Parsivel2

The OTT Parsivel2 is a laser-based disdrometer for comprehensive measurement of all types of precipitation. There are currently 2 such disdrometers in operation at the BCO, although at points we have had 3 disdrometers at once.

The longest running disdrometer (451995) was in operation since 2023-05-14. The other two, 451996 and 451997, were added 2025-09-22. On 2026-07-19, 451995 was decommissioned and replaced with 451997.

As flags in the level 1 datasets, we provide the locations of the three parsivels, referencing the shipping containers as pictured below. We also provide an ‘orientation’ flag with meanings ‘eastward’ (with aperture perpendicular to climatologically dominant easterlies, as recommended by OTT) and ‘northward’ (perpendicular to eastward).

The device works on the extinction principle and measures precipitation particles using the shadowing effects they cause when they pass through a laser band.

The Parsivel2 captures both the size and the rate of fall in detail of the individual hydrometeors and classifies them into a range of 32 classes each. The temporal resolution covers between 10 seconds and one hour. The raw data is used to calculate for example precipitation type, amount and intensity.

The measurement principle

OTT Parsivel2 uses a laser-optical sensor to measure precipitation. The transmitter unit of the sensor generates a flat, horizontal beam of light that the receiver unit converts to an electrical signal. This signal changes whenever a hydrometeor falls through the beam anywhere within the measuring area (54 cm2). The degree of dimming is a measure of the size of the hydrometeor and the fall velocity is derived from the extinction signal duration.

Uh oh!

Comparison with other precipitation measurements corroborates findings from the literature that the parsivel does not accurately capture rain events surpassing 20 mm h-1 in intensity.

Data Availability

The data from the first parsivel is available as BCO.parsivel_451995 in the catalog.

The next two are published under the keys BCO.parsivel_451996 and BCO.parsivel_451997.

Sample Plot

The plot shows rain rate and particle size distribution during September 2024 (the period where the ORCESTRA Campaign visited the BCO).

import intake
import matplotlib.pylab as plt
cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/catalog.yaml")
ds = cat.BCO.parsivel_451995.to_dask()
subset = ds.sel(time="2024-09")
fix, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
subset["rain_rate"].plot(ax=ax1)
subset["raw"].mean("time").plot(cmap="Blues", 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),

The full dataset:

ds

<xarray.Dataset> Size: 3GB
Dimensions:                       (d: 32, bnd: 2, time: 1473459, v: 32)
Coordinates:
    alt                           float64 8B ...
  * d                             (d) float32 128B 0.0625 0.1875 ... 21.5 24.5
    lat                           float64 8B ...
    lon                           float64 8B ...
  * time                          (time) datetime64[ns] 12MB 2023-05-14T00:46...
  * v                             (v) float32 128B 0.05 0.15 0.25 ... 17.6 20.8
Dimensions without coordinates: bnd
Data variables:
    d_bounds                      (d, bnd) float32 256B dask.array<chunksize=(32, 2), meta=np.ndarray>
    disdrometer_rain_rate_status  (time) int8 1MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    error_code                    (time) int16 3MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    field_n_ved                   (time, d) float32 189MB dask.array<chunksize=(131072, 32), meta=np.ndarray>
    field_v                       (time, v) float32 189MB dask.array<chunksize=(131072, 32), meta=np.ndarray>
    firmware_iop                  (time) uint16 3MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    number_of_particles           (time) int32 6MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    rain_rate                     (time) float32 6MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    raw                           (time, v, d) int16 3GB dask.array<chunksize=(4096, 32, 32), meta=np.ndarray>
    sample_interval               (time) int16 3MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    sensor_status                 (time) int16 3MB dask.array<chunksize=(1473459,), meta=np.ndarray>
    v_bounds                      (v, bnd) float32 256B dask.array<chunksize=(32, 2), meta=np.ndarray>
Attributes: (12/15)
    Conventions:           CF-1.12
    _logical_cutoff_date:  2026-07-19T00:00:00Z
    bcoproc_version:       0.0.0.post1672.dev0+6e6e668
    featureType:           timeSeries
    history:               processed with bcoproc l1
    institution:           Max Planck Institute for Meteorology, Hamburg
    ...                    ...
    sensor_serial_number:  451995
    source:                OTT Parsivel2 451995
    station_name:          BCODIS
    summary:               The OTT Parsivel² is a modern laser disdrometer fo...
    title:                 Parsivel 2 Disdrometer data from BCO (Level 1)
    tool_versions:         {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [...

xarray.Dataset

• Dimensions:
  • d: 32
  • bnd: 2
  • time: 1473459
  • v: 32
• Coordinates: (6)
  • alt
    ()
    float64
    ...

    long_name :

    height above mean sea level

    standard_name :

    altitude

    units :

    m

    [1 values with dtype=float64]

  • d
    (d)
    float32
    0.0625 0.1875 0.3125 ... 21.5 24.5

    bounds :

    d_bounds

    long_name :

    nominal centers of particle diameter bins

    units :

    mm

    array([ 0.0625,  0.1875,  0.3125,  0.4375,  0.5625,  0.6875,  0.8125,  0.9375,
            1.0625,  1.1875,  1.375 ,  1.625 ,  1.875 ,  2.125 ,  2.375 ,  2.75  ,
            3.25  ,  3.75  ,  4.25  ,  4.75  ,  5.5   ,  6.5   ,  7.5   ,  8.5   ,
            9.5   , 11.    , 13.    , 15.    , 17.    , 19.    , 21.5   , 24.5   ],
          dtype=float32)

  • 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]
    2023-05-14T00:46:28 ... 2026-07-19

    axis :

    T

    standard_name :

    time

    units_metadata :

    leap_seconds: utc

    array(['2023-05-14T00:46:28.000000000', '2023-05-14T00:47:28.000000000',
           '2023-05-14T00:48:28.000000000', ..., '2026-07-18T23:58:00.000000000',
           '2026-07-18T23:59:00.000000000', '2026-07-19T00:00:00.000000000'],
          shape=(1473459,), dtype='datetime64[ns]')

  • v
    (v)
    float32
    0.05 0.15 0.25 ... 15.2 17.6 20.8

    bounds :

    v_bounds

    long_name :

    nominal centers of particle velocity bins

    units :

    m s-1

    array([ 0.05,  0.15,  0.25,  0.35,  0.45,  0.55,  0.65,  0.75,  0.85,  0.95,
            1.1 ,  1.3 ,  1.5 ,  1.7 ,  1.9 ,  2.2 ,  2.6 ,  3.  ,  3.4 ,  3.8 ,
            4.4 ,  5.2 ,  6.  ,  6.8 ,  7.6 ,  8.8 , 10.4 , 12.  , 13.6 , 15.2 ,
           17.6 , 20.8 ], dtype=float32)

• Data variables: (12)
  • d_bounds
    (d, bnd)
    float32
    dask.array<chunksize=(32, 2), meta=np.ndarray>

    long_name :

    bounds of particle diameter bins

    Array Chunk Bytes 256 B 256 B Shape (32, 2) (32, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • disdrometer_rain_rate_status
    (time)
    int8
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    flag_meanings :

    ok warn

    flag_values :

    [0, 1]

    long_name :

    disdrometer unreliable for rain rate > 20 mm h-1

    Array Chunk Bytes 1.41 MiB 1.41 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

  • error_code
    (time)
    int16
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    long_name :

    Error Code

    Array Chunk Bytes 2.81 MiB 2.81 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type int16 numpy.ndarray

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

    long_name :

    mean volume equivalent diameter per class

    units :

    0.1 lg(re 1 m3 mm)

    Array Chunk Bytes 179.87 MiB 16.00 MiB Shape (1473459, 32) (131072, 32) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    mean velocity per class

    units :

    mm s-1

    Array Chunk Bytes 179.87 MiB 16.00 MiB Shape (1473459, 32) (131072, 32) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • firmware_iop
    (time)
    uint16
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    flag_meanings :

    2.11.6 2.11.11

    flag_values :

    [1, 2, 0]

    Array Chunk Bytes 2.81 MiB 2.81 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • number_of_particles
    (time)
    int32
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    long_name :

    Number of particles

    units :

    1

    Array Chunk Bytes 5.62 MiB 5.62 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • rain_rate
    (time)
    float32
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    long_name :

    Rain Intensity

    standard_name :

    rainfall_rate

    units :

    mm h-1

    Array Chunk Bytes 5.62 MiB 5.62 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • raw
    (time, v, d)
    int16
    dask.array<chunksize=(4096, 32, 32), meta=np.ndarray>

    long_name :

    particle count per class

    Array Chunk Bytes 2.81 GiB 8.00 MiB Shape (1473459, 32, 32) (4096, 32, 32) Dask graph 360 chunks in 2 graph layers Data type int16 numpy.ndarray

  • sample_interval
    (time)
    int16
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    long_name :

    Sample Interval

    units :

    s

    Array Chunk Bytes 2.81 MiB 2.81 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type int16 numpy.ndarray

  • sensor_status
    (time)
    int16
    dask.array<chunksize=(1473459,), meta=np.ndarray>

    long_name :

    Sensor status

    Array Chunk Bytes 2.81 MiB 2.81 MiB Shape (1473459,) (1473459,) Dask graph 1 chunks in 2 graph layers Data type int16 numpy.ndarray

  • v_bounds
    (v, bnd)
    float32
    dask.array<chunksize=(32, 2), meta=np.ndarray>

    long_name :

    bounds of particle velocity bins

    Array Chunk Bytes 256 B 256 B Shape (32, 2) (32, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • d
    PandasIndex

    PandasIndex(Index([0.0625, 0.1875, 0.3125, 0.4375, 0.5625, 0.6875, 0.8125, 0.9375, 1.0625,
           1.1875,  1.375,  1.625,  1.875,  2.125,  2.375,   2.75,   3.25,   3.75,
             4.25,   4.75,    5.5,    6.5,    7.5,    8.5,    9.5,   11.0,   13.0,
             15.0,   17.0,   19.0,   21.5,   24.5],
          dtype='float32', name='d'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-05-14 00:46:28', '2023-05-14 00:47:28',
                   '2023-05-14 00:48:28', '2023-05-14 00:49:28',
                   '2023-05-14 00:50:28', '2023-05-14 00:51:28',
                   '2023-05-14 00:52:28', '2023-05-14 00:53:28',
                   '2023-05-14 00:54:28', '2023-05-14 00:55:28',
                   ...
                   '2026-07-18 23:51:00', '2026-07-18 23:52:00',
                   '2026-07-18 23:53:00', '2026-07-18 23:54:00',
                   '2026-07-18 23:55:00', '2026-07-18 23:56:00',
                   '2026-07-18 23:57:00', '2026-07-18 23:58:00',
                   '2026-07-18 23:59:00', '2026-07-19 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=1473459, freq=None))

  • v
    PandasIndex

    PandasIndex(Index([0.05000000074505806, 0.15000000596046448,                0.25,
            0.3500000238418579, 0.44999998807907104,   0.550000011920929,
            0.6499999761581421,                0.75,  0.8500000238418579,
             0.949999988079071,   1.100000023841858,  1.2999999523162842,
                           1.5,  1.7000000476837158,   1.899999976158142,
             2.200000047683716,  2.5999999046325684,                 3.0,
            3.4000000953674316,   3.799999952316284,   4.400000095367432,
             5.199999809265137,                 6.0,   6.800000190734863,
             7.599999904632568,   8.800000190734863,  10.399999618530273,
                          12.0,  13.600000381469727,  15.199999809265137,
            17.600000381469727,  20.799999237060547],
          dtype='float32', name='v'))

• Attributes: (15)

  Conventions :

  CF-1.12

  _logical_cutoff_date :

  2026-07-19T00:00:00Z

  bcoproc_version :

  0.0.0.post1672.dev0+6e6e668

  featureType :

  timeSeries

  history :

  processed with bcoproc l1

  institution :

  Max Planck Institute for Meteorology, Hamburg

  license :

  CC0-1.0

  location :

  The Barbados Cloud Observatory (BCO), Deebles Point, Barbados, West Indies

  platform :

  BCO

  sensor_serial_number :

  451995

  source :

  OTT Parsivel2 451995

  station_name :

  BCODIS

  summary :

  The OTT Parsivel² is a modern laser disdrometer for comprehensive measurement of all precipitation types. The Parsivel² captures both the size and speed of falling particles, classifying them into one of 32 separate size and velocity classes.

  title :

  Parsivel 2 Disdrometer data from BCO (Level 1)

  tool_versions :

  {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]", "pytest": "8.3.5", "pendulum": "3.1.0", "toolz": "1.0.0", "datarecords": "0.0.0.post1672.dev0+6e6e668", "pytz": "2025.2", "aiosignal": "1.3.2", "fsspec": "2025.3.2", "bcologs": "0.0.0.post1672.dev0+6e6e668", "click": "8.1.8", "python-dateutil": "2.9.0.post0", "idna": "3.10", "importlib_metadata": "8.7.0", "zipp": "3.21.0", "pluggy": "1.5.0", "aiohttp": "3.11.18", "xarray": "2025.4.0", "certifi": "2025.4.26", "iniconfig": "2.1.0", "packaging": "25.0", "propcache": "0.3.1", "partd": "1.4.2", "jinja2": "3.1.6", "numpy": "2.2.5", "multidict": "6.4.3", "MarkupSafe": "3.0.3", "bcoproc": "0.0.0.post1672.dev0+6e6e668", "attrs": "25.3.0", "yarl": "1.20.0", "aiohappyeyeballs": "2.6.1", "netcdf4": "1.7.2", "pandas": "2.2.3", "cloudpickle": "3.1.1", "cftime": "1.6.4.post1", "locket": "1.0.0", "six": "1.17.0", "dask": "2025.4.1", "pyyaml": "6.0.2", "ruff": "0.13.0", "tzdata": "2025.2", "frozenlist": "1.6.0"}