Rain gauge (WAD)

Here be Dragons!

Compared to other precipitation measurements, the data from the rain gauge shows significant discrepancies.

After initial investigations we estimate the bucket was reliable for about 3 months after its installation, after which the data became not suitable for scientific use, up until it was removed on 2025-09-25. It was reinstalled 2026-01-29 and is currently in operation. The data taken since then should be reevaluated for scientific usability.

When investigating the bucket’s data, please also note that on September 1 2025 at 16:23, \(100 \, \mathrm{ml}\) of water was poured into the bucket as a test for instrument functionality.

Instrumentation

Since 2023-05-09, the BCO has operated a WAD200 rain measurement system at the site.

The instrument collects falling hydrometeors in a \(200 \mathrm{cm}^2\) aperture and funnels to a vessel (“bucket”) which is self-emptying and calibrated yearly. The data output includes rain intensity (\(\mathrm{mm} \mathrm{h}^{-1}\)) and total collected rain (\(\mathrm{mm}\)) as well as temperature and system status.

Data Availability

The data is available in .zarr format as BCO.bucket_WAD_c1_v1 in the catalog.

Sample Plot

Let’s plot cumulative rain and hourly average temperature for a few weeks in September 2024, the period of the ORCESTRA Campaign.

import intake
import matplotlib.pylab as plt

cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/catalog.yaml")
ds_bucket = cat.BCO.bucket_WAD_c1_v1.to_dask()

# period of ORCESTRA
orcestra = slice("2024-09-09", "2024-09-30")

subset = ds_bucket.sel(time=orcestra)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
subset.R.plot(ax=ax1)
subset.T.compute().resample(time="1h").mean().plot(ax=ax2)

/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.lines.Line2D at 0x7f15ea638e90>]

The full dataset:

ds_bucket

<xarray.Dataset> Size: 156MB
Dimensions:  (time: 7434042)
Coordinates:
    lat      float64 8B ...
    lon      float64 8B ...
  * time     (time) datetime64[ns] 59MB 2023-05-09T19:22:47 ... 2026-03-06T23...
Data variables:
    R        (time) float32 30MB dask.array<chunksize=(262144,), meta=np.ndarray>
    RIH      (time) float32 30MB dask.array<chunksize=(262144,), meta=np.ndarray>
    SYS      (time) int8 7MB dask.array<chunksize=(262144,), meta=np.ndarray>
    T        (time) float32 30MB dask.array<chunksize=(262144,), meta=np.ndarray>
    alt      float64 8B ...
Attributes:
    Conventions:           CF-1.12
    _logical_cutoff_date:  2026-03-07T00:00:00Z
    bcoproc_version:       0.0.0.post1185.dev0+c830e01
    featureType:           timeSeries
    institution:           Max Planck Institute for Meteorology, Hamburg
    license:               CC0-1.0
    location:              The Barbados Cloud Observatory (BCO), Deebles Poin...
    platform:              BCO
    source:                OTT WAD 200
    summary:               This dataset contains precipitation measurements f...
    title:                 Raingauge measurements from BCO (Level 1)
    tool_versions:         {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [...

xarray.Dataset

• Dimensions:
  • time: 7434042
• Coordinates: (3)
  • 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]

  • time
    (time)
    datetime64[ns]
    2023-05-09T19:22:47 ... 2026-03-...

    axis :

    T

    standard_name :

    time

    array(['2023-05-09T19:22:47.000000000', '2023-05-09T19:22:57.000000000',
           '2023-05-09T19:23:07.000000000', ..., '2026-03-06T23:59:37.000000000',
           '2026-03-06T23:59:47.000000000', '2026-03-06T23:59:58.000000000'],
          shape=(7434042,), dtype='datetime64[ns]')

• Data variables: (5)
  • R
    (time)
    float32
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    total rain amount

    units :

    mm

    Array Chunk Bytes 28.36 MiB 1.00 MiB Shape (7434042,) (262144,) Dask graph 29 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    rain intensity

    units :

    mm h-1

    Array Chunk Bytes 28.36 MiB 1.00 MiB Shape (7434042,) (262144,) Dask graph 29 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SYS
    (time)
    int8
    dask.array<chunksize=(262144,), meta=np.ndarray>

    comment :

    Decimal representation of status bits. Bit 0,1,3 are vendor-internal. Bit 2 set => temperature sensor broken.

    long_name :

    system status

    units :

    1

    Array Chunk Bytes 7.09 MiB 256.00 kiB Shape (7434042,) (262144,) Dask graph 29 chunks in 2 graph layers Data type int8 numpy.ndarray

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

    long_name :

    ambient temperature

    units :

    degree_Celsius

    Array Chunk Bytes 28.36 MiB 1.00 MiB Shape (7434042,) (262144,) Dask graph 29 chunks in 2 graph layers Data type float32 numpy.ndarray

  • alt
    ()
    float64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-05-09 19:22:47', '2023-05-09 19:22:57',
                   '2023-05-09 19:23:07', '2023-05-09 19:23:17',
                   '2023-05-09 19:23:27', '2023-05-09 19:23:38',
                   '2023-05-09 19:23:48', '2023-05-09 19:23:58',
                   '2023-05-09 19:24:08', '2023-05-09 19:24:18',
                   ...
                   '2026-03-06 23:58:26', '2026-03-06 23:58:36',
                   '2026-03-06 23:58:46', '2026-03-06 23:58:57',
                   '2026-03-06 23:59:07', '2026-03-06 23:59:17',
                   '2026-03-06 23:59:27', '2026-03-06 23:59:37',
                   '2026-03-06 23:59:47', '2026-03-06 23:59:58'],
                  dtype='datetime64[ns]', name='time', length=7434042, freq=None))

• Attributes: (12)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2026-03-07T00:00:00Z

  bcoproc_version :

  0.0.0.post1185.dev0+c830e01

  featureType :

  timeSeries

  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 :

  OTT WAD 200

  summary :

  This dataset contains precipitation measurements from a raingauge that is installed at the BCO.

  title :

  Raingauge measurements from BCO (Level 1)

  tool_versions :

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