KATRIN

Work in Progress

Under construction

Data availability

The data is available in zarr format with range axis from 300 m to 8 km and time resolution of 1s as BCO.radar_KATRIN_c1_v1.

Sample plot

import intake
import numpy as np

cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/catalog.yaml")
katrin = cat.BCO.radar_KATRIN_c1(version=1).to_dask()

# it's as simple as:
katrin.sel(time=slice("2010-12-14")).resample(time="5s").mean().Ze.plot(x='time', y='range', cmap='viridis')

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  'dims': dict(self._ds.dims),

<matplotlib.collections.QuadMesh at 0x7f86dcd41100>

The whole dataset:

katrin

<xarray.Dataset> Size: 716MB
Dimensions:     (time: 53452, range: 257)
Coordinates:
    lat         float64 8B ...
    lon         float64 8B ...
  * range       (range) float64 2kB 299.8 329.8 359.8 ... 7.945e+03 7.975e+03
    sensor_alt  int64 8B ...
  * time        (time) datetime64[ns] 428kB 2010-12-14T18:01:06 ... 2010-12-1...
Data variables: (12/19)
    HSDco       (time, range) float32 55MB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    HSDcx       (time, range) float32 55MB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    LDR         (time, range) float32 55MB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    LDRg        (time, range) float32 55MB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    MeltHei     (time) float32 214kB dask.array<chunksize=(53452,), meta=np.ndarray>
    RMS         (time, range) float32 55MB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    ...          ...
    Zg          (time, range) float32 55MB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    azimuth     (time) float32 214kB dask.array<chunksize=(53452,), meta=np.ndarray>
    northangle  (time) float32 214kB dask.array<chunksize=(53452,), meta=np.ndarray>
    status      (time) int16 107kB dask.array<chunksize=(53452,), meta=np.ndarray>
    tpow        (time) float32 214kB dask.array<chunksize=(53452,), meta=np.ndarray>
    zenith      (time) float32 214kB dask.array<chunksize=(53452,), meta=np.ndarray>
Attributes:
    Conventions:           CF-1.12
    _logical_cutoff_date:  2010-12-16T00:00:00Z
    bcoproc_version:       0.0.0.post1065.dev0+14b60a6
    featureType:           timeSeriesProfile
    institution:           Max Planck Institute for Meteorology, Hamburg
    license:               CC0-1.0
    location:              The Barbados Cloud Observatory (BCO), Deebles Poin...
    platform:              BCO
    source:                KATRIN cloud radar
    title:                 Cloud radar data from KATRIN at the BCO
    tool_versions:         {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [...

xarray.Dataset

• Dimensions:
  • time: 53452
  • range: 257
• Coordinates: (5)
  • lat
    ()
    float64
    ...

    [1 values with dtype=float64]

  • lon
    ()
    float64
    ...

    [1 values with dtype=float64]

  • range
    (range)
    float64
    299.8 329.8 ... 7.945e+03 7.975e+03

    axis :

    Z

    long_name :

    range from antenna to the center of each range gate

    standard_name :

    height

    units :

    m

    array([ 299.8 ,  329.78,  359.76, ..., 7914.72, 7944.7 , 7974.68], shape=(257,))

  • sensor_alt
    ()
    int64
    ...

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2010-12-14T18:01:06 ... 2010-12-...

    axis :

    T

    standard_name :

    time

    units_metadata :

    leap_seconds: utc

    array(['2010-12-14T18:01:06.000000000', '2010-12-14T18:01:07.000000000',
           '2010-12-14T18:01:08.000000000', ..., '2010-12-15T14:48:22.000000000',
           '2010-12-15T14:48:23.000000000', '2010-12-15T14:48:24.000000000'],
          shape=(53452,), dtype='datetime64[ns]')

• Data variables: (19)
  • HSDco
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    db :

    1

    long_name :

    co-channel HSdiv noise power. The arbitrary unit is DSP.

    units :

    1

    yrange :

    [-10.0, 60.0]

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • HSDcx
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    db :

    1

    long_name :

    cross-channel HSdiv noise power. The arbitrary unit is DSP.

    units :

    1

    yrange :

    [-10.0, 60.0]

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LDR
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    linear de-polarization ratio LDR of all hydrometeors

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LDRg
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    linear de-polarization ratio LDR of all targets (global)

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • MeltHei
    (time)
    float32
    dask.array<chunksize=(53452,), meta=np.ndarray>

    db :

    0

    long_name :

    melting layer height

    units :

    m

    yrange :

    [0, 14000]

    Array Chunk Bytes 208.80 kiB 208.80 kiB Shape (53452,) (53452,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RMS
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    db :

    0

    long_name :

    peak width RMS of all hydrometeors

    units :

    m s-1

    yrange :

    [0.0, 3.0]

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RMSg
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    db :

    0

    long_name :

    peak width RMS of all targets (global)

    units :

    m s-1

    yrange :

    [0.0, 3.0]

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNR
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    reflectivity SNR

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNRg
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    reflectivity SNRg

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNRplank
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    reflectivity SNRplank

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VEL
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    db :

    0

    long_name :

    doppler velocity VEL of all hydrometeors

    units :

    m s-1

    yrange :

    [-10.5563383102417, 10.5563383102417]

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VELg
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    db :

    0

    long_name :

    doppler velocity VEL of all targets (global)

    units :

    m s-1

    yrange :

    [-10.5563383102417, 10.5563383102417]

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Ze
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    equivalent radar reflectivity of all hydrometeors

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zg
    (time, range)
    float32
    dask.array<chunksize=(8192, 32), meta=np.ndarray>

    long_name :

    equivalent radar reflectivity of all targets (global)

    units :

    dBZ

    Array Chunk Bytes 52.40 MiB 1.00 MiB Shape (53452, 257) (8192, 32) Dask graph 63 chunks in 2 graph layers Data type float32 numpy.ndarray

  • azimuth
    (time)
    float32
    dask.array<chunksize=(53452,), meta=np.ndarray>

    long_name :

    horizontal angle offset to north (0 degrees).

    standard_name :

    sensor_azimuth_angle

    units :

    degree

    Array Chunk Bytes 208.80 kiB 208.80 kiB Shape (53452,) (53452,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • northangle
    (time)
    float32
    dask.array<chunksize=(53452,), meta=np.ndarray>

    long_name :

    north angle

    units :

    degree

    Array Chunk Bytes 208.80 kiB 208.80 kiB Shape (53452,) (53452,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • status
    (time)
    int16
    dask.array<chunksize=(53452,), meta=np.ndarray>

    flag_meanings :

    radar_off radar_on

    flag_values :

    0 1

    long_name :

    radar power flag

    units :

    1

    Array Chunk Bytes 104.40 kiB 104.40 kiB Shape (53452,) (53452,) Dask graph 1 chunks in 2 graph layers Data type int16 numpy.ndarray

  • tpow
    (time)
    float32
    dask.array<chunksize=(53452,), meta=np.ndarray>

    long_name :

    average transmit power

    units :

    W

    Array Chunk Bytes 208.80 kiB 208.80 kiB Shape (53452,) (53452,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • zenith
    (time)
    float32
    dask.array<chunksize=(53452,), meta=np.ndarray>

    long_name :

    zenith angle

    standard_name :

    sensor_zenith_angle

    units :

    degree

    Array Chunk Bytes 208.80 kiB 208.80 kiB Shape (53452,) (53452,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • range
    PandasIndex

    PandasIndex(Index([  299.8,  329.78,  359.76,  389.74,  419.72,   449.7,  479.68,  509.66,
            539.64,  569.62,
           ...
           7704.86, 7734.84, 7764.82,  7794.8, 7824.78, 7854.76, 7884.74, 7914.72,
            7944.7, 7974.68],
          dtype='float64', name='range', length=257))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2010-12-14 18:01:06', '2010-12-14 18:01:07',
                   '2010-12-14 18:01:08', '2010-12-14 18:01:09',
                   '2010-12-14 18:01:10', '2010-12-14 18:01:11',
                   '2010-12-14 18:01:12', '2010-12-14 18:01:13',
                   '2010-12-14 18:01:14', '2010-12-14 18:01:15',
                   ...
                   '2010-12-15 14:48:14', '2010-12-15 14:48:15',
                   '2010-12-15 14:48:17', '2010-12-15 14:48:18',
                   '2010-12-15 14:48:19', '2010-12-15 14:48:20',
                   '2010-12-15 14:48:21', '2010-12-15 14:48:22',
                   '2010-12-15 14:48:23', '2010-12-15 14:48:24'],
                  dtype='datetime64[ns]', name='time', length=53452, freq=None))

• Attributes: (11)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2010-12-16T00:00:00Z

  bcoproc_version :

  0.0.0.post1065.dev0+14b60a6

  featureType :

  timeSeriesProfile

  institution :

  Max Planck Institute for Meteorology, Hamburg

  license :

  CC0-1.0

  location :

  The Barbados Cloud Observatory (BCO), Deebles Point, Barbados, West Indies

  platform :

  BCO

  source :

  KATRIN cloud radar

  title :

  Cloud radar data from KATRIN at the BCO

  tool_versions :

  {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]", "packaging": "25.0", "dask": "2025.4.1", "pluggy": "1.5.0", "pendulum": "3.1.0", "toolz": "1.0.0", "propcache": "0.3.1", "cloudpickle": "3.1.1", "zipp": "3.21.0", "iniconfig": "2.1.0", "click": "8.1.8", "attrs": "25.3.0", "six": "1.17.0", "pyyaml": "6.0.2", "pytz": "2025.2", "bcoproc": "0.0.0.post1065.dev0+14b60a6", "ruff": "0.13.0", "numpy": "2.2.5", "yarl": "1.20.0", "aiohappyeyeballs": "2.6.1", "tzdata": "2025.2", "locket": "1.0.0", "aiohttp": "3.11.18", "python-dateutil": "2.9.0.post0", "importlib_metadata": "8.7.0", "aiosignal": "1.3.2", "xarray": "2025.4.0", "pandas": "2.2.3", "multidict": "6.4.3", "idna": "3.10", "datarecords": "0.0.0.post1065.dev0+14b60a6", "fsspec": "2025.3.2", "cftime": "1.6.4.post1", "partd": "1.4.2", "bcologs": "0.0.0.post1065.dev0+14b60a6", "pytest": "8.3.5", "certifi": "2025.4.26", "frozenlist": "1.6.0", "netcdf4": "1.7.2"}