EMBRAPA

Instrumentation: Micro Rain Radar

The suite of micro-rain radar measurements available to the TCO group includes an MRR operated during the aerosols, clouds, convection experiment (ACONVEX) project near Manaus, Brasil.

The Manaus-Embrapa site is located at \(2.89^{\circ} \mathrm{S}, \, 59.96^{\circ} \mathrm{W}\) and its altitude is \(100 \, \mathrm{m}\).

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.

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 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]

Sample Plots

Here we plot rain rate alongside MDQ (data quality of spectra in percent of spectra per time) for a single day during the campaign.

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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_with_mdq(ds):
    """Plot rain rate alongside MDQ (spectrum data quality percent)."""
    time = ds.time.values
    height = ds.range.values
    RR = ds.RR.values
    MDQ = ds.MDQ.values
    title = ds.title

    fig = plt.figure(figsize=(12, 6), constrained_layout=True)
    gs = fig.add_gridspec(
        2, 2, 
        width_ratios=[20, 1], 
        height_ratios=[12, 1], 
        hspace=0.05, wspace=0.05
    )

    ax_main = fig.add_subplot(gs[0, 0])
    ax_mdq  = fig.add_subplot(gs[1, 0], sharex=ax_main)
    cax_rr  = fig.add_subplot(gs[0, 1])
    cax_mdq = fig.add_subplot(gs[1, 1])

    mesh = ax_main.pcolormesh(time, height, RR.T, shading="auto", cmap="PuBu")
    cbar = fig.colorbar(mesh, cax=cax_rr)
    cbar.set_label("rain rate (mm h-1)")

    ax_main.set_ylabel("height above sensor (m)")
    ax_main.set_title(f"{title} - rain rate")

    cmap = mcolors.LinearSegmentedColormap.from_list(
        "quality", ["red", "yellow", "green"]
    )
    norm = mcolors.Normalize(vmin=0, vmax=100)

    ax_mdq.imshow(
        MDQ[np.newaxis, :],
        aspect="auto",
        cmap=cmap,
        norm=norm,
        extent=[mdates.date2num(time[0]), mdates.date2num(time[-1]), 0, 1]
    )

    ax_mdq.set_yticks([])
    ax_mdq.set_xlabel("time")
    ax_mdq.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))

    cbar_mdq = fig.colorbar(
        plt.cm.ScalarMappable(norm=norm, cmap=cmap),
        cax=cax_mdq
    )
    cbar_mdq.set_label("data qual (%)", loc="center")

    plt.show()

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

# select dataset
mrr = cat.EMBRAPA.mrr.to_dask()

embrapa = slice(np.datetime64("2015-04-16"), np.datetime64("2015-04-17"))
plot_rr_with_mdq(mrr.sel(time=embrapa))

/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 dataset
mrr

<xarray.Dataset> Size: 39GB
Dimensions:  (time: 1564799, 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 200.0 400.0 600.0 ... 5.8e+03 6e+03 6.2e+03
  * time     (time) datetime64[ns] 13MB 2012-06-08T05:23:00 ... 2016-02-28T23...
Data variables: (12/15)
    CC       (time) int64 13MB dask.array<chunksize=(1564799,), meta=np.ndarray>
    D        (time, range, bin) float32 12GB dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>
    DSN      (time) uint16 3MB dask.array<chunksize=(1564799,), meta=np.ndarray>
    DVS      (time) uint16 3MB dask.array<chunksize=(1564799,), meta=np.ndarray>
    F        (time, range, bin) float32 12GB dask.array<chunksize=(4096, 31, 32), meta=np.ndarray>
    LWC      (time, range) float32 194MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    ...       ...
    RR       (time, range) float32 194MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    SVS      (time) uint16 3MB dask.array<chunksize=(1564799,), meta=np.ndarray>
    TF       (time, range) float32 194MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    W        (time, range) float32 194MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    Z        (time, range) float32 194MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
    Za       (time, range) float32 194MB dask.array<chunksize=(131072, 31), meta=np.ndarray>
Attributes:
    Conventions:           CF-1.12
    _logical_cutoff_date:  2016-02-29T00:00:00Z
    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 EMBRAPA (Level 1)
    tool_versions:         {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [...

xarray.Dataset

• Dimensions:
  • time: 1564799
  • 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
    200.0 400.0 600.0 ... 6e+03 6.2e+03

    axis :

    Z

    long_name :

    vertical grid

    units :

    m

    array([ 200.,  400.,  600.,  800., 1000., 1200., 1400., 1600., 1800., 2000.,
           2200., 2400., 2600., 2800., 3000., 3200., 3400., 3600., 3800., 4000.,
           4200., 4400., 4600., 4800., 5000., 5200., 5400., 5600., 5800., 6000.,
           6200.], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2012-06-08T05:23:00 ... 2016-02-...

    axis :

    T

    standard_name :

    time

    units_metadata :

    leap_seconds: utc

    array(['2012-06-08T05:23:00.000000000', '2012-06-08T05:24:00.000000000',
           '2012-06-08T05:25:00.000000000', ..., '2016-02-28T23:57:02.000000000',
           '2016-02-28T23:58:02.000000000', '2016-02-28T23:59:02.000000000'],
          shape=(1564799,), dtype='datetime64[ns]')

• Data variables: (15)
  • CC
    (time)
    int64
    dask.array<chunksize=(1564799,), meta=np.ndarray>

    long_name :

    calibration constant

    Array Chunk Bytes 11.94 MiB 11.94 MiB Shape (1564799,) (1564799,) Dask graph 1 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 11.57 GiB 15.50 MiB Shape (1564799, 31, 64) (4096, 31, 32) Dask graph 766 chunks in 2 graph layers Data type float32 numpy.ndarray

  • DSN
    (time)
    uint16
    dask.array<chunksize=(1564799,), meta=np.ndarray>

    flag_meanings :

    0502092198 0507044526 0507024469 0502092199 0503022302

    flag_values :

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

    Array Chunk Bytes 2.98 MiB 2.98 MiB Shape (1564799,) (1564799,) Dask graph 1 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • DVS
    (time)
    uint16
    dask.array<chunksize=(1564799,), meta=np.ndarray>

    flag_meanings :

    5.10 6.10 6.00

    flag_values :

    [1, 2, 3, 0]

    Array Chunk Bytes 2.98 MiB 2.98 MiB Shape (1564799,) (1564799,) Dask graph 1 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 11.57 GiB 15.50 MiB Shape (1564799, 31, 64) (4096, 31, 32) Dask graph 766 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • MDQ
    (time)
    int64
    dask.array<chunksize=(1564799,), meta=np.ndarray>

    long_name :

    data quality, given % valid spectra per measurement

    units :

    0.01

    Array Chunk Bytes 11.94 MiB 11.94 MiB Shape (1564799,) (1564799,) Dask graph 1 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 11.57 GiB 15.50 MiB Shape (1564799, 31, 64) (4096, 31, 32) Dask graph 766 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SVS
    (time)
    uint16
    dask.array<chunksize=(1564799,), 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 2.98 MiB 2.98 MiB Shape (1564799,) (1564799,) Dask graph 1 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 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 185.05 MiB 15.50 MiB Shape (1564799, 31) (131072, 31) Dask graph 12 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([ 200.0,  400.0,  600.0,  800.0, 1000.0, 1200.0, 1400.0, 1600.0, 1800.0,
           2000.0, 2200.0, 2400.0, 2600.0, 2800.0, 3000.0, 3200.0, 3400.0, 3600.0,
           3800.0, 4000.0, 4200.0, 4400.0, 4600.0, 4800.0, 5000.0, 5200.0, 5400.0,
           5600.0, 5800.0, 6000.0, 6200.0],
          dtype='float32', name='range'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2012-06-08 05:23:00', '2012-06-08 05:24:00',
                   '2012-06-08 05:25:00', '2012-06-08 05:26:00',
                   '2012-06-08 05:27:00', '2012-06-08 05:28:00',
                   '2012-06-08 05:29:00', '2012-06-08 05:30:00',
                   '2012-06-08 05:31:00', '2012-06-08 05:32:00',
                   ...
                   '2016-02-28 23:50:02', '2016-02-28 23:51:01',
                   '2016-02-28 23:52:01', '2016-02-28 23:53:02',
                   '2016-02-28 23:54:02', '2016-02-28 23:55:02',
                   '2016-02-28 23:56:01', '2016-02-28 23:57:02',
                   '2016-02-28 23:58:02', '2016-02-28 23:59:02'],
                  dtype='datetime64[ns]', name='time', length=1564799, freq=None))

• Attributes: (11)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2016-02-29T00:00:00Z

  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 EMBRAPA (Level 1)

  tool_versions :

  {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]", "pytest": "8.3.5", "importlib_metadata": "8.7.0", "multidict": "6.4.3", "ruff": "0.13.0", "certifi": "2025.4.26", "tzdata": "2025.2", "partd": "1.4.2", "propcache": "0.3.1", "netcdf4": "1.7.2", "dask": "2025.4.1", "zipp": "3.21.0", "idna": "3.10", "jinja2": "3.1.6", "cloudpickle": "3.1.1", "yarl": "1.20.0", "aiosignal": "1.3.2", "pyyaml": "6.0.2", "attrs": "25.3.0", "aiohappyeyeballs": "2.6.1", "pytz": "2025.2", "aiohttp": "3.11.18", "pluggy": "1.5.0", "MarkupSafe": "3.0.3", "locket": "1.0.0", "frozenlist": "1.6.0", "datarecords": "0.0.0.post1657.dev0+ae78268", "pandas": "2.2.3", "packaging": "25.0", "fsspec": "2025.3.2", "six": "1.17.0", "bcoproc": "0.0.0.post1657.dev0+ae78268", "python-dateutil": "2.9.0.post0", "cftime": "1.6.4.post1", "xarray": "2025.4.0", "numpy": "2.2.5", "toolz": "1.0.0", "pendulum": "3.1.0", "iniconfig": "2.1.0", "bcologs": "0.0.0.post1657.dev0+ae78268", "click": "8.1.8"}