Micro-Rain Radars

Instrumentation

The BCO is equipped with a micro-rain radar (MRR-2) and an MRR PRO from Metek.

The MRRs at the BCO sit at \(20 \, \mathrm{m}\) above mean sea level.

The MRRs are upward looking \(24 \, \mathrm{GHz}\) micro-rain radars which detect fall velocity and rain rate of hydrometeors.

All data have a temporal resolution of \(1 \, \mathrm{min}\), and the height ranges sampled are documented below. The MRRs resolve the height dimension with 31 distinct levels.

In May 2024, an MRR PRO was installed at the BCO, which measures the characteristics of hydrometeor populations at \(128\) heights from \(25 \, \mathrm{m}\) to \(6375 \, \mathrm{m}\).

Data Availability

Datasets are available in .zarr format in the catalog. The three height range configurations for the MRR are as follows, and datasets are named according to these conventions:

• 1: [100, 3100]

• 2: [200, 6200]

• 3: [35, 1085]

Note

There are very few days with configuration 3, so we currently do not provide this data in the catalog, although the package is capable of reading data with this height range.

The MRR PRO does not have multiple height range configurations.

The table below summarizes the available MRR Level 1 datasets:

Site	Key	Start	Stop	Range
BCO	BCO.mrr_c1	2015-01-16	present	[100, 3100]
CIMH	CIMH.mrr_c1	2009-12-19	2025-06-29	[100, 3100]
EMBRAPA	EMBRAPA.mrr_c2	2012-06-08	2016-02-29	[200, 6400]

MRR-PRO at BCO

Accessible with key: BCO.mrr_pro_v1

Start	Stop	Range
2024-05-11	present	[25, 6375]

Note

The MRRs at CIMH and EMBRAPA are not currently in operation. There is a chance that the MRR at CIMH will resume operation in the future. Also, the MRR at CIMH collects data beginning 2009-12-11, but the first week of data is collected with configuration 3.

Sample Plots

Here we plot rain rate from the MRR and MRR-PRO next to each other for the same day.

Show code cell content

Hide code cell content

import intake
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import matplotlib.colors as mcolors
import matplotlib.dates as mdates

def plot_rr_side_by_side(ds1, ds2):
    time1, height1, RR1, title1 = ds1.time.values, ds1.range.values, ds1.RR.values, ds1.title
    time2, height2, RR2, title2 = ds2.time.values, ds2.range.values, ds2.RR.values, ds2.title

    fig = plt.figure(figsize=(14, 6), constrained_layout=True)
    gs = fig.add_gridspec(1, 3, width_ratios=[20, 20, 1], wspace=0.05)

    ax1 = fig.add_subplot(gs[0, 0])
    ax2 = fig.add_subplot(gs[0, 1], sharey=ax1)
    cax = fig.add_subplot(gs[0, 2])

    mesh1 = ax1.pcolormesh(time1, height1, RR1.T, shading='auto', cmap='PuBu')
    ax1.set_ylabel("height above sensor (m)")
    ax1.set_title(f"{title1} - rain rate")
    ax1.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))

    mesh2 = ax2.pcolormesh(time2, height2, RR2.T, shading='auto', cmap='PuBu')
    ax2.set_title(f"{title2} - rain rate")
    ax2.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))

    cbar = fig.colorbar(mesh2, cax=cax)
    cbar.set_label("rain rate (mm h$^{-1}$)")

    plt.show()

# open catalog
cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/catalog.yaml")

# select datasets
mrr = cat.BCO.mrr_c1(version=1).to_dask()
pro = cat.BCO.mrr_pro_v1.to_dask()

# define subset
day = slice(np.datetime64("2024-08-12"), np.datetime64("2024-08-13"))
mrr_day = mrr.sel(time=day)
pro_day = pro.sel(time=day)

# plot
plot_rr_side_by_side(mrr_day, pro_day)

/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/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),

# full datasets
mrr

<xarray.Dataset> Size: 179GB
Dimensions:  (time: 7241307, range: 31, bin: 64)
Coordinates:
    alt      int64 8B ...
  * bin      (bin) int64 512B 0 1 2 3 4 5 6 7 8 9 ... 55 56 57 58 59 60 61 62 63
    lat      float64 8B ...
    lon      float64 8B ...
  * range    (range) float32 124B 100.0 200.0 300.0 ... 2.9e+03 3e+03 3.1e+03
  * time     (time) datetime64[ns] 58MB 2010-04-03T20:21:00 ... 2026-03-05T23...
Data variables: (12/15)
    CC       (time) int64 58MB dask.array<chunksize=(262144,), meta=np.ndarray>
    D        (time, range, bin) float32 57GB dask.array<chunksize=(256, 31, 32), meta=np.ndarray>
    DSN      (time) uint16 14MB dask.array<chunksize=(262144,), meta=np.ndarray>
    DVS      (time) uint16 14MB dask.array<chunksize=(262144,), meta=np.ndarray>
    F        (time, range, bin) float32 57GB dask.array<chunksize=(256, 31, 32), meta=np.ndarray>
    LWC      (time, range) float32 898MB dask.array<chunksize=(8192, 31), meta=np.ndarray>
    ...       ...
    RR       (time, range) float32 898MB dask.array<chunksize=(8192, 31), meta=np.ndarray>
    SVS      (time) uint16 14MB dask.array<chunksize=(262144,), meta=np.ndarray>
    TF       (time, range) float32 898MB dask.array<chunksize=(8192, 31), meta=np.ndarray>
    W        (time, range) float32 898MB dask.array<chunksize=(8192, 31), meta=np.ndarray>
    Z        (time, range) float32 898MB dask.array<chunksize=(8192, 31), meta=np.ndarray>
    Za       (time, range) float32 898MB dask.array<chunksize=(8192, 31), meta=np.ndarray>
Attributes:
    Conventions:           CF-1.12
    _logical_cutoff_date:  2026-03-06T00:00:00Z
    bcoproc_version:       0.0.0.post1174.dev0+4cf53a3
    featureType:           timeSeriesProfile
    institution:           Max Planck Institute for Meteorology, Hamburg
    license:               CC0-1.0
    location:              The Barbados Cloud Observatory (BCO), Deebles Poin...
    platform:              BCO
    source:                METEK MRR-2
    title:                 MRR Data from BCO (Level 1)
    tool_versions:         {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [...

xarray.Dataset

• Dimensions:
  • time: 7241307
  • range: 31
  • bin: 64
• Coordinates: (6)
  • alt
    ()
    int64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=int64]

  • bin
    (bin)
    int64
    0 1 2 3 4 5 6 ... 58 59 60 61 62 63

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63])

  • lat
    ()
    float64
    ...

    long_name :

    north latitude

    units :

    degree_north

    valid_max :

    90.0

    valid_min :

    -90.0

    [1 values with dtype=float64]

  • lon
    ()
    float64
    ...

    long_name :

    east longitude

    units :

    degree_east

    valid_max :

    180.0

    valid_min :

    -180.0

    [1 values with dtype=float64]

  • range
    (range)
    float32
    100.0 200.0 300.0 ... 3e+03 3.1e+03

    axis :

    Z

    long_name :

    vertical grid

    units :

    m

    array([ 100.,  200.,  300.,  400.,  500.,  600.,  700.,  800.,  900., 1000.,
           1100., 1200., 1300., 1400., 1500., 1600., 1700., 1800., 1900., 2000.,
           2100., 2200., 2300., 2400., 2500., 2600., 2700., 2800., 2900., 3000.,
           3100.], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2010-04-03T20:21:00 ... 2026-03-...

    axis :

    T

    standard_name :

    time

    array(['2010-04-03T20:21:00.000000000', '2010-04-03T20:22:00.000000000',
           '2010-04-03T20:23:00.000000000', ..., '2026-03-05T23:57:00.000000000',
           '2026-03-05T23:58:01.000000000', '2026-03-05T23:59:01.000000000'],
          shape=(7241307,), dtype='datetime64[ns]')

• Data variables: (15)
  • CC
    (time)
    int64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    calibration constant

    Array Chunk Bytes 55.25 MiB 2.00 MiB Shape (7241307,) (262144,) Dask graph 28 chunks in 2 graph layers Data type int64 numpy.ndarray

  • D
    (time, range, bin)
    float32
    dask.array<chunksize=(256, 31, 32), meta=np.ndarray>

    long_name :

    Drop size bin centers

    units :

    mm

    Array Chunk Bytes 53.52 GiB 0.97 MiB Shape (7241307, 31, 64) (256, 31, 32) Dask graph 56574 chunks in 2 graph layers Data type float32 numpy.ndarray

  • DSN
    (time)
    uint16
    dask.array<chunksize=(262144,), meta=np.ndarray>

    flag_meanings :

    0502092198 0507044526 0507024469 0502092199 0503022302

    flag_values :

    [1, 2, 3, 4, 5, 0]

    Array Chunk Bytes 13.81 MiB 512.00 kiB Shape (7241307,) (262144,) Dask graph 28 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • DVS
    (time)
    uint16
    dask.array<chunksize=(262144,), meta=np.ndarray>

    flag_meanings :

    5.10 6.10 6.00

    flag_values :

    [1, 2, 3, 0]

    Array Chunk Bytes 13.81 MiB 512.00 kiB Shape (7241307,) (262144,) Dask graph 28 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • F
    (time, range, bin)
    float32
    dask.array<chunksize=(256, 31, 32), meta=np.ndarray>

    long_name :

    Spectral reflectivities per bin

    units :

    dB

    Array Chunk Bytes 53.52 GiB 0.97 MiB Shape (7241307, 31, 64) (256, 31, 32) Dask graph 56574 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LWC
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    long_name :

    liquid water content

    units :

    g m^-3

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

  • MDQ
    (time)
    int64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    data quality, given as percent of valid spectra per measurement

    Array Chunk Bytes 55.25 MiB 2.00 MiB Shape (7241307,) (262144,) Dask graph 28 chunks in 2 graph layers Data type int64 numpy.ndarray

  • N
    (time, range, bin)
    float32
    dask.array<chunksize=(256, 31, 32), meta=np.ndarray>

    long_name :

    Spectral drop densities per bin

    units :

    m^-3 mm^-1

    Array Chunk Bytes 53.52 GiB 0.97 MiB Shape (7241307, 31, 64) (256, 31, 32) Dask graph 56574 chunks in 2 graph layers Data type float32 numpy.ndarray

  • PIA
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    long_name :

    path integrated attenuation

    units :

    dB

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RR
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    description :

    rain rate derived from Doppler spectra

    long_name :

    Rain Rate

    units :

    mm h^-1

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SVS
    (time)
    uint16
    dask.array<chunksize=(262144,), meta=np.ndarray>

    flag_meanings :

    5.2.0.5 6.0.0.6 6.1.0.6 5.20 6.0.0.9 6.0.0.3

    flag_values :

    [1, 2, 3, 4, 5, 6, 0]

    Array Chunk Bytes 13.81 MiB 512.00 kiB Shape (7241307,) (262144,) Dask graph 28 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • TF
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    long_name :

    transfer function

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

  • W
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    long_name :

    fall velocity

    units :

    m s^-1

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Z
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    long_name :

    radar reflectivity

    units :

    dBZ

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Za
    (time, range)
    float32
    dask.array<chunksize=(8192, 31), meta=np.ndarray>

    long_name :

    attenuated radar reflectivity

    units :

    dBZ

    Array Chunk Bytes 856.33 MiB 0.97 MiB Shape (7241307, 31) (8192, 31) Dask graph 884 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • bin
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
          dtype='int64', name='bin'))

  • range
    PandasIndex

    PandasIndex(Index([ 100.0,  200.0,  300.0,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,
           1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
           1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0,
           2800.0, 2900.0, 3000.0, 3100.0],
          dtype='float32', name='range'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2010-04-03 20:21:00', '2010-04-03 20:22:00',
                   '2010-04-03 20:23:00', '2010-04-03 20:24:00',
                   '2010-04-03 20:25:00', '2010-04-03 20:26:00',
                   '2010-04-03 20:27:00', '2010-04-03 20:28:00',
                   '2010-04-03 20:29:00', '2010-04-03 20:30:00',
                   ...
                   '2026-03-05 23:50:01', '2026-03-05 23:51:00',
                   '2026-03-05 23:52:01', '2026-03-05 23:53:01',
                   '2026-03-05 23:54:01', '2026-03-05 23:55:01',
                   '2026-03-05 23:56:01', '2026-03-05 23:57:00',
                   '2026-03-05 23:58:01', '2026-03-05 23:59:01'],
                  dtype='datetime64[ns]', name='time', length=7241307, freq=None))

• Attributes: (11)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2026-03-06T00:00:00Z

  bcoproc_version :

  0.0.0.post1174.dev0+4cf53a3

  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 :

  METEK MRR-2

  title :

  MRR Data from BCO (Level 1)

  tool_versions :

  {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]", "cloudpickle": "3.1.1", "idna": "3.10", "pluggy": "1.5.0", "pytz": "2025.2", "packaging": "25.0", "python-dateutil": "2.9.0.post0", "pandas": "2.2.3", "dask": "2025.4.1", "datarecords": "0.0.0.post1174.dev0+4cf53a3", "fsspec": "2025.3.2", "tzdata": "2025.2", "locket": "1.0.0", "bcologs": "0.0.0.post1174.dev0+4cf53a3", "pyyaml": "6.0.2", "numpy": "2.2.5", "aiosignal": "1.3.2", "pytest": "8.3.5", "yarl": "1.20.0", "zipp": "3.21.0", "propcache": "0.3.1", "toolz": "1.0.0", "aiohttp": "3.11.18", "attrs": "25.3.0", "partd": "1.4.2", "six": "1.17.0", "importlib_metadata": "8.7.0", "multidict": "6.4.3", "aiohappyeyeballs": "2.6.1", "click": "8.1.8", "cftime": "1.6.4.post1", "pendulum": "3.1.0", "netcdf4": "1.7.2", "xarray": "2025.4.0", "ruff": "0.13.0", "bcoproc": "0.0.0.post1174.dev0+4cf53a3", "iniconfig": "2.1.0", "certifi": "2025.4.26", "frozenlist": "1.6.0"}

pro

<xarray.Dataset> Size: 401GB
Dimensions:                 (n_spectra: 128, spectrum_n_samples: 64,
                             time: 5636435, range: 128, sweep: 1)
Coordinates:
    azimuth                 (time) float32 23MB dask.array<chunksize=(262144,), meta=np.ndarray>
    elevation               (time) float32 23MB dask.array<chunksize=(262144,), meta=np.ndarray>
    lat                     float64 8B ...
    lon                     float64 8B ...
  * range                   (range) float32 512B 25.0 75.0 ... 6.375e+03
    sensor_alt              int64 8B ...
  * time                    (time) datetime64[ns] 45MB 2024-05-09T17:08:50.01...
Dimensions without coordinates: n_spectra, spectrum_n_samples, sweep
Data variables: (12/25)
    D                       (n_spectra, spectrum_n_samples) float32 33kB dask.array<chunksize=(128, 64), meta=np.ndarray>
    LWC                     (time, range) float32 3GB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    N                       (time, n_spectra, spectrum_n_samples) float32 185GB dask.array<chunksize=(256, 32, 32), meta=np.ndarray>
    PIA                     (time, range) float32 3GB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    RR                      (time, range) float32 3GB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    SNR                     (time, range) float32 3GB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    ...                      ...
    sweep_end_ray_index     (sweep) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
    sweep_mode              (sweep) |S128 128B dask.array<chunksize=(1,), meta=np.ndarray>
    sweep_number            (sweep) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
    sweep_start_ray_index   (sweep) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
    transfer_function       (range) float32 512B dask.array<chunksize=(128,), meta=np.ndarray>
    volume_number           float32 4B ...
Attributes:
    Conventions:           CF-1.12
    _logical_cutoff_date:  2026-03-06T00:00:00Z
    bcoproc_version:       0.0.0.post1185.dev0+c830e01
    featureType:           timeSeriesProfile
    institution:           Max Planck Institute for Meteorology, Hamburg
    license:               CC0-1.0
    location:              The Barbados Cloud Observatory (BCO), Deebles Poin...
    platform:              BCO
    source:                Metek MRR PRO 1.2.5
    summary:               This dataset contains profiles of reflectivity and...
    title:                 MRR PRO data from BCO (Level 1)
    tool_versions:         {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [...

xarray.Dataset

• Dimensions:
  • n_spectra: 128
  • spectrum_n_samples: 64
  • time: 5636435
  • range: 128
  • sweep: 1
• Coordinates: (7)
  • azimuth
    (time)
    float32
    dask.array<chunksize=(262144,), meta=np.ndarray>

    axis :

    radial_azimuth_coordinate

    long_name :

    azimuth angle from true North

    units :

    degrees

    Array Chunk Bytes 21.50 MiB 1.00 MiB Shape (5636435,) (262144,) Dask graph 22 chunks in 2 graph layers Data type float32 numpy.ndarray

  • elevation
    (time)
    float32
    dask.array<chunksize=(262144,), meta=np.ndarray>

    axis :

    radial_elevation_coordinate

    units :

    degrees

    Array Chunk Bytes 21.50 MiB 1.00 MiB Shape (5636435,) (262144,) Dask graph 22 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lat
    ()
    float64
    ...

    long_name :

    north latitude

    units :

    degree_north

    valid_max :

    90.0

    valid_min :

    -90.0

    [1 values with dtype=float64]

  • lon
    ()
    float64
    ...

    long_name :

    east longitude

    units :

    degree_east

    valid_max :

    180.0

    valid_min :

    -180.0

    [1 values with dtype=float64]

  • range
    (range)
    float32
    25.0 75.0 ... 6.325e+03 6.375e+03

    axis :

    Z

    units :

    m

    array([  25.,   75.,  125.,  175.,  225.,  275.,  325.,  375.,  425.,  475.,
            525.,  575.,  625.,  675.,  725.,  775.,  825.,  875.,  925.,  975.,
           1025., 1075., 1125., 1175., 1225., 1275., 1325., 1375., 1425., 1475.,
           1525., 1575., 1625., 1675., 1725., 1775., 1825., 1875., 1925., 1975.,
           2025., 2075., 2125., 2175., 2225., 2275., 2325., 2375., 2425., 2475.,
           2525., 2575., 2625., 2675., 2725., 2775., 2825., 2875., 2925., 2975.,
           3025., 3075., 3125., 3175., 3225., 3275., 3325., 3375., 3425., 3475.,
           3525., 3575., 3625., 3675., 3725., 3775., 3825., 3875., 3925., 3975.,
           4025., 4075., 4125., 4175., 4225., 4275., 4325., 4375., 4425., 4475.,
           4525., 4575., 4625., 4675., 4725., 4775., 4825., 4875., 4925., 4975.,
           5025., 5075., 5125., 5175., 5225., 5275., 5325., 5375., 5425., 5475.,
           5525., 5575., 5625., 5675., 5725., 5775., 5825., 5875., 5925., 5975.,
           6025., 6075., 6125., 6175., 6225., 6275., 6325., 6375.], dtype=float32)

  • sensor_alt
    ()
    int64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2024-05-09T17:08:50.010375 ... 2...

    axis :

    T

    standard_name :

    time

    array(['2024-05-09T17:08:50.010375000', '2024-05-09T17:09:00.010332000',
           '2024-05-09T17:09:10.010328000', ..., '2026-03-05T07:09:30.007486000',
           '2026-03-05T07:09:40.007490000', '2026-03-05T07:09:50.007482000'],
          shape=(5636435,), dtype='datetime64[ns]')

• Data variables: (25)
  • D
    (n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(128, 64), meta=np.ndarray>

    units :

    1

    Array Chunk Bytes 32.00 kiB 32.00 kiB Shape (128, 64) (128, 64) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    liquid water concentration

    standard_name :

    mass_concentration_of_liquid_water_in_air

    units :

    g m-3

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

  • N
    (time, n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(256, 32, 32), meta=np.ndarray>

    units :

    1

    Array Chunk Bytes 172.01 GiB 1.00 MiB Shape (5636435, 128, 64) (256, 32, 32) Dask graph 176144 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    dB

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    rainfall_rate

    units :

    mm h-1

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    dB

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    m s-1

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    m s-1

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    dBZ

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    dBZ

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    standard_name :

    equivalent_reflectivity_factor

    units :

    dBZ

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    units :

    dBZ

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

  • alt
    ()
    int64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=int64]

  • calibration_constant
    ()
    float32
    ...

    [1 values with dtype=float32]

  • doppler_shift_spectrum
    ()
    float32
    ...

    units :

    Hz

    [1 values with dtype=float32]

  • fixed_angle
    (sweep)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    Array Chunk Bytes 2.69 GiB 1.00 MiB Shape (5636435, 128) (8192, 32) Dask graph 2756 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_type
    ()
    |S128
    ...

    [1 values with dtype=|S128]

  • spectrum_raw
    (time, n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(256, 32, 32), meta=np.ndarray>

    Array Chunk Bytes 172.01 GiB 1.00 MiB Shape (5636435, 128, 64) (256, 32, 32) Dask graph 176144 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sweep_end_ray_index
    (sweep)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sweep_mode
    (sweep)
    |S128
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 128 B 128 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type |S128 numpy.ndarray

  • sweep_number
    (sweep)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sweep_start_ray_index
    (sweep)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • transfer_function
    (range)
    float32
    dask.array<chunksize=(128,), meta=np.ndarray>

    Array Chunk Bytes 512 B 512 B Shape (128,) (128,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • volume_number
    ()
    float32
    ...

    [1 values with dtype=float32]

• Indexes: (2)
  • range
    PandasIndex

    PandasIndex(Index([  25.0,   75.0,  125.0,  175.0,  225.0,  275.0,  325.0,  375.0,  425.0,
            475.0,
           ...
           5925.0, 5975.0, 6025.0, 6075.0, 6125.0, 6175.0, 6225.0, 6275.0, 6325.0,
           6375.0],
          dtype='float32', name='range', length=128))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-05-09 17:08:50.010375', '2024-05-09 17:09:00.010332',
                   '2024-05-09 17:09:10.010328', '2024-05-09 17:09:20.010334',
                   '2024-05-09 17:09:30.010331', '2024-05-09 17:09:40.010318',
                   '2024-05-09 17:09:50.010334', '2024-05-09 17:10:00.010326',
                   '2024-05-09 17:10:10.010334', '2024-05-09 17:10:20.010310',
                   ...
                   '2026-03-05 07:08:20.007490', '2026-03-05 07:08:30.007484',
                   '2026-03-05 07:08:40.007487', '2026-03-05 07:08:50.007491',
                   '2026-03-05 07:09:00.007484', '2026-03-05 07:09:10.007496',
                   '2026-03-05 07:09:20.007501', '2026-03-05 07:09:30.007486',
                   '2026-03-05 07:09:40.007490', '2026-03-05 07:09:50.007482'],
                  dtype='datetime64[ns]', name='time', length=5636435, freq=None))

• Attributes: (12)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2026-03-06T00:00:00Z

  bcoproc_version :

  0.0.0.post1185.dev0+c830e01

  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 :

  Metek MRR PRO 1.2.5

  summary :

  This dataset contains profiles of reflectivity and hydrometeor characteristics from an MRR PRO at the BCO.

  title :

  MRR PRO data from BCO (Level 1)

  tool_versions :

  {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]", "click": "8.1.8", "python-dateutil": "2.9.0.post0", "multidict": "6.4.3", "certifi": "2025.4.26", "toolz": "1.0.0", "importlib_metadata": "8.7.0", "numpy": "2.2.5", "idna": "3.10", "propcache": "0.3.1", "tzdata": "2025.2", "datarecords": "0.0.0.post1185.dev0+c830e01", "yarl": "1.20.0", "cftime": "1.6.4.post1", "locket": "1.0.0", "packaging": "25.0", "xarray": "2025.4.0", "iniconfig": "2.1.0", "netcdf4": "1.7.2", "attrs": "25.3.0", "aiohappyeyeballs": "2.6.1", "cloudpickle": "3.1.1", "fsspec": "2025.3.2", "pandas": "2.2.3", "pytz": "2025.2", "six": "1.17.0", "ruff": "0.13.0", "pytest": "8.3.5", "pluggy": "1.5.0", "partd": "1.4.2", "pendulum": "3.1.0", "aiohttp": "3.11.18", "frozenlist": "1.6.0", "pyyaml": "6.0.2", "zipp": "3.21.0", "bcoproc": "0.0.0.post1185.dev0+c830e01", "aiosignal": "1.3.2", "bcologs": "0.0.0.post1185.dev0+c830e01", "dask": "2025.4.1"}