Ceilometer

Instrumentation

The first Ceilometer from JenOptik (CHX090102) was installed with a special optical unit in 2010, but this setup proved to suffer from signal loss. As a result, a standard CHM15k was installed in 2011, and the original unit was removed in 2012. The CHM15k setup has been in operation from 2011 until now with time-varying serial numbers (CHM090102, then CHM140102, CHM170159, and CHM200113).

One near infrared laser probes the sky vertically from the instrument to about 15 km above it. Targets like aerosol layers and clouds show up as echoes with certain backscatter intensity and signal extinction. Molecular absorption and Rayleigh scattering by air molecules is negligible at a wavelength of 1064 nm, so the CHM 15k works based on a photon counting method. Photon counting is more accurate and sensitive than analog measurement techniques, which makes it well suited for applications such as this one with a small count rate. The distance from ground is calculated from the travelling time of the laser pulses.

Data Availability

The data is available as .zarr files in the catalog as:

• BCO.ceilometer_c1_v1 (first setup with special optical unit: 2010-02-10 to 2012-03-10)

• BCO.ceilometer_c2_v1 (second setup with standard unit: 2011-01-22 to present)

Sample Plot

Plot cloud base height for each cloud layer for the first period of the instrument’s operation in 2011:

import intake
cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/catalog.yaml")
ds = cat.BCO.ceilometer_c2_v1.to_dask()
ds.sel(time="2011-01").cbh.plot(x='time', y='layer')

/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.collections.QuadMesh at 0x7f53d4480e60>

The full dataset:

ds

<xarray.Dataset> Size: 3GB
Dimensions:           (time: 700092, range: 1024, layer: 3)
Coordinates:
    lat               float64 8B ...
  * layer             (layer) int32 12B 1 2 3
    lon               float64 8B ...
  * range             (range) float32 4kB 14.98 29.97 ... 1.533e+04 1.534e+04
    sensor_alt        int64 8B ...
  * time              (time) datetime64[ns] 6MB 2011-01-22T20:44:22.937000 .....
Data variables: (12/31)
    average_time      (time) float64 6MB dask.array<chunksize=(262144,), meta=np.ndarray>
    azimuth           (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
    base              (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
    bcc               (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
    beta_raw          (time, range) float32 3GB dask.array<chunksize=(8192, 32), meta=np.ndarray>
    cbe               (time, layer) float32 8MB dask.array<chunksize=(8192, 3), meta=np.ndarray>
    ...                ...
    temp_ext          (time) float64 6MB dask.array<chunksize=(262144,), meta=np.ndarray>
    temp_int          (time) float64 6MB dask.array<chunksize=(262144,), meta=np.ndarray>
    voe               (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
    vor               (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
    wavelength        (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
    zenith            (time) float32 3MB dask.array<chunksize=(262144,), meta=np.ndarray>
Attributes:
    Conventions:      CF-1.12
    bcoproc_version:  0.0.0.post1096.dev0+2434541
    featureType:      timeSeriesProfile
    institution:      Max Planck Institute for Meteorology, Hamburg
    license:          CC0-1.0
    location:         The Barbados Cloud Observatory (BCO), Deebles Point, Ba...
    platform:         BCO
    source:           CHM 15k (Campbell Scientific, Jenoptik)
    summary:          This dataset contains measurements of cloud properties ...
    title:            Ceilometer data from BCO (Level 1)
    tool_versions:    {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 1...

xarray.Dataset

• Dimensions:
  • time: 700092
  • range: 1024
  • layer: 3
• Coordinates: (6)
  • lat
    ()
    float64
    ...

    long_name :

    north latitude

    units :

    degree_north

    valid_max :

    90.0

    valid_min :

    -90.0

    [1 values with dtype=float64]

  • layer
    (layer)
    int32
    1 2 3

    axis :

    layer

    long_name :

    Layer index

    units :

    index

    array([1, 2, 3], dtype=int32)

  • 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
    14.98 29.97 ... 1.533e+04 1.534e+04

    axis :

    Z

    long_name :

    distance from lidar

    units :

    m

    array([1.498500e+01, 2.997000e+01, 4.495500e+01, ..., 1.531467e+04,
           1.532966e+04, 1.534464e+04], shape=(1024,), 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]
    2011-01-22T20:44:22.937000 ... 2...

    axis :

    T

    standard_name :

    time

    units_metadata :

    leap_seconds: utc

    array(['2011-01-22T20:44:22.937000000', '2011-01-22T20:44:52.734000000',
           '2011-01-22T20:45:22.515000000', ..., '2011-09-30T18:38:01.140000000',
           '2011-09-30T18:38:31.453000000', '2011-09-30T18:39:01.312000000'],
          shape=(700092,), dtype='datetime64[ns]')

• Data variables: (31)
  • average_time
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    average time per record

    units :

    ms

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    long_name :

    laser direction of site

    units :

    degree_clockwise

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    Daylight correction factor, photons per shot

    units :

    counts

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    Base Cloud Cover

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    background substracted and normilised by laser shot number and stddev

    units :

    SNR

    Array Chunk Bytes 2.67 GiB 1.00 MiB Shape (700092, 1024) (8192, 32) Dask graph 2752 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cbe
    (time, layer)
    float32
    dask.array<chunksize=(8192, 3), meta=np.ndarray>

    long_name :

    cloud base height variation

    units :

    m

    Array Chunk Bytes 8.01 MiB 96.00 kiB Shape (700092, 3) (8192, 3) Dask graph 86 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cbh
    (time, layer)
    float32
    dask.array<chunksize=(8192, 3), meta=np.ndarray>

    axis :

    range

    long_name :

    cloud base height

    units :

    m

    Array Chunk Bytes 8.01 MiB 96.00 kiB Shape (700092, 3) (8192, 3) Dask graph 86 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cde
    (time, layer)
    float32
    dask.array<chunksize=(8192, 3), meta=np.ndarray>

    long_name :

    cloud depth variation

    units :

    m

    Array Chunk Bytes 8.01 MiB 96.00 kiB Shape (700092, 3) (8192, 3) Dask graph 86 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cdp
    (time, layer)
    float32
    dask.array<chunksize=(8192, 3), meta=np.ndarray>

    long_name :

    cloud depth

    units :

    m

    Array Chunk Bytes 8.01 MiB 96.00 kiB Shape (700092, 3) (8192, 3) Dask graph 86 chunks in 2 graph layers Data type float32 numpy.ndarray

  • error_ext
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    31 Bit ServiceCode

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

  • laser_pulses
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    number of laser pulses averaged per record

    units :

    unitless

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

  • life_time
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    h

    units :

    Laser fife time

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    long_name :

    maximum detection height

    units :

    m

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • nn1
    (time)
    float32
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    NN1

    units :

    NAN

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pbl
    (time, layer)
    float32
    dask.array<chunksize=(8192, 3), meta=np.ndarray>

    axis :

    range

    long_name :

    Aerosol Layer in PBL

    units :

    m

    Array Chunk Bytes 8.01 MiB 96.00 kiB Shape (700092, 3) (8192, 3) Dask graph 86 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pbs
    (time, layer)
    float32
    dask.array<chunksize=(8192, 3), meta=np.ndarray>

    long_name :

    Quality score for aerosol Layer in PBL

    Array Chunk Bytes 8.01 MiB 96.00 kiB Shape (700092, 3) (8192, 3) Dask graph 86 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    length of range gate, binwidth

    units :

    m

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    definition :

    0: nothing 1: rain 2: fog 3: snow

    long_name :

    Sky Condition Index

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    flag_meanings :

    ['CHX090102', 'CHM090102', 'CHM170159', 'CHM140102', 'CHM200113', 'unknown']

    flag_values :

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

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    quality of detector signal - 255 max

    units :

    unitless

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    laser quality index - 255 max

    units :

    unitless

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    transmission of optics

    units :

    unitless

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    Standard Deviation raw signal, photons/ shot

    units :

    counts

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    Total Cloud Cover

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temp_det
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    detector temperature

    units :

    K

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

  • temp_ext
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    external temperature

    units :

    K

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

  • temp_int
    (time)
    float64
    dask.array<chunksize=(262144,), meta=np.ndarray>

    long_name :

    internal temperature

    units :

    K

    Array Chunk Bytes 5.34 MiB 2.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

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

    long_name :

    vertical optical range error

    units :

    m

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    vertical optical range

    units :

    m

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    laser wavelength

    units :

    nm

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    long_name :

    laser direction of site

    units :

    degree

    Array Chunk Bytes 2.67 MiB 1.00 MiB Shape (700092,) (262144,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • layer
    PandasIndex

    PandasIndex(Index([1, 2, 3], dtype='int32', name='layer'))

  • range
    PandasIndex

    PandasIndex(Index([14.984999656677246, 29.969999313354492,  44.95500183105469,
           59.939998626708984,  74.92500305175781,  89.91000366210938,
            104.8949966430664, 119.87999725341797, 134.86500549316406,
           149.85000610351562,
           ...
              15209.775390625,    15224.759765625,   15239.7451171875,
               15254.73046875,     15269.71484375,   15284.7001953125,
             15299.6845703125,    15314.669921875,   15329.6552734375,
             15344.6396484375],
          dtype='float32', name='range', length=1024))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2011-01-22 20:44:22.937000', '2011-01-22 20:44:52.734000',
                   '2011-01-22 20:45:22.515000', '2011-01-22 20:45:52.812000',
                   '2011-01-22 20:46:22.609000', '2011-01-22 20:46:52.890000',
                   '2011-01-22 20:47:22.687000', '2011-01-22 20:47:52.484000',
                   '2011-01-22 20:48:22.781000', '2011-01-22 20:48:52.609000',
                   ...
                   '2011-09-30 18:33:10.593000', '2011-09-30 18:33:40.890000',
                   '2011-09-30 18:34:10.562000', '2011-09-30 18:34:40.578000',
                   '2011-09-30 18:35:10.359000', '2011-09-30 18:35:40.171000',
                   '2011-09-30 18:37:31.250000', '2011-09-30 18:38:01.140000',
                   '2011-09-30 18:38:31.453000', '2011-09-30 18:39:01.312000'],
                  dtype='datetime64[ns]', name='time', length=700092, freq=None))

• Attributes: (11)

  Conventions :

  CF-1.12

  bcoproc_version :

  0.0.0.post1096.dev0+2434541

  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 :

  CHM 15k (Campbell Scientific, Jenoptik)

  summary :

  This dataset contains measurements of cloud properties from a ceilometer probing vertically up to 15 km.

  title :

  Ceilometer data from BCO (Level 1)

  tool_versions :

  {"Python": "3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]", "attrs": "25.3.0", "toolz": "1.0.0", "propcache": "0.3.1", "pluggy": "1.5.0", "pytz": "2025.2", "click": "8.1.8", "importlib_metadata": "8.7.0", "frozenlist": "1.6.0", "partd": "1.4.2", "packaging": "25.0", "multidict": "6.4.3", "certifi": "2025.4.26", "python-dateutil": "2.9.0.post0", "aiohappyeyeballs": "2.6.1", "datarecords": "0.0.0.post1096.dev0+2434541", "iniconfig": "2.1.0", "cloudpickle": "3.1.1", "locket": "1.0.0", "six": "1.17.0", "pandas": "2.2.3", "ruff": "0.13.0", "numpy": "2.2.5", "netcdf4": "1.7.2", "zipp": "3.21.0", "idna": "3.10", "xarray": "2025.4.0", "pyyaml": "6.0.2", "bcologs": "0.0.0.post1096.dev0+2434541", "tzdata": "2025.2", "aiohttp": "3.11.18", "bcoproc": "0.0.0.post1096.dev0+2434541", "yarl": "1.20.0", "cftime": "1.6.4.post1", "aiosignal": "1.3.2", "dask": "2025.4.1", "pytest": "8.3.5", "fsspec": "2025.3.2", "pendulum": "3.1.0"}