{"notebook_content":"\n\n\n\n\nNotebook\n\n\n\n\n\n\n\n\n\n\n\n\n
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Comparing ACTRIS data and CAMS forecast model with Providentia\u00b6

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Import libraries\u00b6

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In\u00a0[1]:
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%matplotlib inline\n
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In\u00a0[2]:
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import sys\nsys.path.append('/tljh-home/shared/providentia')\n
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In\u00a0[3]:
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import providentia as prv\n
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Load configuration file\u00b6

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We are going to load hourly data for nitrogen dioxide mass concentration from the ACTRIS framework and CAMS forecast data for the year November 2024.

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In\u00a0[4]:
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provi = prv.Providentia('actris_vre_cams.conf')\n
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In\u00a0[5]:
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provi.print_config()\n
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[ACTRIS-CAMS]\nframework = actris/actris\nspecies = nitrogen dioxide mass concentration\nresolution = hourly\nmodel = cams_forecast_ensemble (CAMS)\ndomain = regional\nstart_date = 20241101\nend_date = 20241201\nreport_type = standard\nreport_summary = True\nreport_stations = False\nreport_filename = PROVIDENTIA_Report\nreport_title = Report\ntemporal_colocation = True\nobservations_data_label = Nitrogen Dioxide Mass Concentration\nforecast = day\ndl_interpolated = False\ndl_mode = both\ndl_thredds_update = False\n
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Download data\u00b6

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Now we format and download the data from NILU Thredds server (https://thredds.nilu.no/thredds/catalog/ebas_doi/catalog.html) into the path defined in settings/data_paths.json for local machines.

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We will also be downloading CAMS European air quality forecast model data from the Atmosphere Data Store (https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=download). For this to work, you need to create an account on the ADS in advance and follow these steps to set it up: https://providentia.readthedocs.io/en/latest/ADS.html.

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In\u00a0[6]:
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provi.download()\n
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Starting Providentia download...\n\nStarting ACTRIS-CAMS section download...\n
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\nThere are some files that were already downloaded in a previous download, do you want to overwrite them ([y]/n)?  y\n
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\n----------------------------------------\n\nDownloading ACTRIS framework data from EBAS DOI...\n\n  - /home/avilanov/data/providentia/obs/nonghost/actris/actris/hourly/sconcno2, source: https://prod-actris-md2.nilu.no/metadata/content/nitrogen dioxide mass concentration\n
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    Processing station data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 48/48
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    === ERRORS (6) ===\n    ['https://thredds.nilu.no/thredds/dodsC/ebas_doi/3T/CW/G2/3TCW-G2TJ.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/G3/TK/S4/G3TK-S4GD.nc'] (CH0053R): Selecting station file: could not convert string to float: np.str_('3a').\n    ['https://thredds.nilu.no/thredds/dodsC/ebas_doi/7G/GF/HG/7GGF-HG46.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/PY/KN/ER/PYKN-ERES.nc'] (DE0043G): Selecting station file: could not convert string to float: np.str_('3a').\n    ['https://thredds.nilu.no/thredds/dodsC/ebas_doi/92/BY/2S/92BY-2SAU.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/97/F4/8A/97F4-8AMW.nc'] (FR0030R): Selecting station file: could not convert string to float: np.str_('3a').\n    ['https://thredds.nilu.no/thredds/dodsC/ebas_doi/FE/KU/SS/FEKU-SSXF.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/J4/B9/63/J4B9-63A7.nc'] (CZ0003R): Selecting station file: could not convert string to float: np.str_('3a').\n    ['https://thredds.nilu.no/thredds/dodsC/ebas_doi/FS/JH/28/FSJH-28FN.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/K8/CT/FE/K8CT-FE2D.nc'] (FR0020R): Selecting station file: could not convert string to float: np.str_('3a').\n    ['https://thredds.nilu.no/thredds/dodsC/ebas_doi/H9/G3/AM/H9G3-AM3S.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/U8/PS/5F/U8PS-5FE2.nc'] (CV0001G): Selecting station file: could not convert string to float: np.str_('3a').\n
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\n    Downloading files (1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588|1/1\n
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\n\n\n----------------------------------------\n\nDownloading cams_forecast_ensemble-regional-000 model data from the Atmosphere/Climate Data Store...\nWarning: Model data will be downloaded 5 day(s) in advance relative to the configured date.\n\ndataset = 'cams-europe-air-quality-forecasts'\nrequest = {\n'variable' : 'nitrogen_dioxide',\n'leadtime_hour' : ['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', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'],\n'date' : '2024-10-27/2024-10-31',\n'model' : 'ensemble',\n'level' : '0',\n'time' : '00:00',\n'type' : 'forecast',\n'data_format' : 'netcdf_zip',\n}\n\nDownloading https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-3/2026-06-12/5bac7d62098c26a145fac511de71fd97.zip\n
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Formatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241027.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241028.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241029.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241030.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241031.nc\n\ndataset = 'cams-europe-air-quality-forecasts'\nrequest = {\n'variable' : 'nitrogen_dioxide',\n'leadtime_hour' : ['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', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'],\n'date' : '2024-11-01/2024-11-30',\n'model' : 'ensemble',\n'level' : '0',\n'time' : '00:00',\n'type' : 'forecast',\n'data_format' : 'netcdf_zip',\n}\n\nDownloading https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-3/2026-06-12/e51ab5c5c2ef9a84eac8ee8be93b8adf.zip\n
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Formatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241101.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241102.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241103.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241104.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241105.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241106.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241107.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241108.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241109.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241110.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241111.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241112.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241113.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241114.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241115.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241116.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241117.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241118.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241119.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241120.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241121.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241122.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241123.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241124.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241125.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241126.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241127.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241128.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241129.nc\nFormatting /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2/sconcno2_20241130.nc\n\n
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Interpolate data\u00b6

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In order to see the model data in the observations space, we need to interpolate it. Inside this function we do a nearest-neighbor interpolation.

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In\u00a0[7]:
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provi.interpolate()\n
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Starting Providentia interpolation...\n\nStarting ACTRIS-CAMS section interpolation...\n\nWarning: During interpolation, multiprocessing must be turned on for local runs, activating...\nVariables used for the interpolation:\n\nghost_version: 1.5\nstart_date: 202411\nend_date: 202412\nmodels:\n - cams_forecast_ensemble-regional-000 (CAMS)\nspecies: ['sconcno2']\nnetwork: ['actris/actris']\nresolution: ['hourly']\nmodel_resolution: []\nforecast: ['day']\ninterp_spinup_timesteps: 0\ninterp_model_downsampling: mean\ninterp_model_upsampling: fill\ninterp_n_neighbours: 4\ninterp_reverse_vertical_orientation: False\ninterp_chunk_size: 16\ninterp_job_array_limit: 100\nmod_root: /home/avilanov/data/providentia/mod\nghost_root: /home/avilanov/data/providentia/obs/ghost\nnonghost_root: /home/avilanov/data/providentia/obs/nonghost\nmod_to_interp_root: /home/avilanov/data/providentia/mod_to_interp\ninterp_multiprocessing: True\n\nMODEL: CAMS\n\n2 observation file(s) for actris/actris and hourly resolution were found in /home/avilanov/data/providentia/obs/nonghost/actris/actris/hourly/sconcno2.\n70 model file(s) for hourly resolution were found in /home/avilanov/data/providentia/mod_to_interp/cams_forecast_ensemble/regional/hourly/sconcno2.\n\n***INTERSECTING OBSERVATIONAL AND EXPERIMENTAL DATA IS AVAILABLE FOR INTERPOLATION.***\n\nUsing multiprocessing to manage the job submission.\nUsing 1 out of 12 available CPUs to ensure that other processes keep running. \nIf you encounter any problems consider reducing the number of CPUS by running Providentia using --cores (./bin/providentia --cores=2).\n\nSubmitting job with arguments: [cams_forecast_ensemble-regional-000 sconcno2 actris/actris hourly 202411]\n1/1 jobs completed\n\nALL 1 INTERPOLATION TASKS COMPLETED SUCCESSFULLY IN 0.56 MINUTES\n
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Read data\u00b6

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It is time to explore the data, for this we need to first load it in memory.

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In\u00a0[8]:
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provi.load()\n
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\nUpdating file tree /home/avilanov/software/providentia/settings/internal/ghost_filetree_1.5.json...\nUpdating file tree /home/avilanov/software/providentia/settings/internal/nonghost_filetree.json...\nReading data\n\nOBSERVATIONS\n - actris/actris|sconcno2\nMODELS\n - cams_forecast_ensemble-regional-000-day1 (CAMS-day1)\n - cams_forecast_ensemble-regional-000-day2 (CAMS-day2)\n - cams_forecast_ensemble-regional-000-day3 (CAMS-day3)\n - cams_forecast_ensemble-regional-000-day4 (CAMS-day4)\nResetting data filters to when class was initialised, loading ACTRIS-CAMS subsection filters.\nFiltering data\n
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In\u00a0[9]:
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data = provi.data(format='xr')\ndata\n
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Out[9]:
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<xarray.Dataset> Size: 631kB\nDimensions:                                                        (\n                                                                    data_label: 5,\n                                                                    station_actris_actris|sconcno2: 39,\n                                                                    time: 720,\n                                                                    month: 1)\nCoordinates:\n  * time                                                           (time) datetime64[ns] 6kB ...\nDimensions without coordinates: data_label, station_actris_actris|sconcno2,\n                                month\nData variables: (12/29)\n    data_labels                                                    (data_label) <U35 700B ...\n    actris_actris|sconcno2_data                                    (data_label, station_actris_actris|sconcno2, time) float32 562kB ...\n    actris_actris|sconcno2_station_reference                       (station_actris_actris|sconcno2, month) <U7 1kB ...\n    actris_actris|sconcno2_longitude                               (station_actris_actris|sconcno2, month) float64 312B ...\n    actris_actris|sconcno2_latitude                                (station_actris_actris|sconcno2, month) float64 312B ...\n    actris_actris|sconcno2_station_name                            (station_actris_actris|sconcno2, month) <U22 3kB ...\n    ...                                                             ...\n    actris_actris|sconcno2_contact_name                            (station_actris_actris|sconcno2, month) <U63 10kB ...\n    actris_actris|sconcno2_contact_institution                     (station_actris_actris|sconcno2, month) <U4 624B ...\n    actris_actris|sconcno2_contact_email_address                   (station_actris_actris|sconcno2, month) <U12 2kB ...\n    actris_actris|sconcno2_data_licence                            (station_actris_actris|sconcno2, month) <U3 468B ...\n    actris_actris|sconcno2_doi                                     (station_actris_actris|sconcno2, month) <U4 624B ...\n    actris_actris|sconcno2_actris_national_facility                (station_actris_actris|sconcno2, month) <U5 780B ...\nAttributes:\n    title:        Saved data from Providentia\n    institution:  Barcelona Supercomputing Center\n    source:       Providentia
xarray.Dataset
    • data_label: 5
    • station_actris_actris|sconcno2: 39
    • time: 720
    • month: 1
    • time
      (time)
      datetime64[ns]
      2024-11-01 ... 2024-11-30T23:00:00
      standard_name :
      time
      long_name :
      time
      description :
      Time in hours since 2024-11-01 00:00 UTC. Time given refers to the start of the time window the measurement is representative of (temporal resolution).
      axis :
      T
      tz :
      UTC
      array(['2024-11-01T00:00:00.000000000', '2024-11-01T01:00:00.000000000',\n       '2024-11-01T02:00:00.000000000', ..., '2024-11-30T21:00:00.000000000',\n       '2024-11-30T22:00:00.000000000', '2024-11-30T23:00:00.000000000'],\n      shape=(720,), dtype='datetime64[ns]')
    • data_labels
      (data_label)
      <U35
      'Nitrogen Dioxide Mass Concentra...
      standard_name :
      data_labels
      long_name :
      data_labels
      description :
      Labels associated with each data array, e.g. observations, model_1, etc.
      array(['Nitrogen Dioxide Mass Concentration', 'CAMS-day1', 'CAMS-day2',\n       'CAMS-day3', 'CAMS-day4'], dtype='<U35')
    • actris_actris|sconcno2_data
      (data_label, station_actris_actris|sconcno2, time)
      float32
      0.1081 0.1044 ... 1.258 1.141
      standard_name :
      nitrogen dioxide
      long_name :
      nitrogen dioxide
      units :
      nmol mol-1
      description :
      Measured value of surface nitrogen dioxide for the stated temporal resolution.
      resolution :
      hourly
      resampling_resolution :
      None
      start_date :
      20241101
      end_date :
      20241201
      temporal_colocation :
      True
      spatial_colocation :
      True
      filter_species :
      {}
      forecast :
      ['day']
      calibration_factor :
      {}
      remove_extreme_stations :
      None
      statistic_mode :
      Temporal|Spatial
      statistic_aggregation :
      Median
      periodic_statistic_mode :
      Independent
      periodic_statistic_aggregation :
      Median
      timeseries_statistic_aggregation :
      Median
      spatial_colocation_tolerance :
      19.053
      spatial_colocation_station_reference :
      True
      spatial_colocation_station_name :
      True
      spatial_colocation_longitude_latitude :
      True
      spatial_colocation_measurement_altitude :
      True
      spatial_colocation_validation :
      True
      spatial_colocation_validation_tolerance :
      10000.0
      array([[[ 0.1081    ,  0.1044    ,  0.1044    , ...,  0.1456    ,\n          0.1362    ,  0.1344    ],\n        [ 1.2       ,  0.8       ,  0.5       , ...,  6.2       ,\n          6.1       ,  5.5       ],\n        [ 1.83      ,  1.52      ,  1.8299912 , ...,  5.78      ,\n                 nan,         nan],\n        ...,\n        [ 8.89      ,  7.62      ,  7.81      , ...,  7.08      ,\n          7.25      ,  7.51      ],\n        [ 0.8599994 ,  0.84      ,  0.84      , ...,  0.54000115,\n          0.58      ,  0.68      ],\n        [ 0.681     ,  0.694     ,  0.687     , ...,  1.237     ,\n          1.369     ,  1.519     ]],\n\n       [[ 0.28936833,  0.264558  ,  0.22955692, ...,  0.21422814,\n          0.18671042,  0.16415396],\n        [ 3.9153981 ,  3.638523  ,  3.3475795 , ...,  4.544748  ,\n          4.407166  ,  4.245712  ],\n        [ 8.641489  ,  7.7312    ,  5.7538195 , ...,  6.708142  ,\n                 nan,         nan],\n...\n        [11.606844  , 11.675176  , 11.696394  , ...,  9.48593   ,\n          8.941064  ,  9.195124  ],\n        [ 0.6983912 ,  0.6693007 ,  0.6711319 , ...,  1.0383463 ,\n          1.1330887 ,  1.337833  ],\n        [ 0.6146215 ,  0.54766774,  0.51533794, ...,  1.2137526 ,\n          1.2564213 ,  1.2346132 ]],\n\n       [[ 0.28353882,  0.24788448,  0.2156254 , ...,  0.2561922 ,\n          0.21549767,  0.17778215],\n        [ 4.080958  ,  3.6677926 ,  3.0815    , ...,  3.668224  ,\n          3.9382844 ,  4.26908   ],\n        [ 7.2228403 ,  6.6661253 ,  6.5867205 , ...,  5.1680193 ,\n                 nan,         nan],\n        ...,\n        [13.666465  , 14.136363  , 13.806133  , ...,  8.555674  ,\n          8.542054  ,  8.801542  ],\n        [ 0.6892451 ,  0.6612716 ,  0.63843554, ...,  1.4759581 ,\n          1.4430975 ,  1.3208728 ],\n        [ 0.4878093 ,  0.44765663,  0.42100197, ...,  1.3193734 ,\n          1.2584934 ,  1.1411233 ]]], shape=(5, 39, 720), dtype=float32)
    • actris_actris|sconcno2_station_reference
      (station_actris_actris|sconcno2, month)
      <U7
      'AT0034G' 'BE0001R' ... 'SI0008R'
      standard_name :
      station reference
      long_name :
      station reference identifier
      units :
      unitless
      description :
      reference ID for station.
      array([['AT0034G'],\n       ['BE0001R'],\n       ['BE0013R'],\n       ['BE0032R'],\n       ['BE0035R'],\n       ['CH0002R'],\n       ['CH0003R'],\n       ['CH0004R'],\n       ['CH0005R'],\n       ['DE0044R'],\n       ['DK0005R'],\n       ['DK0008R'],\n       ['DK0012R'],\n       ['DK0031R'],\n       ['ES0005R'],\n       ['ES0014R'],\n       ['FI0009R'],\n       ['FI0022R'],\n       ['FI0027R'],\n       ['FI0096G'],\n       ['GB0002R'],\n       ['GB0013R'],\n       ['GB0014R'],\n       ['GB0031R'],\n       ['GB0033R'],\n       ['GB0038R'],\n       ['GB0043R'],\n       ['GB0045R'],\n       ['GB0048R'],\n       ['GB0053R'],\n       ['GB1055R'],\n       ['IT0004R'],\n       ['NL0007R'],\n       ['NL0009R'],\n       ['NL0010R'],\n       ['NL0091R'],\n       ['NL0644R'],\n       ['PL0005R'],\n       ['SI0008R']], dtype='<U7')
    • actris_actris|sconcno2_longitude
      (station_actris_actris|sconcno2, month)
      float64
      12.96 5.204 2.582 ... 22.07 14.87
      standard_name :
      longitude
      long_name :
      longitude
      units :
      decimal degrees East
      description :
      Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.
      axis :
      X
      array([[12.95794 ],\n       [ 5.203611],\n       [ 2.58232 ],\n       [ 6.001019],\n       [ 4.989444],\n       [ 6.944722],\n       [ 8.904722],\n       [ 6.979444],\n       [ 8.46389 ],\n       [12.9277  ],\n       [10.73616 ],\n       [11.516667],\n       [12.085797],\n       [ 8.427486],\n       [-8.92361 ],\n       [ 0.73472 ],\n       [21.377222],\n       [29.401667],\n       [27.55872 ],\n       [24.116111],\n       [-3.204167],\n       [-3.713056],\n       [-0.8075  ],\n       [-3.033056],\n       [-3.205   ],\n       [ 0.179444],\n       [-4.691462],\n       [ 0.292778],\n       [-3.2429  ],\n       [-2.68345 ],\n       [-1.438228],\n       [ 8.633333],\n       [ 6.566667],\n       [ 6.277222],\n       [ 5.853611],\n       [ 4.5     ],\n       [ 4.923611],\n       [22.066667],\n       [14.866667]])
    • actris_actris|sconcno2_latitude
      (station_actris_actris|sconcno2, month)
      float64
      47.05 49.88 51.02 ... 54.15 45.57
      standard_name :
      latitude
      long_name :
      latitude
      units :
      decimal degrees North
      description :
      Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.
      axis :
      Y
      array([[47.05407 ],\n       [49.877778],\n       [51.01628 ],\n       [50.629421],\n       [50.503333],\n       [46.813056],\n       [47.479722],\n       [47.049722],\n       [47.0675  ],\n       [51.525   ],\n       [54.746495],\n       [56.716667],\n       [55.693588],\n       [56.290424],\n       [42.72056 ],\n       [41.39389 ],\n       [59.779167],\n       [66.320278],\n       [60.53774 ],\n       [67.973333],\n       [55.313056],\n       [50.596389],\n       [54.334444],\n       [52.503889],\n       [55.858611],\n       [50.792778],\n       [51.781784],\n       [52.298333],\n       [55.79216 ],\n       [51.05625 ],\n       [51.149617],\n       [45.8     ],\n       [52.083333],\n       [53.333889],\n       [51.541111],\n       [52.3     ],\n       [51.974444],\n       [54.15    ],\n       [45.566667]])
    • actris_actris|sconcno2_station_name
      (station_actris_actris|sconcno2, month)
      <U22
      'Sonnblick' 'Offagne' ... 'Iskrba'
      standard_name :
      station name
      long_name :
      station name
      units :
      unitless
      description :
      Name of station where the measurement was conducted.
      array([['Sonnblick'],\n       ['Offagne'],\n       ['Houtem'],\n       ['Eupen'],\n       ['Vezin'],\n       ['Payerne'],\n       ['T\u00e4nikon'],\n       ['Chaumont'],\n       ['Rigi'],\n       ['Melpitz'],\n       ['Keldsnor'],\n       ['Anholt'],\n       ['Risoe'],\n       ['Ulborg'],\n       ['Noia'],\n       ['Els Torms'],\n       ['Ut\u00f6'],\n       ['Oulanka'],\n       ['Virolahti Harju'],\n       ['Pallas (Sammaltunturi)'],\n       ['Eskdalemuir'],\n       ['Yarner Wood'],\n       ['High Muffles'],\n       ['Aston Hill'],\n       ['Bush'],\n       ['Lullington Heath'],\n       ['Narberth'],\n       ['Wicken Fen'],\n       ['Auchencorth Moss'],\n       ['Charlton Mackrell'],\n       ['Chilbolton Observatory'],\n       ['Ispra'],\n       ['Eibergen'],\n       ['Kollumerwaard'],\n       ['Vredepeel'],\n       ['De Zilk'],\n       ['Cabauw Wielsekade'],\n       ['Diabla Gora'],\n       ['Iskrba']], dtype='<U22')
    • actris_actris|sconcno2_measurement_altitude
      (station_actris_actris|sconcno2, month)
      float32
      3.116e+03 430.0 6.0 ... 161.4 520.0
      standard_name :
      measurement altitude
      long_name :
      measurement altitude relative to mean sea level
      units :
      m
      description :
      Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres.
      array([[3.1160e+03],\n       [4.3000e+02],\n       [6.0000e+00],\n       [2.9500e+02],\n       [1.6000e+02],\n       [4.8900e+02],\n       [5.3900e+02],\n       [1.1370e+03],\n       [1.0310e+03],\n       [8.9500e+01],\n       [1.0000e+01],\n       [4.0000e+01],\n       [3.0000e+00],\n       [1.0000e+01],\n       [6.8650e+02],\n       [4.7350e+02],\n       [7.0000e+00],\n       [3.1000e+02],\n       [2.4000e+01],\n       [5.7000e+02],\n       [2.4300e+02],\n       [1.1900e+02],\n       [2.6700e+02],\n       [3.7000e+02],\n       [1.8000e+02],\n       [1.2000e+02],\n       [1.6000e+02],\n       [5.0000e+00],\n       [2.6360e+02],\n       [5.4000e+01],\n       [7.8000e+01],\n       [2.1550e+02],\n       [2.2000e+01],\n       [3.0000e+00],\n       [3.0000e+01],\n       [6.0000e+00],\n       [3.0000e+00],\n       [1.6135e+02],\n       [5.2000e+02]], dtype=float32)
    • actris_actris|sconcno2_station_timezone
      (station_actris_actris|sconcno2, month)
      <U3
      'UTC' 'UTC' 'UTC' ... 'UTC' 'UTC'
      standard_name :
      station timezone
      long_name :
      station timezone
      units :
      unitless
      description :
      Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs).
      array([['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC'],\n       ['UTC']], dtype='<U3')
    • actris_actris|sconcno2_altitude
      (station_actris_actris|sconcno2, month)
      float32
      3.106e+03 430.0 2.0 ... 157.0 520.0
      standard_name :
      altitude
      long_name :
      altitude relative to mean sea level
      units :
      m
      description :
      Altitude of the ground level at the station, relative to the stated vertical datum, in metres.
      array([[3.106e+03],\n       [4.300e+02],\n       [2.000e+00],\n       [2.950e+02],\n       [1.600e+02],\n       [4.890e+02],\n       [5.390e+02],\n       [1.137e+03],\n       [1.031e+03],\n       [8.600e+01],\n       [1.000e+01],\n       [4.000e+01],\n       [3.000e+00],\n       [1.000e+01],\n       [6.830e+02],\n       [4.700e+02],\n       [7.000e+00],\n       [3.100e+02],\n       [2.000e+01],\n       [5.650e+02],\n       [2.430e+02],\n       [1.190e+02],\n       [2.670e+02],\n       [3.700e+02],\n       [1.800e+02],\n       [1.200e+02],\n       [1.600e+02],\n       [5.000e+00],\n       [2.600e+02],\n       [5.400e+01],\n       [7.800e+01],\n       [2.090e+02],\n       [2.000e+01],\n       [1.000e+00],\n       [2.800e+01],\n       [4.000e+00],\n       [1.000e+00],\n       [1.570e+02],\n       [5.200e+02]], dtype=float32)
    • actris_actris|sconcno2_sampling_height
      (station_actris_actris|sconcno2, month)
      float32
      10.0 0.0 4.0 0.0 ... 2.0 4.35 0.0
      standard_name :
      sampling height
      long_name :
      sampling height relative to ground
      units :
      m
      description :
      Height above the ground of the inlet/instrument/sampler, in metres.
      array([[10.  ],\n       [ 0.  ],\n       [ 4.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 3.5 ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 3.5 ],\n       [ 3.5 ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 4.  ],\n       [ 5.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 3.6 ],\n       [ 0.  ],\n       [ 0.  ],\n       [ 6.5 ],\n       [ 2.  ],\n       [ 2.  ],\n       [ 2.  ],\n       [ 2.  ],\n       [ 2.  ],\n       [ 4.35],\n       [ 0.  ]], dtype=float32)
    • actris_actris|sconcno2_data_level
      (station_actris_actris|sconcno2, month)
      <U1
      '2' '2' '2' '2' ... '2' '2' '2' '2'
      standard_name :
      data level
      long_name :
      data level
      units :
      unitless
      description :
      Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as \"variable\". If there is no reported data level this is set as \"none\"
      array([['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2'],\n       ['2']], dtype='<U1')
    • actris_actris|sconcno2_country
      (station_actris_actris|sconcno2, month)
      <U14
      'Austria' 'Belgium' ... 'Slovenia'
      standard_name :
      country
      long_name :
      country
      units :
      unitless
      description :
      Name of the country the station is located in.
      array([['Austria'],\n       ['Belgium'],\n       ['Belgium'],\n       ['Belgium'],\n       ['Belgium'],\n       ['Switzerland'],\n       ['Switzerland'],\n       ['Switzerland'],\n       ['Switzerland'],\n       ['Germany'],\n       ['Denmark'],\n       ['Denmark'],\n       ['Denmark'],\n       ['Denmark'],\n       ['Spain'],\n       ['Spain'],\n       ['Finland'],\n       ['Finland'],\n       ['Finland'],\n       ['Finland'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['United Kingdom'],\n       ['Italy'],\n       ['Netherlands'],\n       ['Netherlands'],\n       ['Netherlands'],\n       ['Netherlands'],\n       ['Netherlands'],\n       ['Poland'],\n       ['Slovenia']], dtype='<U14')
    • actris_actris|sconcno2_network
      (station_actris_actris|sconcno2, month)
      <U3
      'nan' 'nan' 'nan' ... 'nan' 'nan'
      standard_name :
      network
      long_name :
      data network
      units :
      unitless
      description :
      The name of the network which reports data for the specific station in question.
      array([['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan']], dtype='<U3')
    • actris_actris|sconcno2_associated_networks
      (station_actris_actris|sconcno2, month)
      <U29
      \"['EMEP', 'GAW-WDCRG']\" ... \"['G...
      standard_name :
      other associated networks
      long_name :
      other associated networks
      units :
      unitless
      description :
      String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2
      array([[\"['EMEP', 'GAW-WDCRG']\"],\n       [\"['CAMP', 'EMEP']\"],\n       [\"['EMEP', 'CAMP']\"],\n       [\"['CAMP', 'EMEP']\"],\n       [\"['CAMP', 'EMEP']\"],\n       [\"['EMEP', 'GAW-WDCRG']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP', 'GAW-WDCRG']\"],\n       [\"['ACTRIS', 'EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['HELCOM', 'EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['HELCOM', 'EMEP']\"],\n       [\"['EMEP', 'AMAP']\"],\n       [\"['EMEP']\"],\n       [\"['GAW-WDCRG', 'EMEP', 'AMAP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP', 'CAMP']\"],\n       [\"['CAMP', 'EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['ACTRIS', 'EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['GAW-WDCRG', 'CAMP', 'EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['CAMP', 'EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['EMEP']\"],\n       [\"['GAW-WDCRG', 'EMEP']\"]], dtype='<U29')
    • actris_actris|sconcno2_land_use
      (station_actris_actris|sconcno2, month)
      <U16
      'Gravel and stone' 'nan' ... 'nan'
      standard_name :
      land use
      long_name :
      standardised network provided land use type
      units :
      unitless
      description :
      Standardised network provided classification, describing the dominant land use in the area of the reporting station.
      array([['Gravel and stone'],\n       ['nan'],\n       ['Agricultural'],\n       ['nan'],\n       ['nan'],\n       ['Agricultural'],\n       ['nan'],\n       ['nan'],\n       ['Grassland'],\n       ['Agricultural'],\n       ['nan'],\n       ['Forest'],\n       ['Agricultural'],\n       ['nan'],\n       ['nan'],\n       ['Agricultural'],\n       ['nan'],\n       ['nan'],\n       ['Agricultural'],\n       ['Forest'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['Agricultural'],\n       ['nan'],\n       ['nan'],\n       ['Other'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan']], dtype='<U16')
    • actris_actris|sconcno2_WMO_region
      (station_actris_actris|sconcno2, month)
      <U6
      'Europe' 'Europe' ... 'Europe'
      standard_name :
      WMO region
      long_name :
      WMO region code
      units :
      unitless
      description :
      World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, \"Northern America, Central America and the Caribbean\", South-West Pacific, Europe and Antarctica.
      array([['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['nan'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe'],\n       ['Europe']], dtype='<U6')
    • actris_actris|sconcno2_measurement_methodology
      (station_actris_actris|sconcno2, month)
      <U41
      'AT03L_chemi' ... 'SI01L_epa_caps'
      standard_name :
      measurement methodology
      long_name :
      standardised measurement methodology
      units :
      unitless
      description :
      Standardised name of the measurement methodology.
      array([['AT03L_chemi'],\n       ['BE02L_chemilum'],\n       ['BE01L_EN14211'],\n       ['BE02L_chemilum'],\n       ['BE02L_chemilum'],\n       ['CH01L_no2'],\n       ['CH01L_no2'],\n       ['CH01L_no2'],\n       ['CH01L_no2'],\n       ['DE08L_chemiluminescence_photolytic_T200UP'],\n       ['DK01L_Chemiluminescence'],\n       ['DK01L_Chemiluminescence'],\n       ['DK01L_Chemiluminescence'],\n       ['DK01L_Chemiluminescence'],\n       ['ES04L_chemilum'],\n       ['ES04L_chemilum'],\n       ['FI01L_chemilum'],\n       ['FI01L_chemilum'],\n       ['FI01L_chemilum'],\n       ['FI01L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['GB03L_AUC_T500U'],\n       ['GB02L_chemilum'],\n       ['GB02L_chemilum'],\n       ['IT04L_Chemilum_1'],\n       ['NL01L_MIL-W-5300_NL01L_ILG-W-2900'],\n       ['NL01L_MIL-W-5300_NL01L_ILG-W-2900'],\n       ['NL01L_MIL-W-5300_NL01L_ILG-W-2900'],\n       ['NL01L_MIL-W-5300_NL01L_ILG-W-2900'],\n       ['NL01L_MIL-W-5300_NL01L_ILG-W-2900'],\n       ['PL05L_ANOxTHERMO'],\n       ['SI01L_epa_caps']], dtype='<U41')
    • actris_actris|sconcno2_measuring_instrument_name
      (station_actris_actris|sconcno2, month)
      <U28
      'Thermo/42i' ... 'Teledyne/T500U'
      standard_name :
      measuring instrument name
      long_name :
      standardised measuring instrument name
      units :
      unitless
      description :
      Standardised name of the measuring instrument.
      array([['Thermo/42i'],\n       ['Chemiluminescence photolytic'],\n       ['Thermo/42i'],\n       ['Chemiluminescence photolytic'],\n       ['Chemiluminescence photolytic'],\n       ['Teledyne/T500U'],\n       ['Horiba/APNA-370'],\n       ['Thermo/42i-TL'],\n       ['Teledyne/T500U'],\n       ['Teledyne/T200UP'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Thermo/42i-TL'],\n       ['Thermo/42i-TL'],\n       ['Thermo/42i-TL'],\n       ['Thermo/42i-TL'],\n       ['Thermo/42i-TL'],\n       ['Thermo/42i-TL'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Teledyne/T500U'],\n       ['Chemiluminescence molybdenum'],\n       ['Chemiluminescence molybdenum'],\n       ['Thermo/42i'],\n       ['Teledyne API/200E'],\n       ['Teledyne API/200E'],\n       ['Teledyne API/200E'],\n       ['Teledyne API/200E'],\n       ['Teledyne API/200E'],\n       ['Thermo/42i'],\n       ['Teledyne/T500U']], dtype='<U28')
    • actris_actris|sconcno2_measuring_instrument_sampling_type
      (station_actris_actris|sconcno2, month)
      <U54
      \"['chemiluminescence detector, m...
      standard_name :
      measuring instrument sampling type
      long_name :
      standardised sampling type of the measuring instrument
      units :
      unitless
      description :
      Standardised name of the measuring instrument sampling type.
      array([[\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['cavity attenuated phase shift spectrometer']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['cavity attenuated phase shift spectrometer']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, photolytical converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['cavity attenuated phase shift spectrometer']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['chemiluminescence detector, molybdenum converter']\"],\n       [\"['cavity attenuated phase shift spectrometer']\"]], dtype='<U54')
    • actris_actris|sconcno2_measuring_instrument_inlet_information
      (station_actris_actris|sconcno2, month)
      <U3
      'nan' 'nan' 'nan' ... 'nan' 'nan'
      standard_name :
      measuring instrument inlet information
      long_name :
      measuring instrument measurement inlet information
      units :
      unitless
      description :
      Description of sampling inlet of the measuring instrument.
      array([['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan']], dtype='<U3')
    • actris_actris|sconcno2_principal_investigator_name
      (station_actris_actris|sconcno2, month)
      <U63
      'Iris Buxbaum' ... 'Mateja Gjere...
      standard_name :
      principal investigator name
      long_name :
      principal investigator name
      units :
      unitless
      description :
      Full name of the principal scientific investigator for the specific reported data.
      array([['Iris Buxbaum'],\n       ['Charlotte Vanpoucke, Frans Fierens'],\n       ['Christine Matheeussen'],\n       ['Charlotte Vanpoucke, Frans Fierens'],\n       ['Charlotte Vanpoucke, Frans Fierens'],\n       ['Christoph Hueglin'],\n       ['Christoph Hueglin'],\n       ['Christoph Hueglin'],\n       ['Christoph Hueglin'],\n       ['Laurent Poulain'],\n       ['Kaare Kemp, Rune Keller'],\n       ['Kaare Kemp, Rune Keller'],\n       ['Rune Keller'],\n       ['Rune Keller'],\n       ['Jose Antonio Fernandez Monistrol, Jose Luis Cervantes Rodriguez'],\n       ['Jose Antonio Fernandez Monistrol, Jose Luis Cervantes Rodriguez'],\n       ['Hannele Hakola, Heidi Hell\u00e9n'],\n       ['Hannele Hakola, Heidi Hell\u00e9n'],\n       ['Heidi Hell\u00e9n'],\n       ['Karri Saarnio'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['James Cash, Sarah Leeson'],\n       ['Keith Vincent, Willis Paul'],\n       ['Willis Paul'],\n       ['F Barbiere, C Tarricone'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Zdzislaw Przadka, Marcin Syrzycki'],\n       ['Mateja Gjerek, Marijana Murovec']], dtype='<U63')
    • actris_actris|sconcno2_principal_investigator_institution
      (station_actris_actris|sconcno2, month)
      <U4
      'NILU' 'NILU' ... 'NILU' 'NILU'
      standard_name :
      principal investigator institution
      long_name :
      principal investigator institution
      units :
      unitless
      description :
      Institution of the principal scientific investigator for the specific reported data.
      array([['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU']], dtype='<U4')
    • actris_actris|sconcno2_principal_investigator_email_address
      (station_actris_actris|sconcno2, month)
      <U12
      'ebas@nilu.no' ... 'ebas@nilu.no'
      standard_name :
      principal investigator email address
      long_name :
      principal investigator email address
      units :
      unitless
      description :
      Email address of the principal scientific investigator for the specific reported data.
      array([['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no']], dtype='<U12')
    • actris_actris|sconcno2_contact_name
      (station_actris_actris|sconcno2, month)
      <U63
      'Iris Buxbaum' ... 'Mateja Gjere...
      standard_name :
      contact name
      long_name :
      contact name
      units :
      unitless
      description :
      Full name of the principal data contact for the specific reported data.
      array([['Iris Buxbaum'],\n       ['Charlotte Vanpoucke, Frans Fierens'],\n       ['Christine Matheeussen'],\n       ['Charlotte Vanpoucke, Frans Fierens'],\n       ['Charlotte Vanpoucke, Frans Fierens'],\n       ['Christoph Hueglin'],\n       ['Christoph Hueglin'],\n       ['Christoph Hueglin'],\n       ['Christoph Hueglin'],\n       ['Laurent Poulain'],\n       ['Kaare Kemp, Rune Keller'],\n       ['Kaare Kemp, Rune Keller'],\n       ['Rune Keller'],\n       ['Rune Keller'],\n       ['Jose Antonio Fernandez Monistrol, Jose Luis Cervantes Rodriguez'],\n       ['Jose Antonio Fernandez Monistrol, Jose Luis Cervantes Rodriguez'],\n       ['Hannele Hakola, Heidi Hell\u00e9n'],\n       ['Hannele Hakola, Heidi Hell\u00e9n'],\n       ['Heidi Hell\u00e9n'],\n       ['Karri Saarnio'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['Keith Vincent, Willis Paul'],\n       ['James Cash, Sarah Leeson'],\n       ['Keith Vincent, Willis Paul'],\n       ['Willis Paul'],\n       ['F Barbiere, C Tarricone'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Ronald Spoor, Lise Bjerk'],\n       ['Zdzislaw Przadka, Marcin Syrzycki'],\n       ['Mateja Gjerek, Marijana Murovec']], dtype='<U63')
    • actris_actris|sconcno2_contact_institution
      (station_actris_actris|sconcno2, month)
      <U4
      'NILU' 'NILU' ... 'NILU' 'NILU'
      standard_name :
      contact institution
      long_name :
      contact institution
      units :
      unitless
      description :
      Institution of the principal data contact for the specific reported data.
      array([['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU'],\n       ['NILU']], dtype='<U4')
    • actris_actris|sconcno2_contact_email_address
      (station_actris_actris|sconcno2, month)
      <U12
      'ebas@nilu.no' ... 'ebas@nilu.no'
      standard_name :
      contact email address
      long_name :
      contact email address
      units :
      unitless
      description :
      Email address of the principal data contact for the specific reported data.
      array([['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no'],\n       ['ebas@nilu.no']], dtype='<U12')
    • actris_actris|sconcno2_data_licence
      (station_actris_actris|sconcno2, month)
      <U3
      'nan' 'nan' 'nan' ... 'nan' 'nan'
      standard_name :
      data licence
      long_name :
      data licence
      units :
      unitless
      description :
      Information pertaining to the data licence governing the redistribution/publication of the ingested network data.
      array([['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan'],\n       ['nan']], dtype='<U3')
    • actris_actris|sconcno2_doi
      (station_actris_actris|sconcno2, month)
      <U4
      'xbi0' 'tt6j' ... 'n82y' 'ey6p'
      standard_name :
      DOI
      long_name :
      Digital identifier of an object
      units :
      unitless
      description :
      Digital identifier of an object.
      array([['xbi0'],\n       ['tt6j'],\n       ['mn2n'],\n       ['wjl0'],\n       ['ey59'],\n       ['7tbf'],\n       ['xxcc'],\n       ['c694'],\n       ['oyyc'],\n       ['fhhq'],\n       ['usxg'],\n       ['04ih'],\n       ['biuu'],\n       ['2bke'],\n       ['pu8v'],\n       ['dgvq'],\n       ['4iaj'],\n       ['9w63'],\n       ['Q7NI'],\n       ['8h8z'],\n       ['8a51'],\n       ['4t9b'],\n       ['932g'],\n       ['1ogr'],\n       ['zlsm'],\n       ['i56u'],\n       ['7gnn'],\n       ['78ut'],\n       ['cd8l'],\n       ['lxba'],\n       ['bf7p'],\n       ['j133'],\n       ['yk3j'],\n       ['btdz'],\n       ['12xt'],\n       ['bv99'],\n       ['5a4d'],\n       ['n82y'],\n       ['ey6p']], dtype='<U4')
    • actris_actris|sconcno2_actris_national_facility
      (station_actris_actris|sconcno2, month)
      <U5
      'True' 'False' ... 'False' 'False'
      standard_name :
      ACTRIS national facility
      long_name :
      Digital identifier of an object
      units :
      unitless
      description :
      ACTRIS national facility.
      array([['True'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['True'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['True'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['True'],\n       ['False'],\n       ['False'],\n       ['True'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['True'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False'],\n       ['False']], dtype='<U5')
  • title :
    Saved data from Providentia
    institution :
    Barcelona Supercomputing Center
    source :
    Providentia
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The variable data is stored in actris_actris|sconcno2_data by label (in this case only observations), station and time.

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In\u00a0[10]:
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data['actris_actris|sconcno2_data']\n
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Out[10]:
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<xarray.DataArray 'actris_actris|sconcno2_data' (data_label: 5,\n                                                 station_actris_actris|sconcno2: 39,\n                                                 time: 720)> Size: 562kB\narray([[[ 0.1081    ,  0.1044    ,  0.1044    , ...,  0.1456    ,\n          0.1362    ,  0.1344    ],\n        [ 1.2       ,  0.8       ,  0.5       , ...,  6.2       ,\n          6.1       ,  5.5       ],\n        [ 1.83      ,  1.52      ,  1.8299912 , ...,  5.78      ,\n                 nan,         nan],\n        ...,\n        [ 8.89      ,  7.62      ,  7.81      , ...,  7.08      ,\n          7.25      ,  7.51      ],\n        [ 0.8599994 ,  0.84      ,  0.84      , ...,  0.54000115,\n          0.58      ,  0.68      ],\n        [ 0.681     ,  0.694     ,  0.687     , ...,  1.237     ,\n          1.369     ,  1.519     ]],\n\n       [[ 0.28936833,  0.264558  ,  0.22955692, ...,  0.21422814,\n          0.18671042,  0.16415396],\n        [ 3.9153981 ,  3.638523  ,  3.3475795 , ...,  4.544748  ,\n          4.407166  ,  4.245712  ],\n        [ 8.641489  ,  7.7312    ,  5.7538195 , ...,  6.708142  ,\n                 nan,         nan],\n...\n        [11.606844  , 11.675176  , 11.696394  , ...,  9.48593   ,\n          8.941064  ,  9.195124  ],\n        [ 0.6983912 ,  0.6693007 ,  0.6711319 , ...,  1.0383463 ,\n          1.1330887 ,  1.337833  ],\n        [ 0.6146215 ,  0.54766774,  0.51533794, ...,  1.2137526 ,\n          1.2564213 ,  1.2346132 ]],\n\n       [[ 0.28353882,  0.24788448,  0.2156254 , ...,  0.2561922 ,\n          0.21549767,  0.17778215],\n        [ 4.080958  ,  3.6677926 ,  3.0815    , ...,  3.668224  ,\n          3.9382844 ,  4.26908   ],\n        [ 7.2228403 ,  6.6661253 ,  6.5867205 , ...,  5.1680193 ,\n                 nan,         nan],\n        ...,\n        [13.666465  , 14.136363  , 13.806133  , ...,  8.555674  ,\n          8.542054  ,  8.801542  ],\n        [ 0.6892451 ,  0.6612716 ,  0.63843554, ...,  1.4759581 ,\n          1.4430975 ,  1.3208728 ],\n        [ 0.4878093 ,  0.44765663,  0.42100197, ...,  1.3193734 ,\n          1.2584934 ,  1.1411233 ]]], shape=(5, 39, 720), dtype=float32)\nCoordinates:\n  * time     (time) datetime64[ns] 6kB 2024-11-01 ... 2024-11-30T23:00:00\nDimensions without coordinates: data_label, station_actris_actris|sconcno2\nAttributes: (12/26)\n    standard_name:                            nitrogen dioxide\n    long_name:                                nitrogen dioxide\n    units:                                    nmol mol-1\n    description:                              Measured value of surface nitro...\n    resolution:                               hourly\n    resampling_resolution:                    None\n    ...                                       ...\n    spatial_colocation_station_reference:     True\n    spatial_colocation_station_name:          True\n    spatial_colocation_longitude_latitude:    True\n    spatial_colocation_measurement_altitude:  True\n    spatial_colocation_validation:            True\n    spatial_colocation_validation_tolerance:  10000.0
xarray.DataArray
'actris_actris|sconcno2_data'
  • data_label: 5
  • station_actris_actris|sconcno2: 39
  • time: 720
  • 0.1081 0.1044 0.1044 0.1012 0.1 ... 1.302 1.323 1.319 1.258 1.141
    array([[[ 0.1081    ,  0.1044    ,  0.1044    , ...,  0.1456    ,\n          0.1362    ,  0.1344    ],\n        [ 1.2       ,  0.8       ,  0.5       , ...,  6.2       ,\n          6.1       ,  5.5       ],\n        [ 1.83      ,  1.52      ,  1.8299912 , ...,  5.78      ,\n                 nan,         nan],\n        ...,\n        [ 8.89      ,  7.62      ,  7.81      , ...,  7.08      ,\n          7.25      ,  7.51      ],\n        [ 0.8599994 ,  0.84      ,  0.84      , ...,  0.54000115,\n          0.58      ,  0.68      ],\n        [ 0.681     ,  0.694     ,  0.687     , ...,  1.237     ,\n          1.369     ,  1.519     ]],\n\n       [[ 0.28936833,  0.264558  ,  0.22955692, ...,  0.21422814,\n          0.18671042,  0.16415396],\n        [ 3.9153981 ,  3.638523  ,  3.3475795 , ...,  4.544748  ,\n          4.407166  ,  4.245712  ],\n        [ 8.641489  ,  7.7312    ,  5.7538195 , ...,  6.708142  ,\n                 nan,         nan],\n...\n        [11.606844  , 11.675176  , 11.696394  , ...,  9.48593   ,\n          8.941064  ,  9.195124  ],\n        [ 0.6983912 ,  0.6693007 ,  0.6711319 , ...,  1.0383463 ,\n          1.1330887 ,  1.337833  ],\n        [ 0.6146215 ,  0.54766774,  0.51533794, ...,  1.2137526 ,\n          1.2564213 ,  1.2346132 ]],\n\n       [[ 0.28353882,  0.24788448,  0.2156254 , ...,  0.2561922 ,\n          0.21549767,  0.17778215],\n        [ 4.080958  ,  3.6677926 ,  3.0815    , ...,  3.668224  ,\n          3.9382844 ,  4.26908   ],\n        [ 7.2228403 ,  6.6661253 ,  6.5867205 , ...,  5.1680193 ,\n                 nan,         nan],\n        ...,\n        [13.666465  , 14.136363  , 13.806133  , ...,  8.555674  ,\n          8.542054  ,  8.801542  ],\n        [ 0.6892451 ,  0.6612716 ,  0.63843554, ...,  1.4759581 ,\n          1.4430975 ,  1.3208728 ],\n        [ 0.4878093 ,  0.44765663,  0.42100197, ...,  1.3193734 ,\n          1.2584934 ,  1.1411233 ]]], shape=(5, 39, 720), dtype=float32)
    • time
      (time)
      datetime64[ns]
      2024-11-01 ... 2024-11-30T23:00:00
      standard_name :
      time
      long_name :
      time
      description :
      Time in hours since 2024-11-01 00:00 UTC. Time given refers to the start of the time window the measurement is representative of (temporal resolution).
      axis :
      T
      tz :
      UTC
      array(['2024-11-01T00:00:00.000000000', '2024-11-01T01:00:00.000000000',\n       '2024-11-01T02:00:00.000000000', ..., '2024-11-30T21:00:00.000000000',\n       '2024-11-30T22:00:00.000000000', '2024-11-30T23:00:00.000000000'],\n      shape=(720,), dtype='datetime64[ns]')
  • standard_name :
    nitrogen dioxide
    long_name :
    nitrogen dioxide
    units :
    nmol mol-1
    description :
    Measured value of surface nitrogen dioxide for the stated temporal resolution.
    resolution :
    hourly
    resampling_resolution :
    None
    start_date :
    20241101
    end_date :
    20241201
    temporal_colocation :
    True
    spatial_colocation :
    True
    filter_species :
    {}
    forecast :
    ['day']
    calibration_factor :
    {}
    remove_extreme_stations :
    None
    statistic_mode :
    Temporal|Spatial
    statistic_aggregation :
    Median
    periodic_statistic_mode :
    Independent
    periodic_statistic_aggregation :
    Median
    timeseries_statistic_aggregation :
    Median
    spatial_colocation_tolerance :
    19.053
    spatial_colocation_station_reference :
    True
    spatial_colocation_station_name :
    True
    spatial_colocation_longitude_latitude :
    True
    spatial_colocation_measurement_altitude :
    True
    spatial_colocation_validation :
    True
    spatial_colocation_validation_tolerance :
    10000.0
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While the station information is stored in different variables by month, such as actris_actris|sconcno2_station_reference, actris_actris|sconcno2_station_name, actris_actris|sconcno2_latitude and actris_actris|sconcno2_longitude.

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In\u00a0[11]:
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data['actris_actris|sconcno2_station_reference']\n
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Out[11]:
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<xarray.DataArray 'actris_actris|sconcno2_station_reference' (\n                                                              station_actris_actris|sconcno2: 39,\n                                                              month: 1)> Size: 1kB\narray([['AT0034G'],\n       ['BE0001R'],\n       ['BE0013R'],\n       ['BE0032R'],\n       ['BE0035R'],\n       ['CH0002R'],\n       ['CH0003R'],\n       ['CH0004R'],\n       ['CH0005R'],\n       ['DE0044R'],\n       ['DK0005R'],\n       ['DK0008R'],\n       ['DK0012R'],\n       ['DK0031R'],\n       ['ES0005R'],\n       ['ES0014R'],\n       ['FI0009R'],\n       ['FI0022R'],\n       ['FI0027R'],\n       ['FI0096G'],\n       ['GB0002R'],\n       ['GB0013R'],\n       ['GB0014R'],\n       ['GB0031R'],\n       ['GB0033R'],\n       ['GB0038R'],\n       ['GB0043R'],\n       ['GB0045R'],\n       ['GB0048R'],\n       ['GB0053R'],\n       ['GB1055R'],\n       ['IT0004R'],\n       ['NL0007R'],\n       ['NL0009R'],\n       ['NL0010R'],\n       ['NL0091R'],\n       ['NL0644R'],\n       ['PL0005R'],\n       ['SI0008R']], dtype='<U7')\nDimensions without coordinates: station_actris_actris|sconcno2, month\nAttributes:\n    standard_name:  station reference\n    long_name:      station reference identifier\n    units:          unitless\n    description:    reference ID for station.
xarray.DataArray
'actris_actris|sconcno2_station_reference'
  • station_actris_actris|sconcno2: 39
  • month: 1
  • 'AT0034G' 'BE0001R' 'BE0013R' ... 'NL0644R' 'PL0005R' 'SI0008R'
    array([['AT0034G'],\n       ['BE0001R'],\n       ['BE0013R'],\n       ['BE0032R'],\n       ['BE0035R'],\n       ['CH0002R'],\n       ['CH0003R'],\n       ['CH0004R'],\n       ['CH0005R'],\n       ['DE0044R'],\n       ['DK0005R'],\n       ['DK0008R'],\n       ['DK0012R'],\n       ['DK0031R'],\n       ['ES0005R'],\n       ['ES0014R'],\n       ['FI0009R'],\n       ['FI0022R'],\n       ['FI0027R'],\n       ['FI0096G'],\n       ['GB0002R'],\n       ['GB0013R'],\n       ['GB0014R'],\n       ['GB0031R'],\n       ['GB0033R'],\n       ['GB0038R'],\n       ['GB0043R'],\n       ['GB0045R'],\n       ['GB0048R'],\n       ['GB0053R'],\n       ['GB1055R'],\n       ['IT0004R'],\n       ['NL0007R'],\n       ['NL0009R'],\n       ['NL0010R'],\n       ['NL0091R'],\n       ['NL0644R'],\n       ['PL0005R'],\n       ['SI0008R']], dtype='<U7')
  • standard_name :
    station reference
    long_name :
    station reference identifier
    units :
    unitless
    description :
    reference ID for station.
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In\u00a0[12]:
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data['actris_actris|sconcno2_station_name']\n
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Out[12]:
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<xarray.DataArray 'actris_actris|sconcno2_station_name' (\n                                                         station_actris_actris|sconcno2: 39,\n                                                         month: 1)> Size: 3kB\narray([['Sonnblick'],\n       ['Offagne'],\n       ['Houtem'],\n       ['Eupen'],\n       ['Vezin'],\n       ['Payerne'],\n       ['T\u00e4nikon'],\n       ['Chaumont'],\n       ['Rigi'],\n       ['Melpitz'],\n       ['Keldsnor'],\n       ['Anholt'],\n       ['Risoe'],\n       ['Ulborg'],\n       ['Noia'],\n       ['Els Torms'],\n       ['Ut\u00f6'],\n       ['Oulanka'],\n       ['Virolahti Harju'],\n       ['Pallas (Sammaltunturi)'],\n       ['Eskdalemuir'],\n       ['Yarner Wood'],\n       ['High Muffles'],\n       ['Aston Hill'],\n       ['Bush'],\n       ['Lullington Heath'],\n       ['Narberth'],\n       ['Wicken Fen'],\n       ['Auchencorth Moss'],\n       ['Charlton Mackrell'],\n       ['Chilbolton Observatory'],\n       ['Ispra'],\n       ['Eibergen'],\n       ['Kollumerwaard'],\n       ['Vredepeel'],\n       ['De Zilk'],\n       ['Cabauw Wielsekade'],\n       ['Diabla Gora'],\n       ['Iskrba']], dtype='<U22')\nDimensions without coordinates: station_actris_actris|sconcno2, month\nAttributes:\n    standard_name:  station name\n    long_name:      station name\n    units:          unitless\n    description:    Name of station where the measurement was conducted.
xarray.DataArray
'actris_actris|sconcno2_station_name'
  • station_actris_actris|sconcno2: 39
  • month: 1
  • 'Sonnblick' 'Offagne' 'Houtem' ... 'Diabla Gora' 'Iskrba'
    array([['Sonnblick'],\n       ['Offagne'],\n       ['Houtem'],\n       ['Eupen'],\n       ['Vezin'],\n       ['Payerne'],\n       ['T\u00e4nikon'],\n       ['Chaumont'],\n       ['Rigi'],\n       ['Melpitz'],\n       ['Keldsnor'],\n       ['Anholt'],\n       ['Risoe'],\n       ['Ulborg'],\n       ['Noia'],\n       ['Els Torms'],\n       ['Ut\u00f6'],\n       ['Oulanka'],\n       ['Virolahti Harju'],\n       ['Pallas (Sammaltunturi)'],\n       ['Eskdalemuir'],\n       ['Yarner Wood'],\n       ['High Muffles'],\n       ['Aston Hill'],\n       ['Bush'],\n       ['Lullington Heath'],\n       ['Narberth'],\n       ['Wicken Fen'],\n       ['Auchencorth Moss'],\n       ['Charlton Mackrell'],\n       ['Chilbolton Observatory'],\n       ['Ispra'],\n       ['Eibergen'],\n       ['Kollumerwaard'],\n       ['Vredepeel'],\n       ['De Zilk'],\n       ['Cabauw Wielsekade'],\n       ['Diabla Gora'],\n       ['Iskrba']], dtype='<U22')
  • standard_name :
    station name
    long_name :
    station name
    units :
    unitless
    description :
    Name of station where the measurement was conducted.
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In\u00a0[13]:
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data['actris_actris|sconcno2_latitude']\n
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Out[13]:
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<xarray.DataArray 'actris_actris|sconcno2_latitude' (\n                                                     station_actris_actris|sconcno2: 39,\n                                                     month: 1)> Size: 312B\narray([[47.05407 ],\n       [49.877778],\n       [51.01628 ],\n       [50.629421],\n       [50.503333],\n       [46.813056],\n       [47.479722],\n       [47.049722],\n       [47.0675  ],\n       [51.525   ],\n       [54.746495],\n       [56.716667],\n       [55.693588],\n       [56.290424],\n       [42.72056 ],\n       [41.39389 ],\n       [59.779167],\n       [66.320278],\n       [60.53774 ],\n       [67.973333],\n       [55.313056],\n       [50.596389],\n       [54.334444],\n       [52.503889],\n       [55.858611],\n       [50.792778],\n       [51.781784],\n       [52.298333],\n       [55.79216 ],\n       [51.05625 ],\n       [51.149617],\n       [45.8     ],\n       [52.083333],\n       [53.333889],\n       [51.541111],\n       [52.3     ],\n       [51.974444],\n       [54.15    ],\n       [45.566667]])\nDimensions without coordinates: station_actris_actris|sconcno2, month\nAttributes:\n    standard_name:  latitude\n    long_name:      latitude\n    units:          decimal degrees North\n    description:    Geodetic latitude of measuring instrument, in decimal deg...\n    axis:           Y
xarray.DataArray
'actris_actris|sconcno2_latitude'
  • station_actris_actris|sconcno2: 39
  • month: 1
  • 47.05 49.88 51.02 50.63 50.5 46.81 ... 51.54 52.3 51.97 54.15 45.57
    array([[47.05407 ],\n       [49.877778],\n       [51.01628 ],\n       [50.629421],\n       [50.503333],\n       [46.813056],\n       [47.479722],\n       [47.049722],\n       [47.0675  ],\n       [51.525   ],\n       [54.746495],\n       [56.716667],\n       [55.693588],\n       [56.290424],\n       [42.72056 ],\n       [41.39389 ],\n       [59.779167],\n       [66.320278],\n       [60.53774 ],\n       [67.973333],\n       [55.313056],\n       [50.596389],\n       [54.334444],\n       [52.503889],\n       [55.858611],\n       [50.792778],\n       [51.781784],\n       [52.298333],\n       [55.79216 ],\n       [51.05625 ],\n       [51.149617],\n       [45.8     ],\n       [52.083333],\n       [53.333889],\n       [51.541111],\n       [52.3     ],\n       [51.974444],\n       [54.15    ],\n       [45.566667]])
  • standard_name :
    latitude
    long_name :
    latitude
    units :
    decimal degrees North
    description :
    Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.
    axis :
    Y
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In\u00a0[14]:
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data['actris_actris|sconcno2_longitude']\n
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Out[14]:
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<xarray.DataArray 'actris_actris|sconcno2_longitude' (\n                                                      station_actris_actris|sconcno2: 39,\n                                                      month: 1)> Size: 312B\narray([[12.95794 ],\n       [ 5.203611],\n       [ 2.58232 ],\n       [ 6.001019],\n       [ 4.989444],\n       [ 6.944722],\n       [ 8.904722],\n       [ 6.979444],\n       [ 8.46389 ],\n       [12.9277  ],\n       [10.73616 ],\n       [11.516667],\n       [12.085797],\n       [ 8.427486],\n       [-8.92361 ],\n       [ 0.73472 ],\n       [21.377222],\n       [29.401667],\n       [27.55872 ],\n       [24.116111],\n       [-3.204167],\n       [-3.713056],\n       [-0.8075  ],\n       [-3.033056],\n       [-3.205   ],\n       [ 0.179444],\n       [-4.691462],\n       [ 0.292778],\n       [-3.2429  ],\n       [-2.68345 ],\n       [-1.438228],\n       [ 8.633333],\n       [ 6.566667],\n       [ 6.277222],\n       [ 5.853611],\n       [ 4.5     ],\n       [ 4.923611],\n       [22.066667],\n       [14.866667]])\nDimensions without coordinates: station_actris_actris|sconcno2, month\nAttributes:\n    standard_name:  longitude\n    long_name:      longitude\n    units:          decimal degrees East\n    description:    Geodetic longitude of measuring instrument, in decimal de...\n    axis:           X
xarray.DataArray
'actris_actris|sconcno2_longitude'
  • station_actris_actris|sconcno2: 39
  • month: 1
  • 12.96 5.204 2.582 6.001 4.989 6.945 ... 5.854 4.5 4.924 22.07 14.87
    array([[12.95794 ],\n       [ 5.203611],\n       [ 2.58232 ],\n       [ 6.001019],\n       [ 4.989444],\n       [ 6.944722],\n       [ 8.904722],\n       [ 6.979444],\n       [ 8.46389 ],\n       [12.9277  ],\n       [10.73616 ],\n       [11.516667],\n       [12.085797],\n       [ 8.427486],\n       [-8.92361 ],\n       [ 0.73472 ],\n       [21.377222],\n       [29.401667],\n       [27.55872 ],\n       [24.116111],\n       [-3.204167],\n       [-3.713056],\n       [-0.8075  ],\n       [-3.033056],\n       [-3.205   ],\n       [ 0.179444],\n       [-4.691462],\n       [ 0.292778],\n       [-3.2429  ],\n       [-2.68345 ],\n       [-1.438228],\n       [ 8.633333],\n       [ 6.566667],\n       [ 6.277222],\n       [ 5.853611],\n       [ 4.5     ],\n       [ 4.923611],\n       [22.066667],\n       [14.866667]])
  • standard_name :
    longitude
    long_name :
    longitude
    units :
    decimal degrees East
    description :
    Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.
    axis :
    X
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Save data\u00b6

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Data can be saved as netCDF or Numpy.

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Save to netCDF\u00b6

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In\u00a0[15]:
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provi.save(format='nc')\n
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Data saved to /home/avilanov/software/providentia/saved_data/PRV_20260612_1128.nc\n
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Save to Numpy\u00b6

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In\u00a0[16]:
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provi.save(format='np')\n
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Data saved to /home/avilanov/software/providentia/saved_data/PRV_20260612_1128.npz\n
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Plot\u00b6

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The statistics that can be plotted are: Mean, Median, StdDev, Var, Min, Max, NData, Data%, NStations, Exceedances, p1, p5, p10, p25, p75 , p90, p95, p99, MDA8, MB, NMB, ME, NME, MNB, MNE, MFB, MFE, RMSE, NRMSE, COE, FAC2, IOA, r, r2 and UPA.

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Maps\u00b6

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In\u00a0[17]:
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provi.plot('map-r2', labela='Nitrogen Dioxide Mass Concentration', labelb='CAMS-day1')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Timeseries\u00b6

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In\u00a0[18]:
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provi.plot('timeseries')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Periodic plots\u00b6

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In\u00a0[19]:
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provi.plot('periodic-RMSE')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Periodic violin plots\u00b6

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In\u00a0[20]:
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provi.plot('periodic-violin')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Distribution plots\u00b6

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In\u00a0[21]:
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provi.plot('distribution')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Boxplot\u00b6

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In\u00a0[22]:
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provi.plot('boxplot')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Scatter\u00b6

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In\u00a0[23]:
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provi.plot('scatter')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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In\u00a0[24]:
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provi.plot('scatter', regression=True, hidedata=True)\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Taylor diagram\u00b6

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In\u00a0[25]:
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provi.plot('taylor-r')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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Statistic summary\u00b6

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In\u00a0[26]:
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provi.plot('statsummary')\n
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Warning: Width and/or height have not been passed. The default values will be set.\nResetting data filters to when class was initialised, loading ACTRIS-CAMS subsection filters.\nFiltering data\nResetting data filters to when class was initialised, loading ACTRIS-CAMS subsection filters.\nFiltering data\n
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\n\"No\n
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In\u00a0[27]:
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provi.plot('statsummary', bias=True)\n
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Warning: Width and/or height have not been passed. The default values will be set.\nResetting data filters to when class was initialised, loading ACTRIS-CAMS subsection filters.\nFiltering data\nResetting data filters to when class was initialised, loading ACTRIS-CAMS subsection filters.\nFiltering data\n
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\n\"No\n
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FAIRMODE plots\u00b6

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In\u00a0[28]:
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provi.plot('fairmode-target')\n
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Warning: Width and/or height have not been passed. The default values will be set.\nData for area_classification is not available and will not be shown in the legend\n
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\n\"No\n
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In\u00a0[29]:
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provi.plot('fairmode-statsummary')\n
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Warning: Width and/or height have not been passed. The default values will be set.\n
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\n\"No\n
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\n\n\n"}