MBRS

The polarised 35.5 GHz Doppler radar MBRS is similar to the previously installed MBR2 radar, but was installed 7.5 years later (in December 2022). Currently, both, MBR2 and MBRS are being operated concurrently.

Configurations

Up to now, the radar has only been operated in a single configuration:

2s timestep, 155m-18km range

from 2022-12-04 until today

available as BCO.radar_MBRS_c1_v1

Example Plot

import intake
import numpy as np

cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/catalog.yaml")
mbrs = cat.BCO.radar_MBRS_c1_v1.to_dask()

# the power of one line:
mbrs.sel(time=slice("2022-12-04", "2022-12-05")).resample(time="2min").mean().Ze.plot(x='time', 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),

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(
/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(
/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(
/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(
/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(
/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(
/builds/tco/bco/docs/.venv/lib/python3.12/site-packages/dask/array/core.py:5003: PerformanceWarning: Increasing number of chunks by factor of 11
  result = blockwise(

<matplotlib.collections.QuadMesh at 0x7f28bf2541a0>

The whole dataset:

mbrs

<xarray.Dataset> Size: 1TB
Dimensions:     (time: 30467295, alt: 605)
Coordinates:
  * alt         (alt) float64 5kB 174.4 205.6 236.8 ... 1.898e+04 1.901e+04
    lat         float64 8B ...
    lon         float64 8B ...
  * time        (time) datetime64[ns] 244MB 2022-12-04T17:50:35 ... 2025-04-2...
Data variables: (12/23)
    HSDco       (time, alt) float32 74GB dask.array<chunksize=(259200, 55), meta=np.ndarray>
    HSDcx       (time, alt) float32 74GB dask.array<chunksize=(259200, 55), meta=np.ndarray>
    LDR         (time, alt) float32 74GB dask.array<chunksize=(259200, 55), meta=np.ndarray>
    LDRg        (time, alt) float32 74GB dask.array<chunksize=(259200, 55), meta=np.ndarray>
    MeltHei     (time) float32 122MB dask.array<chunksize=(2592000,), meta=np.ndarray>
    RMS         (time, alt) float32 74GB dask.array<chunksize=(259200, 55), meta=np.ndarray>
    ...          ...
    mask        (time, alt) bool 18GB dask.array<chunksize=(259200, 55), meta=np.ndarray>
    northangle  (time) float32 122MB dask.array<chunksize=(2592000,), meta=np.ndarray>
    sensor_alt  float64 8B ...
    status      (time) int16 61MB dask.array<chunksize=(2592000,), meta=np.ndarray>
    tpow        (time) float32 122MB dask.array<chunksize=(2592000,), meta=np.ndarray>
    zenith      float64 8B ...
Attributes: (12/13)
    Conventions:      CF-1.7
    Copyright:        
    Copyright_Owner:  
    bcoproc_version:  2.0.1.dev221+g18013fd
    created_with:     bcoproc (kband.py)
    creation_date:    Sat Mar 18 20:31:10 2023
    ...               ...
    instrument:       MBRS cloud radar
    location:         The Barbados Cloud Observatory, Deebles Point, Barbados...
    reference:        Ka Band Cloud Radar MIRA, METEK GmbH www.metek.de
    system:           MBRS
    title:            MIRA Cloud Radar Data
    tool_versions:    {"Python": "3.10.6 | packaged by conda-forge | (main, A...

xarray.Dataset

• Dimensions:
  • time: 30467295
  • alt: 605
• Coordinates: (4)
  • alt
    (alt)
    float64
    174.4 205.6 ... 1.898e+04 1.901e+04

    axis :

    Z

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    array([  174.4 ,   205.58,   236.75, ..., 18944.28, 18975.45, 19006.63],
          shape=(605,))

  • lat
    ()
    float64
    ...

    [1 values with dtype=float64]

  • lon
    ()
    float64
    ...

    [1 values with dtype=float64]

  • time
    (time)
    datetime64[ns]
    2022-12-04T17:50:35 ... 2025-04-...

    axis :

    T

    standard_name :

    time

    array(['2022-12-04T17:50:35.000000000', '2022-12-04T17:50:37.000000000',
           '2022-12-04T17:50:39.000000000', ..., '2025-04-27T23:59:59.000000000',
           '2025-04-28T00:00:01.000000000', '2025-04-28T00:00:03.000000000'],
          shape=(30467295,), dtype='datetime64[ns]')

• Data variables: (23)
  • HSDco
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    db :

    1

    long_name :

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

    units :

    1

    yrange :

    [-20.0, 40.0]

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • HSDcx
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    db :

    1

    long_name :

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

    units :

    1

    yrange :

    [-20.0, 40.0]

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LDR
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

    linear de-polarization ratio LDR of all hydrometeors

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LDRg
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

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

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • MeltHei
    (time)
    float32
    dask.array<chunksize=(2592000,), meta=np.ndarray>

    db :

    0

    long_name :

    melting layer height

    units :

    m

    yrange :

    [0, 14000]

    Array Chunk Bytes 116.22 MiB 9.89 MiB Shape (30467295,) (2592000,) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RMS
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), 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 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RMSg
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), 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 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RadarConst
    (time)
    float32
    dask.array<chunksize=(2592000,), meta=np.ndarray>

    db :

    1

    long_name :

    radar constant related to 5 km height

    units :

    mm6 m-3

    yrange :

    [-35.0, -20.0]

    Array Chunk Bytes 116.22 MiB 9.89 MiB Shape (30467295,) (2592000,) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNR
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

    reflectivity SNR

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNRg
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

    reflectivity SNRg

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNRplank
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

    reflectivity SNRplank

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VEL
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    db :

    0

    long_name :

    doppler velocity VEL of all hydrometeors

    units :

    m s-1

    yrange :

    [-10.661450386047363, 10.661450386047363]

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VELg
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    db :

    0

    long_name :

    doppler velocity VEL of all targets (global)

    units :

    m s-1

    yrange :

    [-10.661450386047363, 10.661450386047363]

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Ze
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

    equivalent radar reflectivity of all hydrometeors

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zf
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    applied_thresholds :

    dBZ < -35 and VELg < -2.0 or VELg > 0.5 between 1000m and 8000m

    details :

    artifacts (rabbit-ears), caused by side lobes, are removed

    long_name :

    rabbit-ear filtered radar reflectivity of all hydrometeors

    standard_name :

    equivalent_reflectivity_factor

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zg
    (time, alt)
    float32
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    long_name :

    equivalent radar reflectivity of all targets (global)

    units :

    dBZ

    Array Chunk Bytes 68.67 GiB 54.38 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type float32 numpy.ndarray

  • azimuth
    ()
    float64
    ...

    long_name :

    horizontal angle offset to north (0 degrees).

    standard_name :

    sensor_azimuth_angle

    units :

    degree

    [1 values with dtype=float64]

  • mask
    (time, alt)
    bool
    dask.array<chunksize=(259200, 55), meta=np.ndarray>

    flag_meanings :

    rabbit_ear_retrieval no_rabbit_ear_retrieval

    flag_values :

    False True

    long_name :

    rabbit ear mask

    units :

    1

    usage :

    multiply radar products with this mask to reduce false signals by rabbit-ear retrievals

    Array Chunk Bytes 17.17 GiB 13.60 MiB Shape (30467295, 605) (259200, 55) Dask graph 1298 chunks in 2 graph layers Data type bool numpy.ndarray

  • northangle
    (time)
    float32
    dask.array<chunksize=(2592000,), meta=np.ndarray>

    long_name :

    north angle

    units :

    degree

    Array Chunk Bytes 116.22 MiB 9.89 MiB Shape (30467295,) (2592000,) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sensor_alt
    ()
    float64
    ...

    long_name :

    antenna height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=float64]

  • status
    (time)
    int16
    dask.array<chunksize=(2592000,), meta=np.ndarray>

    flag_meanings :

    radar_off radar_on

    flag_values :

    0 1

    long_name :

    radar power flag

    units :

    1

    Array Chunk Bytes 58.11 MiB 4.94 MiB Shape (30467295,) (2592000,) Dask graph 12 chunks in 2 graph layers Data type int16 numpy.ndarray

  • tpow
    (time)
    float32
    dask.array<chunksize=(2592000,), meta=np.ndarray>

    long_name :

    average transmit power

    units :

    W

    Array Chunk Bytes 116.22 MiB 9.89 MiB Shape (30467295,) (2592000,) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • zenith
    ()
    float64
    ...

    long_name :

    zenith angle

    standard_name :

    sensor_zenith_angle

    units :

    degree

    [1 values with dtype=float64]

• Indexes: (2)
  • alt
    PandasIndex

    PandasIndex(Index([   174.4,   205.58,   236.75,   267.93,   299.11,   330.29,   361.47,
             392.65,   423.83,   455.01,
           ...
           18726.02,  18757.2, 18788.38, 18819.56, 18850.74, 18881.92,  18913.1,
           18944.28, 18975.45, 19006.63],
          dtype='float64', name='alt', length=605))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-12-04 17:50:35', '2022-12-04 17:50:37',
                   '2022-12-04 17:50:39', '2022-12-04 17:50:41',
                   '2022-12-04 17:50:43', '2022-12-04 17:50:45',
                   '2022-12-04 17:50:47', '2022-12-04 17:50:49',
                   '2022-12-04 17:50:51', '2022-12-04 17:50:53',
                   ...
                   '2025-04-27 23:59:45', '2025-04-27 23:59:47',
                   '2025-04-27 23:59:49', '2025-04-27 23:59:51',
                   '2025-04-27 23:59:53', '2025-04-27 23:59:55',
                   '2025-04-27 23:59:57', '2025-04-27 23:59:59',
                   '2025-04-28 00:00:01', '2025-04-28 00:00:03'],
                  dtype='datetime64[ns]', name='time', length=30467295, freq=None))

• Attributes: (13)

  Conventions :

  CF-1.7

  Copyright :

  Copyright_Owner :

  bcoproc_version :

  2.0.1.dev221+g18013fd

  created_with :

  bcoproc (kband.py)

  creation_date :

  Sat Mar 18 20:31:10 2023

  institution :

  Max Planck Institute for Meteorology, Hamburg

  instrument :

  MBRS cloud radar

  location :

  The Barbados Cloud Observatory, Deebles Point, Barbados, West Indies

  reference :

  Ka Band Cloud Radar MIRA, METEK GmbH www.metek.de

  system :

  MBRS

  title :

  MIRA Cloud Radar Data

  tool_versions :

  {"Python": "3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:36:39) [GCC 10.4.0]", "pytz": "2022.2.1", "netCDF4": "1.6.1", "cftime": "1.6.2", "cloudpickle": "2.2.0", "pyzstd": "0.15.3", "fsspec": "2022.8.2", "numpy": "1.23.3", "partd": "1.3.0", "xarray": "2022.6.0", "locket": "1.0.0", "dask": "2022.9.1", "pandas": "1.5.0", "python-dateutil": "2.8.2", "bcoproc": "2.0.1.dev221+g18013fd", "toolz": "0.12.0", "pyparsing": "3.0.9", "six": "1.16.0", "pyyaml": "6.0", "packaging": "21.3"}