Basic concept of the Ruisdael data gather

import xarray as xr
import matplotlib.pyplot as plt

import os
import pooch
import pandas as pd 

def sample_downloader(url):

    file_path = pooch.retrieve(
        url=url,
        known_hash=None)
    
    return file_path    

def data_path_url(date):

    path = (
        f"{base_url}/{operation_mode}/"
        f"{instrument_family}/{instrument_site_name}/"
        f"{date.strftime('%Y/%m/%d')}"
    )

    target_file = f"{instrument_site_name}_{date.strftime('%Y%m%d_%H')}0000.nc"

    return f"{path}/{target_file}"

# Base URL of the Ruisadel data collection
# https://opendap.4tu.nl/thredds/catalog/data2/Ruisdael/catalog.html

# Base URL for downloading Ruisdael data
base_url = 'https://opendap.4tu.nl/thredds/fileServer/data2/Ruisdael'

operation_mode = 'continuous_monitoring'
instrument_family = 'micro_rain_radar'
instrument_site_name = 'MRR005_Slufter'

# selected data
date = '20250622'

# URL data generator for the selected day
url_list = [data_path_url(d) for d in pd.date_range(f'{date} 00', f'{date} 23', freq='h')]

# Gathering the selected data
path_local_cache = [sample_downloader(u) for u in url_list]

# Reading the data
data = xr.open_mfdataset(path_local_cache, engine='netcdf4')

# Looking into the data
data

<xarray.Dataset> Size: 915MB
Dimensions:                 (time: 8640, n_spectra: 128,
                             spectrum_n_samples: 64, range: 128)
Coordinates:
  * range                   (range) float32 512B 0.0 35.0 ... 4.41e+03 4.445e+03
  * time                    (time) datetime64[ns] 69kB 2025-06-22T00:00:00.00...
Dimensions without coordinates: n_spectra, spectrum_n_samples
Data variables: (12/25)
    latitude                (time) float32 35kB 51.93 51.93 ... 51.93 51.93
    longitude               (time) float32 35kB 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0
    altitude                (time) float32 35kB 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0
    D                       (time, n_spectra, spectrum_n_samples) float32 283MB dask.array<chunksize=(360, 128, 64), meta=np.ndarray>
    LWC                     (time, range) float32 4MB dask.array<chunksize=(1, 128), meta=np.ndarray>
    ML                      (time, range) float32 4MB dask.array<chunksize=(1, 128), meta=np.ndarray>
    ...                      ...
    spectrum_raw            (time, n_spectra, spectrum_n_samples) float32 283MB dask.array<chunksize=(1, 128, 64), meta=np.ndarray>
    time_coverage_end       (time) |S128 1MB b'2025-06-22T00:59:50Z' ... b'20...
    time_coverage_start     (time) |S128 1MB b'2025-06-22T00:00:00Z' ... b'20...
    time_reference          (time) |S128 1MB b'1970-01-01T00:00:00Z' ... b'19...
    transfer_function       (time, range) float32 4MB dask.array<chunksize=(360, 128), meta=np.ndarray>
    volume_number           (time) int32 35kB 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
Attributes: (12/23)
    Conventions:               CF/Radial
    version:                   1.3
    title:                     METEK MRR Pro 1.2.5 Data
    references:                
    field_names:               Za,Z,Zea,Ze,RR,LWC,PIA,VEL,WIDTH,SNR,spectrum_...
    institution:               Delft University of Technology
    ...                        ...
    sensor_name:               MRR005
    project_name:              https://ruisdael-observatory.nl/
    sampling_frequency:        500 kHz
    wavelength:                1.238 cm
    license:                   https://creativecommons.org/licenses/by-sa/4.0/
    NCO:                       netCDF Operators version 5.0.6 (Homepage = htt...

xarray.Dataset

• Dimensions:
  • time: 8640
  • n_spectra: 128
  • spectrum_n_samples: 64
  • range: 128
• Coordinates: (2)
  • range
    (range)
    float32
    0.0 35.0 ... 4.41e+03 4.445e+03

    standard name :

    projection_range_coordinate

    long_name :

    range_to_measurement_volume

    units :

    meters

    spacing_is_constant :

    true

    meters_to_center_of_first_gate :

    0.0

    meters_between_gates :

    35.0

    axis :

    radial_range_coordinate

    array([   0.,   35.,   70.,  105.,  140.,  175.,  210.,  245.,  280.,  315.,
            350.,  385.,  420.,  455.,  490.,  525.,  560.,  595.,  630.,  665.,
            700.,  735.,  770.,  805.,  840.,  875.,  910.,  945.,  980., 1015.,
           1050., 1085., 1120., 1155., 1190., 1225., 1260., 1295., 1330., 1365.,
           1400., 1435., 1470., 1505., 1540., 1575., 1610., 1645., 1680., 1715.,
           1750., 1785., 1820., 1855., 1890., 1925., 1960., 1995., 2030., 2065.,
           2100., 2135., 2170., 2205., 2240., 2275., 2310., 2345., 2380., 2415.,
           2450., 2485., 2520., 2555., 2590., 2625., 2660., 2695., 2730., 2765.,
           2800., 2835., 2870., 2905., 2940., 2975., 3010., 3045., 3080., 3115.,
           3150., 3185., 3220., 3255., 3290., 3325., 3360., 3395., 3430., 3465.,
           3500., 3535., 3570., 3605., 3640., 3675., 3710., 3745., 3780., 3815.,
           3850., 3885., 3920., 3955., 3990., 4025., 4060., 4095., 4130., 4165.,
           4200., 4235., 4270., 4305., 4340., 4375., 4410., 4445.], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2025-06-22T00:00:00.003011072 .....

    standard name :

    time

    long_name :

    time_in_seconds_since_volume_start

    array(['2025-06-22T00:00:00.003011072', '2025-06-22T00:00:10.002994176',
           '2025-06-22T00:00:20.003004160', ..., '2025-06-22T23:59:30.003008000',
           '2025-06-22T23:59:40.003004160', '2025-06-22T23:59:50.003009024'],
          dtype='datetime64[ns]')

• Data variables: (25)
  • latitude
    (time)
    float32
    51.93 51.93 51.93 ... 51.93 51.93

    units :

    degrees_north

    array([51.93348, 51.93348, 51.93348, ..., 51.93348, 51.93348, 51.93348],
          dtype=float32)

  • longitude
    (time)
    float32
    4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0

    units :

    degrees_east

    array([3.999866, 3.999866, 3.999866, ..., 3.999866, 3.999866, 3.999866],
          dtype=float32)

  • altitude
    (time)
    float32
    4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0

    units :

    meters

    array([4., 4., 4., ..., 4., 4., 4.], dtype=float32)

  • D
    (time, n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(360, 128, 64), meta=np.ndarray>

    standard name :

    drop_sizes

    long_name :

    units :

    1

    index_var_name :

    block_avg_length :

    0

    is_spectrum :

    true

    Array Chunk Bytes 270.00 MiB 11.25 MiB Shape (8640, 128, 64) (360, 128, 64) Dask graph 24 chunks in 73 graph layers Data type float32 numpy.ndarray

  • LWC
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    mass_concentration_of_liquid_water_in_air

    long_name :

    units :

    g m-3

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • ML
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    melting_layer

    long_name :

    units :

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • N
    (time, n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(1, 128, 64), meta=np.ndarray>

    standard name :

    drop_size_distribution

    long_name :

    units :

    1

    index_var_name :

    block_avg_length :

    0

    is_spectrum :

    true

    Array Chunk Bytes 270.00 MiB 32.00 kiB Shape (8640, 128, 64) (1, 128, 64) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • PIA
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    path_integrated_rain_attenuation

    long_name :

    units :

    dB

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • RR
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    rainfall_rate

    long_name :

    units :

    mm h-1

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • SNR
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    signal_to_noise_ratio

    long_name :

    units :

    dB

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • VEL
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    radial_velocity_of_scatterers_towards_instrument

    long_name :

    units :

    m s-1

    field_folds :

    true

    fold_limit_lower :

    -0.0

    fold_limit_upper :

    11.890331

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • WIDTH
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    doppler_spectrum_width

    long_name :

    units :

    m/s

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • Z
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    log_reflectivity

    long_name :

    units :

    dBZ

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • Za
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    log_attenuated_reflectivity

    long_name :

    units :

    dBZ

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • Ze
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    equivalent_reflectivity_factor

    long_name :

    units :

    dBZ

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • Zea
    (time, range)
    float32
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    attenuated_equivalent_reflectivity_factor

    long_name :

    units :

    dBZ

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    false

    Array Chunk Bytes 4.22 MiB 512 B Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • calibration_constant
    (time)
    float32
    1.038e+07 1.038e+07 ... 1.038e+07

    array([10375414., 10375414., 10375414., ..., 10375414., 10375414.,
           10375414.], dtype=float32)

  • doppler_shift_spectrum
    (time)
    float32
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)

  • index_spectra
    (time, range)
    float64
    dask.array<chunksize=(1, 128), meta=np.ndarray>

    standard_name :

    index variable spectra

    long_name :

    units :

    field_folds :

    false

    fold_limit_lower :

    0.0

    fold_limit_upper :

    0.0

    thresholding_xml :

    legend_xml :

    is_discreet :

    true

    Array Chunk Bytes 8.44 MiB 1.00 kiB Shape (8640, 128) (1, 128) Dask graph 8640 chunks in 49 graph layers Data type float64 numpy.ndarray

  • spectrum_raw
    (time, n_spectra, spectrum_n_samples)
    float32
    dask.array<chunksize=(1, 128, 64), meta=np.ndarray>

    standard name :

    log_attenuated_power

    long_name :

    units :

    dB

    index_var_name :

    block_avg_length :

    0

    is_spectrum :

    true

    Array Chunk Bytes 270.00 MiB 32.00 kiB Shape (8640, 128, 64) (1, 128, 64) Dask graph 8640 chunks in 49 graph layers Data type float32 numpy.ndarray

  • time_coverage_end
    (time)
    |S128
    b'2025-06-22T00:59:50Z' ... b'20...

    array([b'2025-06-22T00:59:50Z', b'2025-06-22T00:59:50Z',
           b'2025-06-22T00:59:50Z', ..., b'2025-06-22T23:59:50Z',
           b'2025-06-22T23:59:50Z', b'2025-06-22T23:59:50Z'], dtype='|S128')

  • time_coverage_start
    (time)
    |S128
    b'2025-06-22T00:00:00Z' ... b'20...

    array([b'2025-06-22T00:00:00Z', b'2025-06-22T00:00:00Z',
           b'2025-06-22T00:00:00Z', ..., b'2025-06-22T23:00:00Z',
           b'2025-06-22T23:00:00Z', b'2025-06-22T23:00:00Z'], dtype='|S128')

  • time_reference
    (time)
    |S128
    b'1970-01-01T00:00:00Z' ... b'19...

    array([b'1970-01-01T00:00:00Z', b'1970-01-01T00:00:00Z',
           b'1970-01-01T00:00:00Z', ..., b'1970-01-01T00:00:00Z',
           b'1970-01-01T00:00:00Z', b'1970-01-01T00:00:00Z'], dtype='|S128')

  • transfer_function
    (time, range)
    float32
    dask.array<chunksize=(360, 128), meta=np.ndarray>

    Array Chunk Bytes 4.22 MiB 180.00 kiB Shape (8640, 128) (360, 128) Dask graph 24 chunks in 73 graph layers Data type float32 numpy.ndarray

  • volume_number
    (time)
    int32
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    array([0, 0, 0, ..., 0, 0, 0], dtype=int32)

• Indexes: (2)
  • range
    PandasIndex

    PandasIndex(Index([   0.0,   35.0,   70.0,  105.0,  140.0,  175.0,  210.0,  245.0,  280.0,
            315.0,
           ...
           4130.0, 4165.0, 4200.0, 4235.0, 4270.0, 4305.0, 4340.0, 4375.0, 4410.0,
           4445.0],
          dtype='float32', name='range', length=128))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2025-06-22 00:00:00.003011072',
                   '2025-06-22 00:00:10.002994176',
                   '2025-06-22 00:00:20.003004160',
                   '2025-06-22 00:00:30.002996992',
                   '2025-06-22 00:00:40.002995968',
                   '2025-06-22 00:00:50.003011840',
                   '2025-06-22 00:01:00.003002880',
                   '2025-06-22 00:01:10.003025920',
                   '2025-06-22 00:01:20.002994944',
                   '2025-06-22 00:01:30.003002880',
                   ...
                   '2025-06-22 23:58:20.003001088',
                   '2025-06-22 23:58:30.003011840',
                   '2025-06-22 23:58:40.003026944',
                   '2025-06-22 23:58:50.003001856',
                   '2025-06-22 23:59:00.002995968',
                   '2025-06-22 23:59:10.002993152',
                   '2025-06-22 23:59:20.003035136',
                      '2025-06-22 23:59:30.003008',
                   '2025-06-22 23:59:40.003004160',
                   '2025-06-22 23:59:50.003009024'],
                  dtype='datetime64[ns]', name='time', length=8640, freq=None))

• Attributes: (23)

  Conventions :

  CF/Radial

  version :

  1.3

  title :

  METEK MRR Pro 1.2.5 Data

  references :

  field_names :

  Za,Z,Zea,Ze,RR,LWC,PIA,VEL,WIDTH,SNR,spectrum_reflectivity,N

  institution :

  Delft University of Technology

  source :

  vertically profiling micro rain radar

  history :

  Sun Jun 22 03:05:06 2025: /home/ruisdael/miniconda3/bin/ncpdq -O -7 -L 5 --pck_map=dbl_flt --output=/mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc /mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc Sun Jun 22 03:05:04 2025: /home/ruisdael/miniconda3/bin/ncap2 -O -s latitude=double(51.933481);longitude=double(3.999866);altitude=double(4) --output=/mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc /mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc Sun Jun 22 03:05:02 2025: /home/ruisdael/miniconda3/bin/ncks -C -x -v azimuth,elevation,instrument_type,sweep_end_ray_index,sweep_mode,sweep_number,sweep_start_ray_index,fixed_angle --overwrite --output=/mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc /mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc Sun Jun 22 03:05:02 2025: /home/ruisdael/miniconda3/bin/ncatted -O -a comment,global,o,c,- -a contributors,global,o,c,Marc Schleiss, Saverio Guzzo, Rob Mackenzie, Andre Castro, Mahaut Sourzac -a frequency,global,o,c,24.23 GHz -a institution,global,o,c,Delft University of Technology -a instrument_firmware,global,o,c,MRR Pro 1.2.5 -a instrument_id,global,o,c,MRRPro62 -a instrument_name,global,o,c,METEK MRR Pro -a instrument_serial_number,global,o,c,0513108322 -a instrument_type,global,o,c,MRR Pro by Metek GmbH -a sensor_name,global,o,c,MRR005 -a project_name,global,o,c,https://ruisdael-observatory.nl/ -a sampling_frequency,global,o,c,500 kHz -a site_name,global,o,c,Slufter -a source,global,o,c,vertically profiling micro rain radar -a wavelength,global,o,c,1.238 cm -a license,global,o,c,https://creativecommons.org/licenses/by-sa/4.0/ --output=/mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc /mnt/c/ftp/mrrpro62/202506/20250622/20250622_000000.nc

  comment :

  -

  instrument_name :

  METEK MRR Pro

  site_name :

  Slufter

  contributors :

  Marc Schleiss, Saverio Guzzo, Rob Mackenzie, Andre Castro, Mahaut Sourzac

  frequency :

  24.23 GHz

  instrument_firmware :

  MRR Pro 1.2.5

  instrument_id :

  MRRPro62

  instrument_serial_number :

  0513108322

  instrument_type :

  MRR Pro by Metek GmbH

  sensor_name :

  MRR005

  project_name :

  https://ruisdael-observatory.nl/

  sampling_frequency :

  500 kHz

  wavelength :

  1.238 cm

  license :

  https://creativecommons.org/licenses/by-sa/4.0/

  NCO :

  netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

# Single variable visualization
plt.figure(figsize=(15,4))
data['RR'].plot(y='range', vmin=0, vmax=5, cmap='YlOrRd')
plt.show()