xarray IRIS backend

Contents

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 plt
import numpy as np
import xradar as xd
import xarray as xr

try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.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/main/data/sigmet/SUR210819000227.RAWKPJV' to '/home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data'.

Georeferencing#

[6]:

swp = vol["sweep_0"].ds.copy()
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.wrl.georef.georeference()

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.

For more details on plotting radar data see under Visualization.

[8]:

swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)

[8]:

<matplotlib.collections.QuadMesh at 0x7f93a0a15d60>

../../../_images/notebooks_fileio_backends_iris_backend_16_1.png

[9]:

fig = plt.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(crs={"latmin": 3e3}, fig=fig)

../../../_images/notebooks_fileio_backends_iris_backend_17_0.png