Iris/Sigmet - Reader#

[1]:

import xarray as xr
from open_radar_data import DATASETS

import xradar as xd

Download#

Fetching Iris radar data file from open-radar-data repository.

[2]:

filename_single = DATASETS.fetch("SUR210819000227.RAWKPJV")
filename_volume = DATASETS.fetch("cor-main131125105503.RAW2049")

Downloading file 'SUR210819000227.RAWKPJV' from 'https://github.com/openradar/open-radar-data/raw/main/data/SUR210819000227.RAWKPJV' to '/home/docs/.cache/open-radar-data'.
Downloading file 'cor-main131125105503.RAW2049' from 'https://github.com/openradar/open-radar-data/raw/main/data/cor-main131125105503.RAW2049' to '/home/docs/.cache/open-radar-data'.

open_iris_datatree#

The same works analoguous with the datatree loader. But additionally we can provide a sweep string, number or list. The underlying xarray.Dataset can be accessed with property .ds.

[9]:

help(xd.io.open_iris_datatree)

Help on function open_iris_datatree in module xradar.io.backends.iris:

open_iris_datatree(filename_or_obj, **kwargs)
    Open Iris/Sigmet dataset as :py:class:`datatree.DataTree`.

    Parameters
    ----------
    filename_or_obj : str, Path, file-like or DataStore
        Strings and Path objects are interpreted as a path to a local or remote
        radar file

    Keyword Arguments
    -----------------
    sweep : int, list of int, optional
        Sweep number(s) to extract, default to first sweep. If None, all sweeps are
        extracted into a list.
    first_dim : str
        Can be ``time`` or ``auto`` first dimension. If set to ``auto``,
        first dimension will be either ``azimuth`` or ``elevation`` depending on
        type of sweep. Defaults to ``auto``.
    reindex_angle : bool or dict
        Defaults to False, no reindexing. Given dict should contain the kwargs to
        reindex_angle. Only invoked if `decode_coord=True`.
    fix_second_angle : bool
        If True, fixes erroneous second angle data. Defaults to ``False``.
    site_coords : bool
        Attach radar site-coordinates to Dataset, defaults to ``True``.
    kwargs : dict
        Additional kwargs are fed to :py:func:`xarray.open_dataset`.

    Returns
    -------
    dtree: datatree.DataTree
        DataTree

[10]:

dtree = xd.io.open_iris_datatree(filename_volume)
display(dtree)

[11]:

dtree = xd.io.open_iris_datatree(filename_volume, sweep="sweep_8")
display(dtree)

[12]:

dtree = xd.io.open_iris_datatree(filename_volume, sweep=[1, 2, 8])
display(dtree)

[13]:

dtree = xd.io.open_iris_datatree(
    filename_volume,
    sweep=["sweep_0", "sweep_1", "sweep_8"],
)
display(dtree)

Plot Time vs. Azimuth#

[14]:

dtree["sweep_0"].ds.azimuth.plot(y="time")

[14]:

[<matplotlib.lines.Line2D at 0x7fd4b0e7bd90>]

../_images/notebooks_Iris_22_1.png

Plot Sweep Range vs. Time#

We need to sort by time and specify the y-coordinate. Please also observe the different resolutions of this plot, compared to the Azimuth vs. Range plot below. This is due to second-resolution of the time coordinate.

[15]:

dtree["sweep_0"].ds.DBZH.sortby("time").plot(y="time")

[15]:

<matplotlib.collections.QuadMesh at 0x7fd4a8e47390>

../_images/notebooks_Iris_24_1.png

Plot Sweep Range vs. Azimuth#

[16]:

dtree["sweep_0"].ds.DBZH.plot()

[16]:

<matplotlib.collections.QuadMesh at 0x7fd4a8d83110>

../_images/notebooks_Iris_26_1.png