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	2015-01-16	present	[100, 3100]
CIMH	CIMH.mrr	2009-12-19	2025-06-29	[100, 3100]
EMBRAPA	EMBRAPA.mrr	2012-06-08	2016-02-29	[200, 6400]

MRR-PRO at BCO

Accessible with key: BCO.mrrpro

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.to_dask()
pro = cat.BCO.mrrpro.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/python3.14/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/python3.14/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: 181GB
Dimensions:  (time: 7312169, range: 31, bin: 64)
Coordinates:
  * time     (time) datetime64[ns] 58MB 2010-04-03T19:21:00 ... 2026-10-19T23...
  * range    (range) float32 124B 100.0 200.0 300.0 ... 2.9e+03 3e+03 3.1e+03
  * bin      (bin) int64 512B 0 1 2 3 4 5 6 7 8 9 ... 55 56 57 58 59 60 61 62 63
    alt      int64 8B ...
    lat      float64 8B ...
    lon      float64 8B ...
Data variables: (12/15)
    CC       (time) int64 58MB dask.array<chunksize=(4194304,), meta=np.ndarray>
    D        (time, range, bin) float32 58GB dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>
    DSN      (time) uint16 15MB dask.array<chunksize=(4194304,), meta=np.ndarray>
    DVS      (time) uint16 15MB dask.array<chunksize=(4194304,), meta=np.ndarray>
    F        (time, range, bin) float32 58GB dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>
    LWC      (time, range) float32 907MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    ...       ...
    RR       (time, range) float32 907MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    SVS      (time) uint16 15MB dask.array<chunksize=(4194304,), meta=np.ndarray>
    TF       (time, range) float32 907MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    W        (time, range) float32 907MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    Z        (time, range) float32 907MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    Za       (time, range) float32 907MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
Attributes:
    Conventions:           CF-1.12
    _logical_cutoff_date:  2026-10-20T00:00:00Z
    bcoproc_version:       0.0.0.post1657.dev0+ae78268
    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: 7312169
  • range: 31
  • bin: 64
• Coordinates: (6)
  • time
    (time)
    datetime64[ns]
    2010-04-03T19:21:00 ... 2026-10-...

    axis :

    T

    standard_name :

    time

    units_metadata :

    leap_seconds: utc

    array(['2010-04-03T19:21:00.000000000', '2010-04-03T19:22:00.000000000',
           '2010-04-03T19:23:00.000000000', ..., '2026-10-19T23:57:00.000000000',
           '2026-10-19T23:58:01.000000000', '2026-10-19T23:59:01.000000000'],
          shape=(7312169,), dtype='datetime64[ns]')

  • 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)

  • 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])

  • alt
    ()
    int64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=int64]

  • 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]

• Data variables: (15)
  • CC
    (time)
    int64
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    long_name :

    calibration constant

    Array Chunk Bytes 55.79 MiB 32.00 MiB Shape (7312169,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type int64 numpy.ndarray

  • D
    (time, range, bin)
    float32
    dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>

    long_name :

    Drop size bin centers

    units :

    mm

    Array Chunk Bytes 54.04 GiB 15.50 MiB Shape (7312169, 31, 64) (4096, 31, 32) Dask graph 3572 chunks in 2 graph layers Data type float32 numpy.ndarray

  • DSN
    (time)
    uint16
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    flag_meanings :

    0502092198 0507044526 0507024469 0502092199 0503022302

    flag_values :

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

    Array Chunk Bytes 13.95 MiB 8.00 MiB Shape (7312169,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • DVS
    (time)
    uint16
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    flag_meanings :

    5.10 6.10 6.00

    flag_values :

    [1, 2, 3, 0]

    Array Chunk Bytes 13.95 MiB 8.00 MiB Shape (7312169,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • F
    (time, range, bin)
    float32
    dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>

    long_name :

    Spectral reflectivities per bin

    units :

    dB

    Array Chunk Bytes 54.04 GiB 15.50 MiB Shape (7312169, 31, 64) (4096, 31, 32) Dask graph 3572 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LWC
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    liquid water content

    units :

    g m-3

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

  • MDQ
    (time)
    int64
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    long_name :

    data quality, given % valid spectra per measurement

    units :

    0.01

    Array Chunk Bytes 55.79 MiB 32.00 MiB Shape (7312169,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type int64 numpy.ndarray

  • N
    (time, range, bin)
    float32
    dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>

    long_name :

    Spectral drop number densities per bin

    units :

    m-3 mm-1

    Array Chunk Bytes 54.04 GiB 15.50 MiB Shape (7312169, 31, 64) (4096, 31, 32) Dask graph 3572 chunks in 2 graph layers Data type float32 numpy.ndarray

  • PIA
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    path integrated attenuation

    units :

    dB

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RR
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    rain rate

    units :

    mm h-1

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SVS
    (time)
    uint16
    dask.array<chunksize=(4194304,), 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 13.95 MiB 8.00 MiB Shape (7312169,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • TF
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    transfer function

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

  • W
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    fall velocity

    units :

    m s-1

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Z
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    radar reflectivity

    units :

    dBZ

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Za
    (time, range)
    float32
    dask.array<chunksize=(131072, 31), meta=np.ndarray>

    long_name :

    attenuated radar reflectivity

    units :

    dBZ

    Array Chunk Bytes 864.71 MiB 15.50 MiB Shape (7312169, 31) (131072, 31) Dask graph 56 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (11)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2026-10-20T00:00:00Z

  bcoproc_version :

  0.0.0.post1657.dev0+ae78268

  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]", "locket": "1.0.0", "iniconfig": "2.1.0", "tzdata": "2025.2", "yarl": "1.20.0", "aiosignal": "1.3.2", "jinja2": "3.1.6", "partd": "1.4.2", "pendulum": "3.1.0", "certifi": "2025.4.26", "attrs": "25.3.0", "fsspec": "2025.3.2", "importlib_metadata": "8.7.0", "multidict": "6.4.3", "cftime": "1.6.4.post1", "pyyaml": "6.0.2", "bcologs": "0.0.0.post1657.dev0+ae78268", "numpy": "2.2.5", "ruff": "0.13.0", "bcoproc": "0.0.0.post1657.dev0+ae78268", "click": "8.1.8", "aiohttp": "3.11.18", "dask": "2025.4.1", "propcache": "0.3.1", "frozenlist": "1.6.0", "datarecords": "0.0.0.post1657.dev0+ae78268", "pandas": "2.2.3", "netcdf4": "1.7.2", "python-dateutil": "2.9.0.post0", "toolz": "1.0.0", "pytz": "2025.2", "pluggy": "1.5.0", "MarkupSafe": "3.0.3", "zipp": "3.21.0", "pytest": "8.3.5", "cloudpickle": "3.1.1", "packaging": "25.0", "xarray": "2025.4.0", "idna": "3.10", "six": "1.17.0", "aiohappyeyeballs": "2.6.1"}

pro

<xarray.Dataset> Size: 700GB
Dimensions:                 (n_spectra: 128, spectrum_n_samples: 64,
                             time: 6703246, range: 128, sweep: 1)
Coordinates:
  * time                    (time) datetime64[ns] 54MB 2024-05-09T17:08:50.01...
    azimuth                 (time) float32 27MB dask.array<chunksize=(4194304,), meta=np.ndarray>
    elevation               (time) float32 27MB dask.array<chunksize=(4194304,), meta=np.ndarray>
  * range                   (range) float32 512B 25.0 75.0 ... 6.375e+03
    alt                     int64 8B ...
    lat                     float64 8B ...
    lon                     float64 8B ...
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=(131072, 32), meta=np.ndarray>
    N                       (time, n_spectra, spectrum_n_samples) float32 220GB dask.array<chunksize=(4096, 32, 32), meta=np.ndarray>
    PIA                     (time, range) float32 3GB dask.array<chunksize=(131072, 32), meta=np.ndarray>
    RR                      (time, range) float32 3GB dask.array<chunksize=(131072, 32), meta=np.ndarray>
    SNR                     (time, range) float32 3GB dask.array<chunksize=(131072, 32), meta=np.ndarray>
    ...                      ...
    sweep_end_ray_index     (sweep) int32 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) int32 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:  2026-07-07T00:00:00Z
    bcoproc_version:       0.0.0.post1747.dev0+b972f2d
    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: 6703246
  • range: 128
  • sweep: 1
• Coordinates: (7)
  • time
    (time)
    datetime64[ns]
    2024-05-09T17:08:50.010375 ... 2...

    axis :

    T

    standard_name :

    time

    units_metadata :

    leap_seconds: utc

    array(['2024-05-09T17:08:50.010375000', '2024-05-09T17:09:00.010332000',
           '2024-05-09T17:09:10.010328000', ..., '2026-07-06T23:59:30.005172000',
           '2026-07-06T23:59:40.005191000', '2026-07-06T23:59:50.005182000'],
          shape=(6703246,), dtype='datetime64[ns]')

  • azimuth
    (time)
    float32
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 25.57 MiB 16.00 MiB Shape (6703246,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

  • elevation
    (time)
    float32
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    Array Chunk Bytes 25.57 MiB 16.00 MiB Shape (6703246,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 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)

  • alt
    ()
    int64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=int64]

  • 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]

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

    long_name :

    drop sizes

    units :

    mm

    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=(131072, 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 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • N
    (time, n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(4096, 32, 32), meta=np.ndarray>

    long_name :

    drop size distribution

    units :

    m-3 mm-1

    Array Chunk Bytes 204.57 GiB 16.00 MiB Shape (6703246, 128, 64) (4096, 32, 32) Dask graph 13096 chunks in 2 graph layers Data type float32 numpy.ndarray

  • PIA
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    path integrated attenuation

    units :

    dB

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • RR
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    rain rate

    standard_name :

    rainfall_rate

    units :

    mm h-1

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNR
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    units :

    dB

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VEL
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    fall velocity

    units :

    m s-1

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • WIDTH
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    doppler spectrum width

    units :

    m s-1

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Z
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    log radar reflectivity

    units :

    dBZ

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Za
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    log attenuated radar reflectivity

    units :

    dBZ

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Ze
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    equivalent reflectivity factor

    standard_name :

    equivalent_reflectivity_factor

    units :

    dBZ

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zea
    (time, range)
    float32
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    long_name :

    attenuated equivalent reflectivity factor

    units :

    dBZ

    Array Chunk Bytes 3.20 GiB 16.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 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>

    dtype :

    <f4

    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)
    float64
    dask.array<chunksize=(131072, 32), meta=np.ndarray>

    dtype :

    <f4

    Array Chunk Bytes 6.39 GiB 32.00 MiB Shape (6703246, 128) (131072, 32) Dask graph 208 chunks in 2 graph layers Data type float64 numpy.ndarray

  • instrument_type
    ()
    |S128
    ...

    [1 values with dtype=|S128]

  • spectrum_raw
    (time, n_spectra, spectrum_n_samples)
    float64
    dask.array<chunksize=(4096, 32, 32), meta=np.ndarray>

    dtype :

    <f4

    long_name :

    log attenuated power

    units :

    dB

    Array Chunk Bytes 409.13 GiB 32.00 MiB Shape (6703246, 128, 64) (4096, 32, 32) Dask graph 13096 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sunshade_flag
    (time)
    int8
    dask.array<chunksize=(4194304,), meta=np.ndarray>

    flag_meanings :

    no yes

    flag_values :

    [0, 1]

    long_name :

    sun shade on instrument

    Array Chunk Bytes 6.39 MiB 4.00 MiB Shape (6703246,) (4194304,) Dask graph 2 chunks in 2 graph layers Data type int8 numpy.ndarray

  • sweep_end_ray_index
    (sweep)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    dtype :

    <f4

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

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

    dtype :

    O

    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)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    dtype :

    <f4

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

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

    long_name :

    transfer function

    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]

• Attributes: (12)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2026-07-07T00:00:00Z

  bcoproc_version :

  0.0.0.post1747.dev0+b972f2d

  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]", "pyyaml": "6.0.2", "partd": "1.4.2", "pendulum": "3.1.0", "datarecords": "0.0.0.post1747.dev0+b972f2d", "bcologs": "0.0.0.post1747.dev0+b972f2d", "cftime": "1.6.4.post1", "numpy": "2.2.5", "locket": "1.0.0", "pytest": "8.3.5", "six": "1.17.0", "packaging": "25.0", "netcdf4": "1.7.2", "toolz": "1.0.0", "attrs": "25.3.0", "iniconfig": "2.1.0", "propcache": "0.3.1", "yarl": "1.20.0", "click": "8.1.8", "multidict": "6.4.3", "MarkupSafe": "3.0.3", "ruff": "0.13.0", "python-dateutil": "2.9.0.post0", "certifi": "2025.4.26", "bcoproc": "0.0.0.post1747.dev0+b972f2d", "aiosignal": "1.3.2", "dask": "2025.4.1", "importlib_metadata": "8.7.0", "jinja2": "3.1.6", "xarray": "2025.4.0", "frozenlist": "1.6.0", "pytz": "2025.2", "aiohappyeyeballs": "2.6.1", "cloudpickle": "3.1.1", "zipp": "3.21.0", "pandas": "2.2.3", "tzdata": "2025.2", "aiohttp": "3.11.18", "pluggy": "1.5.0", "fsspec": "2025.3.2", "idna": "3.10"}