xarray IRIS backend#
In this example, we read IRIS (sigmet) data files using the xradar iris
xarray backend.
[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 xradar as xd
import datatree as xt
import xarray as xr
try:
get_ipython().run_line_magic("matplotlib inline")
except:
pl.ion()
Load IRIS Volume Data#
[2]:
fpath = "sigmet/SUR210819000227.RAWKPJV"
f = wrl.util.get_wradlib_data_file(fpath)
vol = xd.io.open_iris_datatree(f, reindex_angle=False)
Downloading file 'sigmet/SUR210819000227.RAWKPJV' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/sigmet/SUR210819000227.RAWKPJV' to '/home/runner/work/wradlib/wradlib/wradlib-data'.
Inspect RadarVolume#
[3]:
display(vol)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2021-08-19T00:02:28Z' time_coverage_end <U20 '2021-08-19T00:02:49Z' longitude float64 25.52 altitude float64 157.0 latitude float64 58.48 Attributes: Conventions: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
Inspect root group#
The sweep
dimension contains the number of scans in this radar volume. Further the dataset consists of variables (location coordinates, time_coverage) and attributes (Conventions, metadata).
[4]:
vol.root
[4]:
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2021-08-19T00:02:28Z' time_coverage_end <U20 '2021-08-19T00:02:49Z' longitude float64 25.52 altitude float64 157.0 latitude float64 58.48 Attributes: Conventions: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
Inspect sweep group(s)#
The sweep-groups can be accessed via their respective keys. The dimensions consist of range
and time
with added coordinates azimuth
, elevation
, range
and time
. There will be variables like radar moments (DBZH etc.) and sweep-dependend metadata (like fixed_angle
, sweep_mode
etc.).
[5]:
display(vol["sweep_0"])
<xarray.DatasetView> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float64 0.03021 1.035 2.054 ... 358.0 359.0 elevation (azimuth) float32 ... time (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ..... * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 ... latitude float64 ... altitude float64 ... Data variables: (12/16) DBTH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... ... ... SNRH (azimuth, range) float32 ... sweep_mode <U20 ... sweep_number int64 ... prt_mode <U7 ... follow_mode <U7 ... sweep_fixed_angle float64 ...
Goereferencing#
[6]:
swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.pipe(wrl.georef.georeference_dataset)
Plotting#
[7]:
swp.DBZH.plot.pcolormesh(x="x", y="y")
pl.gca().set_aspect("equal")

[8]:
fig = pl.figure(figsize=(10, 10))
swp.DBZH.wrl.vis.plot(proj="cg", fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7f6055849410>

[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.wrl.vis.plot(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)
< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=25.518660116940737 +lat_0=58.48231002688408 +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.wrl.vis.plot(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6049ee6f10>

[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).wrl.vis.plot(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6049f3b6d0>

[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.wrl.vis.plot(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6049f7b6d0>

[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.wrl.vis.plot(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7f6049e3b810>

[15]:
swp.DBZH.wrl.vis.plot()
[15]:
<matplotlib.collections.QuadMesh at 0x7f60554fdf90>

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: 359, range: 833)> array([[-327.68, -327.68, -327.68, ..., -327.68, -327.68, -327.68], [-327.68, 3.39, 6.45, ..., -327.68, -327.68, -327.68], [ -8.86, 5.25, 8.58, ..., -327.68, -327.68, -327.68], ..., [-327.68, -327.68, -327.68, ..., -327.68, -327.68, -327.68], [-327.68, 4.75, 10.95, ..., -327.68, -327.68, -327.68], [-327.68, -327.68, 4.94, ..., -327.68, -327.68, -327.68]]) Coordinates: (12/14) * azimuth (azimuth) float64 0.03021 1.035 2.054 ... 357.0 358.0 359.0 elevation (azimuth) float64 0.5054 0.5054 0.5054 ... 0.5054 0.5054 0.5054 time (azimuth) datetime64[ns] 2021-08-19T00:02:31.104000 ... 2021-... * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 25.52 latitude float64 58.48 ... ... x (azimuth, range) float64 0.07909 0.2373 ... -4.211e+03 y (azimuth, range) float64 150.0 450.0 ... 2.493e+05 2.496e+05 z (azimuth, range) float64 158.3 161.0 ... 6.023e+03 6.034e+03 gr (azimuth, range) float64 150.0 450.0 ... 2.493e+05 2.496e+05 rays (azimuth, range) float64 0.03021 0.03021 0.03021 ... 359.0 359.0 bins (azimuth, range) float32 150.0 450.0 ... 2.494e+05 2.498e+05 Attributes: units: dBZ standard_name: radar_equivalent_reflectivity_factor_h long_name: Equivalent reflectivity factor H coordinates: elevation azimuth range latitude longitude altitude time ...
Create simple plot#
Using xarray features a simple plot can be created like this. Note the sortby('time')
method, which sorts the radials by time.
[17]:
swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7f6049e4e710>

[18]:
fig = pl.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(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 0x7f605582d750>

Export to ODIM and CfRadial2#
Need to remove DB_XHDR since it can’t be represented as ODIM/CfRadial2 moment.
[20]:
# vol[0] = vol[0].drop("DB_XHDR", errors="ignore")
# vol[0].DBZH.sortby("rtime").plot(y="rtime")
[21]:
xd.io.to_odim(vol, "iris_as_odim.h5")
xd.io.to_cfradial2(vol, "iris_as_cfradial2.nc")
Import again#
[22]:
vola = xd.io.open_odim_datatree("iris_as_odim.h5", reindex_angle=False)
display(vola)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 0 platform_type <U5 'fixed' instrument_type <U5 'radar' time_coverage_start <U20 '2021-08-19T00:02:28Z' time_coverage_end <U20 '2021-08-19T00:02:49Z' longitude float64 25.52 altitude float64 157.0 latitude float64 58.48 Attributes: Conventions: ODIM_H5/V2_2 version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar instrument_name: None
[23]:
volb = xt.open_datatree("iris_as_cfradial2.nc")
display(volb)
<xarray.DatasetView> Dimensions: () Data variables: volume_number int64 ... platform_type object ... instrument_type object ... time_coverage_start object ... time_coverage_end object ... longitude float64 ... altitude float64 ... latitude float64 ... Attributes: Conventions: Cf/Radial version: 2.0 title: None institution: None references: None source: None history: None: xradar v0.1.0 CfRadial2 export comment: im/exported using xradar instrument_name: None
More Iris loading mechanisms#
Use xr.open_dataset
to retrieve explicit group#
[24]:
swp_b = xr.open_dataset(
f, engine="iris", group="sweep_0", backend_kwargs=dict(reindex_angle=False)
)
display(swp_b)
<xarray.Dataset> Dimensions: (azimuth: 359, range: 833) Coordinates: * azimuth (azimuth) float64 0.03021 1.035 2.054 ... 358.0 359.0 elevation (azimuth) float32 ... time (azimuth) datetime64[ns] ... * range (range) float32 150.0 450.0 750.0 ... 2.494e+05 2.498e+05 longitude float64 ... latitude float64 ... altitude float64 ... Data variables: (12/16) DBTH (azimuth, range) float32 ... DBZH (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... KDP (azimuth, range) float32 ... ... ... SNRH (azimuth, range) float32 ... sweep_mode <U20 ... sweep_number int64 ... prt_mode <U7 ... follow_mode <U7 ... sweep_fixed_angle float64 ...