan,        nan, ..., -69.81    , -70.17001 ,
        -69.600006],
       [       nan,        nan,        nan, ..., -69.95999 , -69.98999 ,
        -69.98001 ]], dtype=float32)
Coordinates: (12/15)
  * azimuth     (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9
    elevation   (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8
  * range       (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
    time        datetime64[ns] 2021-07-30T16:00:00
    rtime       (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20...
    longitude   float64 15.45
    ...          ...
    x           (azimuth, range) float32 0.09078 0.2723 0.4539 ... -31.13 -31.18
    y           (azimuth, range) float32 24.77 74.3 ... 2.973e+04 2.978e+04
    z           (azimuth, range) float32 411.0 418.0 ... 4.535e+03 4.542e+03
    gr          (azimuth, range) float32 24.77 74.3 ... 2.973e+04 2.978e+04
    rays        (azimuth, range) float32 0.21 0.21 0.21 ... 359.9 359.9 359.9
    bins        (azimuth, range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
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('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 0x7fc3d56fb2e0>
../../_images/notebooks_fileio_wradlib_furuno_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_furuno_backend_27_0.png

Mask some values

[19]:
dbzh = swp['DBZH'].where(swp['DBZH'] >= 0)
dbzh.plot(x="x", y="y")
[19]:
<matplotlib.collections.QuadMesh at 0x7fc3d4c36f80>
../../_images/notebooks_fileio_wradlib_furuno_backend_29_1.png
[20]:
vol[0]
[20]:
<xarray.Dataset>
Dimensions:     (azimuth: 1376, range: 602)
Coordinates:
  * azimuth     (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9
    elevation   (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8
  * range       (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
    time        datetime64[ns] 2021-07-30T16:00:00
    rtime       (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20...
    longitude   float64 15.45
    latitude    float64 47.08
    altitude    float64 407.9
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  7.8

Export to ODIM and CfRadial2

[21]:
vol[0].DBZH.sortby("rtime").plot(y="rtime")
[21]:
<matplotlib.collections.QuadMesh at 0x7fc3d4c9d450>
../../_images/notebooks_fileio_wradlib_furuno_backend_32_1.png
[22]:
vol.to_odim('furuno_scn_as_odim.h5')
vol.to_cfradial2('furuno_scn_as_cfradial2.nc')

Import again

[23]:
vola = wrl.io.open_odim_dataset('furuno_scn_as_odim.h5', reindex_angle=False, keep_elevation=True)
display(vola.root)
display(vola[0])
vola[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset>
Dimensions:              (sweep: 1)
Coordinates:
    time                 datetime64[ns] 2021-07-30T16:00:00
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 15.45
    altitude             float64 407.9
    latitude             float64 47.08
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 '2021-07-30T16:00:00Z'
    time_coverage_end    <U20 '2021-07-30T16:00:14Z'
    sweep_group_name     (sweep) <U7 'sweep_0'
    sweep_fixed_angle    (sweep) float64 7.8
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      7.8
<xarray.Dataset>
Dimensions:     (azimuth: 1376, range: 602)
Coordinates:
  * azimuth     (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9
    elevation   (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8
    rtime       (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723392 ... 20...
  * range       (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
    time        datetime64[ns] 2021-07-30T16:00:00
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 15.45
    latitude    float64 47.08
    altitude    float64 407.9
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
Attributes:
    fixed_angle:  7.8
[23]:
<matplotlib.collections.QuadMesh at 0x7fc3d4b54a90>
../../_images/notebooks_fileio_wradlib_furuno_backend_35_3.png
[24]:
volb = wrl.io.open_cfradial2_dataset('furuno_scn_as_cfradial2.nc')
display(volb.root)
display(volb[0])
volb[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset>
Dimensions:              (sweep: 1)
Coordinates:
    longitude            float64 15.45
    altitude             float64 407.9
    sweep_mode           <U20 'azimuth_surveillance'
    time                 datetime64[ns] 2021-07-30T16:00:00
    latitude             float64 47.08
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 '2021-07-30T16:00:00Z'
    time_coverage_end    <U20 '2021-07-30T16:00:14Z'
    sweep_group_name     (sweep) <U7 'sweep_0'
    sweep_fixed_angle    (sweep) float64 7.8
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      7.8
<xarray.Dataset>
Dimensions:     (azimuth: 1376, range: 602)
Coordinates:
  * azimuth     (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9
    elevation   (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8
  * range       (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
    rtime       (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20...
    longitude   float64 15.45
    latitude    float64 47.08
    altitude    float64 407.9
    sweep_mode  <U20 'azimuth_surveillance'
    time        datetime64[ns] 2021-07-30T16:00:00
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  7.8
[24]:
<matplotlib.collections.QuadMesh at 0x7fc3cb8a0430>
../../_images/notebooks_fileio_wradlib_furuno_backend_36_3.png

Check equality

We have to drop the time variable when checking equality since IRIS has millisecond resolution.

[25]:
xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(vol[0].drop(["rtime", "time", "QUAL"]), vola[0].drop(["rtime", "time"]))
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
#xr.testing.assert_allclose(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", "QUAL"]))

More Furuno loading mechanisms

Use xr.open_dataset to retrieve explicit group

[26]:
swp = xr.open_dataset(f, engine="furuno", group=1, backend_kwargs=dict(reindex_angle=False))
display(swp)
<xarray.Dataset>
Dimensions:     (azimuth: 1376, range: 602)
Coordinates:
  * azimuth     (azimuth) float32 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9
    elevation   (azimuth) float32 7.8 7.8 7.8 7.8 7.8 ... 7.8 7.8 7.8 7.8 7.8
  * range       (range) float32 25.0 75.0 125.0 ... 3.002e+04 3.008e+04
    time        datetime64[ns] 2021-07-30T16:00:00
    rtime       (azimuth) datetime64[ns] 2021-07-30T16:00:06.277723500 ... 20...
    longitude   float64 15.45
    latitude    float64 47.08
    altitude    float64 407.9
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  7.8

Load furuno scnx Data

Data provided by GFZ German Research Centre for Geosciences.

[27]:
fpath = 'furuno/2006_20220324_000000_000.scnx.gz'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_furuno_dataset(f, reindex_angle=False)

Inspect RadarVolume

[28]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 1)
Elevation(s): (0.5)

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).

[29]:
vol.root
[29]:
<xarray.Dataset>
Dimensions:              (sweep: 1)
Coordinates:
    time                 datetime64[ns] 2022-03-24T00:00:01
    longitude            float64 13.24
    altitude             float64 38.0
    sweep_mode           <U20 'azimuth_surveillance'
    latitude             float64 53.55
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 '2022-03-24T00:00:01Z'
    time_coverage_end    <U20 '2022-03-24T00:00:28Z'
    sweep_group_name     (sweep) <U7 'sweep_0'
    sweep_fixed_angle    (sweep) float64 0.5
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.5

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.).

[30]:
display(vol[0])
<xarray.Dataset>
Dimensions:     (azimuth: 722, range: 936)
Coordinates:
  * azimuth     (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7
    elevation   (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
    time        datetime64[ns] 2022-03-24T00:00:01
    rtime       (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20...
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  0.5

Goereferencing

[31]:
swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)

Plotting

Currently the data dynamic range is left as read from the file. That way the difference between shortpulse and longpulse can be clearly seen.

[32]:
swp.DBZH.plot.pcolormesh(x='x', y='y')
pl.gca().set_aspect('equal')
../../_images/notebooks_fileio_wradlib_furuno_backend_53_0.png
[33]:
fig = pl.figure(figsize=(10,10))
swp.DBZH.wradlib.plot_ppi(proj='cg', fig=fig)
[33]:
<matplotlib.collections.QuadMesh at 0x7fc3cb7ac3a0>
../../_images/notebooks_fileio_wradlib_furuno_backend_54_1.png
[34]:
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)
[35]:
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=13.243970000000001 +lat_0=53.55478 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
../../_images/notebooks_fileio_wradlib_furuno_backend_56_1.png
[36]:
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)
[36]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc3cb91cbb0>
../../_images/notebooks_fileio_wradlib_furuno_backend_57_1.png
[37]:
import cartopy.feature as cfeature
def plot_rivers(ax):
    rivers = cfeature.NaturalEarthFeature(category="physical",
                                           name="rivers_lake_centerlines",
                                           scale="10m",
                                           facecolor='none')
    ax.add_feature(rivers, edgecolor='blue', 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_rivers(ax)
ax.gridlines(draw_labels=True)
[37]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc3cb843820>
../../_images/notebooks_fileio_wradlib_furuno_backend_58_1.png
[38]:
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)
[38]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc3cb6b5240>
../../_images/notebooks_fileio_wradlib_furuno_backend_59_1.png
[39]:
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()
[39]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc3cb701570>
../../_images/notebooks_fileio_wradlib_furuno_backend_60_1.png
[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7fc3cb733d60>
../../_images/notebooks_fileio_wradlib_furuno_backend_61_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.

[41]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 722, range: 936)>
array([[       nan,        nan,        nan, ..., -80.740005, -79.34    ,
        -79.240005],
       [       nan,        nan,        nan, ..., -80.31    , -79.06    ,
        -79.25    ],
       [       nan,        nan,        nan, ..., -80.2     , -79.149994,
        -79.31999 ],
       ...,
       [       nan,        nan,        nan, ..., -79.78999 , -79.45999 ,
        -79.      ],
       [       nan,        nan,        nan, ..., -80.09    , -79.31    ,
        -79.020004],
       [       nan,        nan,        nan, ..., -80.369995, -79.33    ,
        -79.149994]], dtype=float32)
Coordinates: (12/15)
  * azimuth     (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7
    elevation   (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
    time        datetime64[ns] 2022-03-24T00:00:01
    rtime       (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20...
    longitude   float64 13.24
    ...          ...
    x           (azimuth, range) float32 0.1244 0.3731 0.6218 ... -403.6 -404.1
    y           (azimuth, range) float32 37.5 112.5 ... 7.008e+04 7.015e+04
    z           (azimuth, range) float32 38.0 39.0 39.0 ... 937.0 939.0 940.0
    gr          (azimuth, range) float32 37.53 112.5 ... 7.008e+04 7.015e+04
    rays        (azimuth, range) float32 0.19 0.19 0.19 ... 359.7 359.7 359.7
    bins        (azimuth, range) float32 37.5 112.5 ... 7.009e+04 7.016e+04
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('rtime') method, which sorts the radials by time.

[42]:
swp.DBZH.sortby('rtime').plot(x="range", y="rtime", add_labels=False)
[42]:
<matplotlib.collections.QuadMesh at 0x7fc3cb7741c0>
../../_images/notebooks_fileio_wradlib_furuno_backend_65_1.png
[43]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZH.wradlib.plot_ppi(proj={'latmin': 3e3}, fig=fig)
../../_images/notebooks_fileio_wradlib_furuno_backend_66_0.png

Mask some values

[44]:
dbzh = swp['DBZH'].where(swp['DBZH'] >= 0)
dbzh.plot(x="x", y="y")
[44]:
<matplotlib.collections.QuadMesh at 0x7fc3cb538460>
../../_images/notebooks_fileio_wradlib_furuno_backend_68_1.png
[45]:
vol[0]
[45]:
<xarray.Dataset>
Dimensions:     (azimuth: 722, range: 936)
Coordinates:
  * azimuth     (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7
    elevation   (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
    time        datetime64[ns] 2022-03-24T00:00:01
    rtime       (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20...
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  0.5

Export to ODIM and CfRadial2

[46]:
vol[0].DBZH.sortby("rtime").plot(y="rtime")
[46]:
<matplotlib.collections.QuadMesh at 0x7fc3cb38c7c0>
../../_images/notebooks_fileio_wradlib_furuno_backend_71_1.png
[47]:
vol.to_odim('furuno_scnx_as_odim.h5')
vol.to_cfradial2('furuno_scnx_as_cfradial2.nc')

Import again

[48]:
vola = wrl.io.open_odim_dataset('furuno_scnx_as_odim.h5', reindex_angle=False, keep_elevation=True)
display(vola.root)
display(vola[0])
vola[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset>
Dimensions:              (sweep: 1)
Coordinates:
    time                 datetime64[ns] 2022-03-24T00:00:01
    sweep_mode           <U20 'azimuth_surveillance'
    longitude            float64 13.24
    altitude             float64 38.0
    latitude             float64 53.55
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 '2022-03-24T00:00:01Z'
    time_coverage_end    <U20 '2022-03-24T00:00:28Z'
    sweep_group_name     (sweep) <U7 'sweep_0'
    sweep_fixed_angle    (sweep) float64 0.5
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.5
<xarray.Dataset>
Dimensions:     (azimuth: 722, range: 936)
Coordinates:
  * azimuth     (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7
    elevation   (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
    rtime       (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439552 ... 20...
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
    time        datetime64[ns] 2022-03-24T00:00:01
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
Attributes:
    fixed_angle:  0.5
[48]:
<matplotlib.collections.QuadMesh at 0x7fc3d4b27820>
../../_images/notebooks_fileio_wradlib_furuno_backend_74_3.png
[49]:
volb = wrl.io.open_cfradial2_dataset('furuno_scnx_as_cfradial2.nc')
display(volb.root)
display(volb[0])
volb[0].DBZH.sortby("rtime").plot(y="rtime")
<xarray.Dataset>
Dimensions:              (sweep: 1)
Coordinates:
    longitude            float64 13.24
    altitude             float64 38.0
    sweep_mode           <U20 'azimuth_surveillance'
    time                 datetime64[ns] 2022-03-24T00:00:01
    latitude             float64 53.55
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 '2022-03-24T00:00:01Z'
    time_coverage_end    <U20 '2022-03-24T00:00:28Z'
    sweep_group_name     (sweep) <U7 'sweep_0'
    sweep_fixed_angle    (sweep) float64 0.5
Attributes:
    version:          None
    title:            None
    institution:      None
    references:       None
    source:           None
    history:          None
    comment:          im/exported using wradlib
    instrument_name:  None
    fixed_angle:      0.5
<xarray.Dataset>
Dimensions:     (azimuth: 722, range: 936)
Coordinates:
  * azimuth     (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7
    elevation   (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
    rtime       (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20...
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
    sweep_mode  <U20 'azimuth_surveillance'
    time        datetime64[ns] 2022-03-24T00:00:01
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  0.5
[49]:
<matplotlib.collections.QuadMesh at 0x7fc3cb310040>
../../_images/notebooks_fileio_wradlib_furuno_backend_75_3.png

Check equality

We have to drop the time variable when checking equality since IRIS has millisecond resolution.

[50]:
xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(vol[0].drop(["rtime", "time", "QUAL"]), vola[0].drop(["rtime", "time"]))
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
#xr.testing.assert_allclose(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", "QUAL"]))

More Furuno loading mechanisms

Use xr.open_dataset to retrieve explicit group

[51]:
swp = xr.open_dataset(f, engine="furuno", group=1, backend_kwargs=dict(reindex_angle=False))
display(swp)
<xarray.Dataset>
Dimensions:     (azimuth: 722, range: 936)
Coordinates:
  * azimuth     (azimuth) float32 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7
    elevation   (azimuth) float32 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
    time        datetime64[ns] 2022-03-24T00:00:01
    rtime       (azimuth) datetime64[ns] 2022-03-24T00:00:17.656439500 ... 20...
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (azimuth, range) float32 ...
    DBZH        (azimuth, range) float32 ...
    VRADH       (azimuth, range) float32 ...
    ZDR         (azimuth, range) float32 ...
    KDP         (azimuth, range) float32 ...
    PHIDP       (azimuth, range) float32 ...
    RHOHV       (azimuth, range) float32 ...
    WRADH       (azimuth, range) float32 ...
    QUAL        (azimuth, range) uint16 ...
Attributes:
    fixed_angle:  0.5