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()
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
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) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 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 0x7f193516d660>
[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 0x7f1934f7e890>
[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 0x7f1934ddd1b0>
[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 0x7f1934fd4100>
[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 0x7f19349624d0>
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f1934dd47f0>
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) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 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) float64 0.09078 0.2723 0.4539 ... -31.13 -31.19 y (azimuth, range) float64 24.77 74.3 ... 2.973e+04 2.978e+04 z (azimuth, range) float64 411.4 418.2 ... 4.535e+03 4.542e+03 gr (azimuth, range) float64 24.77 74.3 ... 2.973e+04 2.978e+04 rays (azimuth, range) float64 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 long_name: Equivalent reflectivity factor H units: dBZ
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 0x7f19349ca9b0>
[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 0x7f194034eef0>
[20]:
vol[0]
[20]:
<xarray.Dataset> Dimensions: (azimuth: 1376, range: 602) Coordinates: * azimuth (azimuth) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 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 0x7f19346c3730>
[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) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 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 0x7f1934786500>
[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) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 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 0x7f1934628af0>
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) float64 0.21 0.47 0.74 1.0 ... 359.2 359.4 359.7 359.9 elevation (azimuth) float64 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) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 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 0x7f193469a620>
[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 0x7f193448e6e0>
[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 0x7f19343fba00>
[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 0x7f1934414a60>
[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 0x7f1934307f70>
[40]:
swp.DBZH.wradlib.plot_ppi()
[40]:
<matplotlib.collections.QuadMesh at 0x7f19344ebf40>
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) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 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) float64 0.1243 0.373 0.6217 ... -403.6 -404.0 y (azimuth, range) float64 37.5 112.5 ... 7.008e+04 7.015e+04 z (azimuth, range) float64 38.3 38.95 39.61 ... 937.5 939.2 940.3 gr (azimuth, range) float64 37.53 112.5 ... 7.008e+04 7.015e+04 rays (azimuth, range) float64 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 long_name: Equivalent reflectivity factor H units: dBZ
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 0x7f1934372170>
[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 0x7f1934e19360>
[45]:
vol[0]
[45]:
<xarray.Dataset> Dimensions: (azimuth: 722, range: 936) Coordinates: * azimuth (azimuth) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 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 0x7f1934dd4940>
[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) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 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 0x7f1934767490>
[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) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 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 0x7f1941624100>
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) float64 0.19 0.68 1.16 1.69 ... 358.7 359.2 359.7 elevation (azimuth) float64 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