Vaisala Sigmet IRIS

import wradlib as wrl
import matplotlib.pyplot as plt
import numpy as np
import warnings

warnings.filterwarnings("ignore")
try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.ion()

/home/runner/micromamba/envs/wradlib-tests/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "

IRIS refers to the commercial Vaisala Sigmet Interactive Radar Information System. The Vaisala Sigmet Digital Receivers export data in a well documented binary format.

The philosophy behind the \(\omega radlib\) interface to the IRIS data model is very straightforward: \(\omega radlib\) simply translates the complete binary file structure to one dictionary and returns this dictionary to the user. Thus, the potential complexity of the stored data is kept and it is left to the user how to proceed with this data. The keys of the output dictionary are strings that correspond to the Sigmet Data Structures.

Note

For radar data in IRIS/Sigmet format the openradar community published xradar where xarray-based readers/writers are implemented. That particular code was ported from \(\omega radlib\) to xradar. Please refer to xradar for enhancements for polar radar.

From \(\omega radlib\) version 1.19 IRIS/Sigmet reading code is imported from xradar-package whenever and wherever necessary.

Please read the more indepth notebook iris_backend.

Such a file (typical ending: *.RAWXXXX) can be read by:

fpath = "sigmet/cor-main131125105503.RAW2049"
f = wrl.util.get_wradlib_data_file(fpath)
fcontent = wrl.io.read_iris(f)

Downloading file 'sigmet/cor-main131125105503.RAW2049' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/sigmet/cor-main131125105503.RAW2049' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

# which keywords can be used to access the content?
print(fcontent.keys())
# print the entire content including values of data and
# metadata of the first sweep
# (numpy arrays will not be entirely printed)
print(fcontent["data"][1].keys())
print()
print(fcontent["data"][1]["ingest_data_hdrs"].keys())
print(fcontent["data"][1]["ingest_data_hdrs"]["DB_DBZ"])
print()
print(fcontent["data"][1]["sweep_data"].keys())
print(fcontent["data"][1]["sweep_data"]["DB_DBZ"])

odict_keys(['product_hdr', 'product_type', 'ingest_header', 'nsweeps', 'nrays', 'nbins', 'data_types', 'data', 'raw_product_bhdrs'])
odict_keys(['ingest_data_hdrs', 'record_number', 'first_ray_byte_offset', 'sweep_data', 'ray_offsets'])

odict_keys(['DB_DBZ', 'DB_VEL', 'DB_ZDR', 'DB_KDP', 'DB_PHIDP', 'DB_RHOHV', 'DB_HCLASS'])
OrderedDict([('structure_header', OrderedDict([('structure_identifier', 24), ('format_version', 3), ('bytes_in_structure', 244876), ('flag', 1)])), ('sweep_start_time', datetime.datetime(2013, 11, 25, 10, 55, 3, 541000, tzinfo=datetime.timezone.utc)), ('sweep_number', 1), ('number_rays_per_sweep', 360), ('first_ray_index', 0), ('number_rays_file_expected', 360), ('number_rays_file_written', 360), ('fixed_angle', 0.4998779296875), ('bits_per_bin', 8), ('data_type', 2)])

odict_keys(['DB_DBZ', 'azi_start', 'azi_stop', 'azimuth', 'ele_start', 'ele_stop', 'elevation', 'rbins', 'dtime', 'DB_VEL', 'DB_ZDR', 'DB_KDP', 'DB_PHIDP', 'DB_RHOHV', 'DB_HCLASS'])
[[-20.5 -32.  -32.  ... -32.  -32.  -32. ]
 [-32.  -32.  -32.  ... -32.  -32.  -32. ]
 [-32.  -32.   20.5 ... -32.  -32.  -32. ]
 ...
 [-32.  -32.  -32.  ... -32.  -32.  -32. ]
 [-20.  -32.  -32.  ... -32.  -32.  -32. ]
 [-32.    3.5   6.  ... -32.  -32.  -32. ]]

fig = plt.figure(figsize=(10, 10))
swp = fcontent["data"][1]["sweep_data"]
da = wrl.georef.create_xarray_dataarray(
    swp["DB_DBZ"],
).wrl.georef.georeference()
im = da.wrl.vis.plot(fig=fig, crs="cg")