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')
[8]:
fig = pl.figure(figsize=(10,10))
swp.DBZH.wradlib.plot_ppi(proj='cg', fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f213f88a620>
[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 >
[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 0x7f213f53b4c0>
[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 0x7f213f3e5450>
[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 0x7f213ef75780>
[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 0x7f213f27ffa0>
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f213f007ee0>
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: standard_name: radar_equivalent_reflectivity_factor_h units: dBZ long_name: 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 0x7f213f517790>
[18]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZH.wradlib.plot_ppi(proj={'latmin': 3e3}, fig=fig)
Mask some values¶
[19]:
dbzh = swp['DBZH'].where(swp['DBZH'] >= 0)
dbzh.plot(x="x", y="y")
[19]:
<matplotlib.collections.QuadMesh at 0x7f213ee42f80>
[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 0x7f213ee96170>
[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 0x7f213ed51870>
[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 0x7f2135ab8040>
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')
[33]:
fig = pl.figure(figsize=(10,10))
swp.DBZH.wradlib.plot_ppi(proj='cg', fig=fig)
[33]:
<matplotlib.collections.QuadMesh at 0x7f21359ceb60>
[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 >
[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 0x7f2135979990>
[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 0x7f2135b399c0>
[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 0x7f2135890c70>
[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 0x7f21358f0cd0>
[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7f2135913430>
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: standard_name: radar_equivalent_reflectivity_factor_h units: dBZ long_name: 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 0x7f213594ffd0>
[43]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZH.wradlib.plot_ppi(proj={'latmin': 3e3}, fig=fig)
Mask some values¶
[44]:
dbzh = swp['DBZH'].where(swp['DBZH'] >= 0)
dbzh.plot(x="x", y="y")
[44]:
<matplotlib.collections.QuadMesh at 0x7f21356fbb50>
[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 0x7f213557c160>
[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 0x7f213ed23f70>
[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 0x7f21354f8250>
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