Generate CCI cubes

Generate CCI data cubes¶

A DeepESDL example notebook¶

This notebook demonstrates how to access CCI data via two dedicated xcube stores, which provide dynamic data cube views into each gridded data set. More information on the data sets offered can be found in the Open Data Portal of the ESA Climate Change Initiative (CCI).

The xcube data store cciodp provides direct access to the datasets of the Open Data Portal. On the other hand, the xcube data store ccizarr is a subset of the cciodp store and contains CCI data that is already available in cloud-ready format, resulting in better data access performance. The store is regularly updated with new data. Details on the functionality of the two data stores can be found in the xcube documentation.

Please, also refer to the DeepESDL documentation and visit the platform's website for further information!

Brockmann Consult, 2025

---

This notebook runs with the python environment users-deepesdl-xcube-1.9.1, please checkout the documentation for help on changing the environment.

In [1]:

# mandatory imports
import datetime

import matplotlib.pyplot as plt

# Utilities for notebook visualization
import shapely.geometry
from IPython.display import JSON
from xcube.core.store import (
    find_data_store_extensions,
    get_data_store_params_schema,
    new_data_store,
)

# mandatory imports import datetime import matplotlib.pyplot as plt # Utilities for notebook visualization import shapely.geometry from IPython.display import JSON from xcube.core.store import ( find_data_store_extensions, get_data_store_params_schema, new_data_store, )

Configure matplotlib to display graphs inline directly in the notebook and set a sensible default figure size.

In [2]:

%matplotlib inline
plt.rcParams["figure.figsize"] = 16, 8

%matplotlib inline plt.rcParams["figure.figsize"] = 16, 8

1. xcube data store cciodp¶

Check whether the cciodp store is among the available stores, if not please follow the installation information from the top of this notebook.

In [3]:

JSON({e.name: e.metadata for e in find_data_store_extensions()})

JSON({e.name: e.metadata for e in find_data_store_extensions()})

Out[3]:

<IPython.core.display.JSON object>

Usually we need more information to get the actual data store object. Which data store parameters are available for cciodp?

In [4]:

get_data_store_params_schema("cciodp")

get_data_store_params_schema("cciodp")

Out[4]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7f13deb26b30>

Provide mandatory parameters to instantiate the store class:

In [5]:

odp_store = new_data_store("cciodp")

odp_store = new_data_store("cciodp")

Which datasets are provided? (the list may contain both gridded and vector datasets):

In [6]:

# odp_store.list_data_ids()

# odp_store.list_data_ids()

We may ask for a specific dataset ...

In [7]:

odp_store.has_data(
    "esacci.OC.5-days.L3S.CHLOR_A.multi-sensor.multi-platform.MERGED.3-1.geographic"
)

odp_store.has_data( "esacci.OC.5-days.L3S.CHLOR_A.multi-sensor.multi-platform.MERGED.3-1.geographic" )

Out[7]:

False

In [8]:

odp_store.has_data(
    "esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst"
)

odp_store.has_data( "esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst" )

Out[8]:

True

... but in many cases we want to query for certain criteria. How can we do that?

In [9]:

odp_store.get_search_params_schema()

odp_store.get_search_params_schema()

Out[9]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7f13ed8e7690>

Now search, let's search for sea surface temperature data (SST) with a daily frequency:

In [10]:

iterator = odp_store.search_data(
    cci_attrs=dict(ecv="SST", frequency="day", processing_level="L4")
)
JSON([item.to_dict() for item in iterator])

iterator = odp_store.search_data( cci_attrs=dict(ecv="SST", frequency="day", processing_level="L4") ) JSON([item.to_dict() for item in iterator])

/home/conda/users/437be1a4-1747641005-21-deepesdl-xcube-1.9.1/lib/python3.13/site-packages/xcube_cci/cciodp.py:2053: CciOdpWarning: Variable "field_name" has no fill value, cannot set one. For parts where no data is available you will see random values. This is usually the case when data is missing for a time step.
  warnings.warn(f'Variable "{fixed_key}" has no fill value, '

Out[10]:

<IPython.core.display.JSON object>

Which parameters must be passsed or are available to open the dataset?

In [11]:

odp_store.get_open_data_params_schema(
    "esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst"
)

odp_store.get_open_data_params_schema( "esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst" )

Out[11]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7f13de2c5550>

There are no required parameters, so we can decide what parameters we would like to provide them to open a dataset:

In [12]:

odp_dataset = odp_store.open_data(
    "esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst",
    variable_names=["analysed_sst"],
    time_range=["1981-08-31", "2016-12-31"],
)

odp_dataset = odp_store.open_data( "esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst", variable_names=["analysed_sst"], time_range=["1981-08-31", "2016-12-31"], )

Plot one time stamp of the dataset for a analysed_sst in order to take a brief look at the dataset:

In [13]:

odp_dataset

odp_dataset

Out[13]:

<xarray.Dataset> Size: 3TB
Dimensions:       (time: 12907, lat: 3600, lon: 7200, bnds: 2)
Coordinates:
  * lat           (lat) float32 14kB -89.97 -89.93 -89.88 ... 89.88 89.93 89.97
    lat_bnds      (lat, bnds) float32 29kB dask.array<chunksize=(3600, 2), meta=np.ndarray>
  * lon           (lon) float32 29kB -180.0 -179.9 -179.9 ... 179.9 179.9 180.0
    lon_bnds      (lon, bnds) float32 58kB dask.array<chunksize=(7200, 2), meta=np.ndarray>
  * time          (time) datetime64[ns] 103kB 1981-08-31T12:00:00 ... 2016-12...
    time_bnds     (time, bnds) datetime64[ns] 207kB dask.array<chunksize=(12907, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
    analysed_sst  (time, lat, lon) float64 3TB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    title:                   esacci.SST.day.L4.SSTdepth.multi-sensor.multi-pl...
    date_created:            2025-05-22T11:03:25.939346
    processing_level:        L4
    time_coverage_start:     1981-08-31T00:00:00
    time_coverage_end:       2017-01-01T00:00:00
    time_coverage_duration:  P12907DT0H0M0S
    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore...

xarray.Dataset

• Dimensions:
  • time: 12907
  • lat: 3600
  • lon: 7200
  • bnds: 2
• Coordinates: (6)
  • lat
    (lat)
    float32
    -89.97 -89.93 ... 89.93 89.97

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    axis :

    Y

    comment :

    Latitude geographical coordinates,WGS84 projection

    long_name :

    Latitude

    reference_datum :

    geographical coordinates, WGS84 projection

    bounds :

    lat_bnds

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    3600

    shape :

    [3600]

    chunk_sizes :

    3600

    file_chunk_sizes :

    3600

    data_type :

    float32

    dimensions :

    ['lat']

    file_dimensions :

    ['lat']

    array([-89.975, -89.925, -89.875, ...,  89.875,  89.925,  89.975],
          shape=(3600,), dtype=float32)

  • lat_bnds
    (lat, bnds)
    float32
    dask.array<chunksize=(3600, 2), meta=np.ndarray>

    units :

    degrees_north

    long_name :

    Latitude cell boundaries

    valid_min :

    -90.0

    valid_max :

    90.0

    comment :

    Contains the northern and southern boundaries of the grid cells.

    reference_datum :

    geographical coordinates, WGS84 projection

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    7200

    shape :

    [3600, 2]

    chunk_sizes :

    [3600, 2]

    file_chunk_sizes :

    [3600, 2]

    data_type :

    float32

    dimensions :

    ['lat', 'bnds']

    file_dimensions :

    ['lat', 'bnds']

    Array Chunk Bytes 28.12 kiB 28.12 kiB Shape (3600, 2) (3600, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lon
    (lon)
    float32
    -180.0 -179.9 ... 179.9 180.0

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    axis :

    X

    comment :

    Longitude geographical coordinates,WGS84 projection

    long_name :

    Longitude

    reference_datum :

    geographical coordinates, WGS84 projection

    bounds :

    lon_bnds

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    7200

    shape :

    [7200]

    chunk_sizes :

    7200

    file_chunk_sizes :

    7200

    data_type :

    float32

    dimensions :

    ['lon']

    file_dimensions :

    ['lon']

    array([-179.975, -179.925, -179.875, ...,  179.875,  179.925,  179.975],
          shape=(7200,), dtype=float32)

  • lon_bnds
    (lon, bnds)
    float32
    dask.array<chunksize=(7200, 2), meta=np.ndarray>

    valid_max :

    180.0

    comment :

    Contains the eastern and western boundaries of the grid cells.

    reference_datum :

    geographical coordinates, WGS84 projection

    valid_min :

    -180.0

    units :

    degrees_east

    long_name :

    Longitude cell boundaries

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    14400

    shape :

    [7200, 2]

    chunk_sizes :

    [7200, 2]

    file_chunk_sizes :

    [7200, 2]

    data_type :

    float32

    dimensions :

    ['lon', 'bnds']

    file_dimensions :

    ['lon', 'bnds']

    Array Chunk Bytes 56.25 kiB 56.25 kiB Shape (7200, 2) (7200, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time
    (time)
    datetime64[ns]
    1981-08-31T12:00:00 ... 2016-12-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['1981-08-31T12:00:00.000000000', '1981-09-01T12:00:00.000000000',
           '1981-09-02T12:00:00.000000000', ..., '2016-12-29T12:00:00.000000000',
           '2016-12-30T12:00:00.000000000', '2016-12-31T12:00:00.000000000'],
          shape=(12907,), dtype='datetime64[ns]')

  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(12907, 2), meta=np.ndarray>

    standard_name :

    time_bnds

    Array Chunk Bytes 201.67 kiB 201.67 kiB Shape (12907, 2) (12907, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (1)
  • analysed_sst
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>

    long_name :

    analysed sea surface temperature

    standard_name :

    sea_water_temperature

    units :

    kelvin

    valid_min :

    -300

    valid_max :

    4500

    source :

    ATSR<1,2>-ESACCI-L3U-v2.0, AATSR-ESACCI-L3U-v2.0, AVHRR<07,09,11,12,14,15,16,17,18,19>_G-ESACCI-L3U-v2.0, AVHRRMTA_G-ESACCI-L3U-v2.0

    depth :

    20 cm

    orig_data_type :

    int16

    fill_value :

    -32768

    size :

    334549440000

    shape :

    [12907, 3600, 7200]

    chunk_sizes :

    [1, 1200, 2400]

    file_chunk_sizes :

    [1, 1200, 2400]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_dimensions :

    ['time', 'lat', 'lon']

    Array Chunk Bytes 2.43 TiB 21.97 MiB Shape (12907, 3600, 7200) (1, 1200, 2400) Dask graph 116163 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ -89.9749984741211, -89.92500305175781,            -89.875,
           -89.82499694824219,  -89.7750015258789,  -89.7249984741211,
           -89.67500305175781,            -89.625, -89.57499694824219,
            -89.5250015258789,
           ...
             89.5250015258789,  89.57499694824219,             89.625,
            89.67500305175781,   89.7249984741211,   89.7750015258789,
            89.82499694824219,             89.875,  89.92500305175781,
             89.9749984741211],
          dtype='float32', name='lat', length=3600))

  • lon
    PandasIndex

    PandasIndex(Index([-179.97500610351562,  -179.9250030517578,            -179.875,
            -179.8249969482422, -179.77499389648438, -179.72500610351562,
            -179.6750030517578,            -179.625,  -179.5749969482422,
           -179.52499389648438,
           ...
            179.52499389648438,   179.5749969482422,             179.625,
             179.6750030517578,  179.72500610351562,  179.77499389648438,
             179.8249969482422,             179.875,   179.9250030517578,
            179.97500610351562],
          dtype='float32', name='lon', length=7200))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1981-08-31 12:00:00', '1981-09-01 12:00:00',
                   '1981-09-02 12:00:00', '1981-09-03 12:00:00',
                   '1981-09-04 12:00:00', '1981-09-05 12:00:00',
                   '1981-09-06 12:00:00', '1981-09-07 12:00:00',
                   '1981-09-08 12:00:00', '1981-09-09 12:00:00',
                   ...
                   '2016-12-22 12:00:00', '2016-12-23 12:00:00',
                   '2016-12-24 12:00:00', '2016-12-25 12:00:00',
                   '2016-12-26 12:00:00', '2016-12-27 12:00:00',
                   '2016-12-28 12:00:00', '2016-12-29 12:00:00',
                   '2016-12-30 12:00:00', '2016-12-31 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=12907, freq=None))

• Attributes: (8)

  Conventions :

  CF-1.7

  title :

  esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst

  date_created :

  2025-05-22T11:03:25.939346

  processing_level :

  L4

  time_coverage_start :

  1981-08-31T00:00:00

  time_coverage_end :

  2017-01-01T00:00:00

  time_coverage_duration :

  P12907DT0H0M0S

  history :

  [{'program': 'xcube_cci.chunkstore.CciChunkStore', 'cube_params': {'variable_names': ['analysed_sst'], 'time_range': ['1981-08-31T00:00:00', '2016-12-31T00:00:00']}}]

In [14]:

odp_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")

odp_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")

Out[14]:

<matplotlib.image.AxesImage at 0x7f13d5ec8980>

2. xcube data store ccizarr¶

Some of the CCI data is already available in a cloud-ready format, and new data is added on a regular basis. To access this data, select the ccizarr store instead of the cciodp store.

In [15]:

zarr_store = new_data_store("ccizarr")

zarr_store = new_data_store("ccizarr")

In [16]:

zarr_store.list_data_ids()

zarr_store.list_data_ids()

Out[16]:

['ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2018-fv2.0.zarr',
 'ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2020-fv4.0.zarr',
 'ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr',
 'ESACCI-GHG-L2-CO2-OCO-2-FOCAL-2014-2021-v10.zarr',
 'ESACCI-GHG-L2-CO2-SCIAMACHY-WFMD-2002-2012-fv1.zarr',
 'ESACCI-ICESHEETS_Antarctica_GMB-2002-2016-v1.1.zarr',
 'ESACCI-ICESHEETS_Greenland_GMB-2003-2016-v1.1.zarr',
 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-AVHRR_NOAA-1982-2016-fv3.0.zarr',
 'ESACCI-L3C_SNOW-SWE-1979-2018-fv1.0.zarr',
 'ESACCI-L3C_SNOW-SWE-1979-2020-fv2.0.zarr',
 'ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr',
 'ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-2015-v2.0.7b.zarr',
 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-2002-2018-fv3.00.zarr',
 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_NIGHT-2002-2018-fv3.00.zarr',
 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1DAILY_DAY-1995-2020-fv3.00.zarr',
 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1DAILY_NIGHT-1995-2020-fv3.00.zarr',
 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-1995-2020-fv3.00.zarr',
 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_NIGHT-1995-2020-fv3.00.zarr',
 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr',
 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-IOP-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-IOP-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-IOP-MERGED-5D_DAILY_4km_GEO_PML_OCx_QAA-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-IOP-MERGED-8D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-5D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-8D_DAILY_4km_GEO_PML_OCx_QAA-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-RRS-MERGED-1M_MONTHLY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-RRS-MERGED-1Y_YEARLY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-RRS-MERGED-1D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-RRS-MERGED-5D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',
 'ESACCI-OC-L3S-RRS-MERGED-8D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',
 'ESACCI-PERMAFROST-L4-ALT-MODISLST-AREA4_PP-1997-2018-fv02.0.zarr',
 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-NH25KMEASE2-2002-2012-fv2.0.zarr',
 'ESACCI-SEAICE-L3C-SITHICK-SIRAL_CRYOSAT2-NH25KMEASE2-2010-2017-fv2.0.zarr',
 'ESACCI-SEAICE-L4-SICONC-AMSR_50.0kmEASE2-NH-2002-2017-fv2.1.zarr',
 'ESACCI-SEALEVEL-IND-MSLTR-MERGED-1993-2016-fv02.zarr',
 'ESACCI-SEALEVEL-L4-MSLA-MERGED-1993-2015-fv02.zarr',
 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2020-fv05.3.zarr',
 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2021-fv07.1.zarr']

The data store ccizarr has no dedicated data store parameters. The dataset must be opened as a whole, temporal and spatial subsets can follow afterwards.

In [17]:

dataset = zarr_store.open_data(
    "ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr"
)

dataset = zarr_store.open_data( "ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr" )

In [18]:

dataset

dataset

Out[18]:

<xarray.Dataset> Size: 2TB
Dimensions:           (time: 280, lat: 4320, lon: 8640)
Coordinates:
  * lat               (lat) float64 35kB 89.98 89.94 89.9 ... -89.94 -89.98
  * lon               (lon) float64 69kB -180.0 -179.9 -179.9 ... 179.9 180.0
  * time              (time) datetime64[ns] 2kB 1997-09-04 ... 2020-12-01
Data variables: (12/55)
    MERIS_nobs_sum    (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    MODISA_nobs_sum   (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    OLCI_nobs_sum     (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    SeaWiFS_nobs_sum  (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    VIIRS_nobs_sum    (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    adg_412           (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    ...                ...
    bbp_490           (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    bbp_510           (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    bbp_560           (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    bbp_665           (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
    crs               (time) int32 1kB dask.array<chunksize=(16,), meta=np.ndarray>
    total_nobs_sum    (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
Attributes: (12/51)
    Conventions:                       CF-1.7
    Metadata_Conventions:              Unidata Dataset Discovery v1.0
    catalogue_url:                     https://catalogue.ceda.ac.uk/uuid/88c2...
    cdm_data_type:                     Grid
    comment:                           See summary attribute
    creation_date:                     Tue Feb  2 10:58:12 2021
    ...                                ...
    time_coverage_duration:            P1M
    time_coverage_end:                 19700101T000000Z
    time_coverage_resolution:          P1M
    time_coverage_start:               19700101T000000Z
    title:                             ESA CCI Ocean Colour Product
    tracking_id:                       95e99081-7011-4044-8f2b-d7db84276a4e

xarray.Dataset

• Dimensions:
  • time: 280
  • lat: 4320
  • lon: 8640
• Coordinates: (3)
  • lat
    (lat)
    float64
    89.98 89.94 89.9 ... -89.94 -89.98

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    89.97916666666667

    valid_min :

    -89.97916666666666

    array([ 89.979167,  89.9375  ,  89.895833, ..., -89.895833, -89.9375  ,
           -89.979167], shape=(4320,))

  • lon
    (lon)
    float64
    -180.0 -179.9 ... 179.9 180.0

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    179.97916666666663

    valid_min :

    -179.97916666666666

    array([-179.979167, -179.9375  , -179.895833, ...,  179.895833,  179.9375  ,
            179.979167], shape=(8640,))

  • time
    (time)
    datetime64[ns]
    1997-09-04 ... 2020-12-01

    axis :

    T

    standard_name :

    time

    array(['1997-09-04T00:00:00.000000000', '1997-10-01T00:00:00.000000000',
           '1997-11-01T00:00:00.000000000', ..., '2020-10-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000'],
          shape=(280,), dtype='datetime64[ns]')

• Data variables: (55)
  • MERIS_nobs_sum
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    long_name :

    Count of the number of observations from the MERIS sensor contributing to this bin cell

    number_of_files_composited :

    19

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • MODISA_nobs_sum
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    long_name :

    Count of the number of observations from the MODIS (Aqua) sensor contributing to this bin cell

    number_of_files_composited :

    19

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • OLCI_nobs_sum
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    long_name :

    Count of the number of observations from the OLCI sensor contributing to this bin cell

    number_of_files_composited :

    19

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SeaWiFS_nobs_sum
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    long_name :

    Count of the number of observations from the SeaWiFS (GAC and LAC) sensor contributing to this bin cell

    number_of_files_composited :

    19

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VIIRS_nobs_sum
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    long_name :

    Count of the number of observations from the VIIRS sensor contributing to this bin cell

    number_of_files_composited :

    19

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_412
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    adg_412_rmsd adg_412_bias

    grid_mapping :

    crs

    long_name :

    Absorption coefficient for dissolved and detrital material at 412 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_412_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of absorption coefficient for dissolved and detrital material at 412 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_412_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of absorption coefficient for dissolved and detrital material at 412 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_443
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    adg_443_rmsd adg_443_bias

    grid_mapping :

    crs

    long_name :

    Absorption coefficient for dissolved and detrital material at 443 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_443_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of absorption coefficient for dissolved and detrital material at 443 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_443_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of absorption coefficient for dissolved and detrital material at 443 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_490
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    adg_490_rmsd adg_490_bias

    grid_mapping :

    crs

    long_name :

    Absorption coefficient for dissolved and detrital material at 490 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_490_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of absorption coefficient for dissolved and detrital material at 490 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_490_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of absorption coefficient for dissolved and detrital material at 490 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_510
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    adg_510_rmsd adg_510_bias

    grid_mapping :

    crs

    long_name :

    Absorption coefficient for dissolved and detrital material at 510 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_510_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of absorption coefficient for dissolved and detrital material at 510 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_510_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of absorption coefficient for dissolved and detrital material at 510 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_560
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    adg_560_rmsd adg_560_bias

    grid_mapping :

    crs

    long_name :

    Absorption coefficient for dissolved and detrital material at 560 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_560_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of absorption coefficient for dissolved and detrital material at 560 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_560_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of absorption coefficient for dissolved and detrital material at 560 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_665
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    adg_665_rmsd adg_665_bias

    grid_mapping :

    crs

    long_name :

    Absorption coefficient for dissolved and detrital material at 665 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_665_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of absorption coefficient for dissolved and detrital material at 665 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • adg_665_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/adg/cci_iop_adg_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of absorption coefficient for dissolved and detrital material at 665 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_412
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    aph_412_rmsd aph_412_bias

    grid_mapping :

    crs

    long_name :

    Phytoplankton absorption coefficient at 412 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_412_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of phytoplankton absorption coefficient at 412 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_412_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of phytoplankton absorption coefficient at 412 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_443
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    aph_443_rmsd aph_443_bias

    grid_mapping :

    crs

    long_name :

    Phytoplankton absorption coefficient at 443 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_443_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of phytoplankton absorption coefficient at 443 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_443_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of phytoplankton absorption coefficient at 443 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_490
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    aph_490_rmsd aph_490_bias

    grid_mapping :

    crs

    long_name :

    Phytoplankton absorption coefficient at 490 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_490_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of phytoplankton absorption coefficient at 490 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_490_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of phytoplankton absorption coefficient at 490 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_510
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    aph_510_rmsd aph_510_bias

    grid_mapping :

    crs

    long_name :

    Phytoplankton absorption coefficient at 510 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_510_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of phytoplankton absorption coefficient at 510 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_510_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of phytoplankton absorption coefficient at 510 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_560
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    aph_560_rmsd aph_560_bias

    grid_mapping :

    crs

    long_name :

    Phytoplankton absorption coefficient at 560 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_560_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of phytoplankton absorption coefficient at 560 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_560_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of phytoplankton absorption coefficient at 560 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_665
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    ancillary_variables :

    aph_665_rmsd aph_665_bias

    grid_mapping :

    crs

    long_name :

    Phytoplankton absorption coefficient at 665 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0063

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_665_bias
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_bias.dat

    grid_mapping :

    crs

    long_name :

    Bias of phytoplankton absorption coefficient at 665 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aph_665_rmsd
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    comment :

    Uncertainty lookups derived from file: /data/datasets/CCI/v5.0-production/stage09b-uncertainty_tables/aph/cci_iop_aph_rmsd.dat

    grid_mapping :

    crs

    long_name :

    Root-mean-square-difference of phytoplankton absorption coefficient at 665 nm.

    ref :

    http://www.uncertweb.org

    rel :

    uncertainty

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • atot_412
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total absorption coefficient at 412 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0062

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • atot_443
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total absorption coefficient at 443 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0062

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • atot_490
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total absorption coefficient at 490 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0062

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • atot_510
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total absorption coefficient at 510 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0062

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • atot_560
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total absorption coefficient at 560 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0062

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • atot_665
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Total absorption coefficient at 665 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P071/19/CFSN0062

    standard_name :

    volume_absorption_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bbp_412
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Particulate backscattering coefficient for dissolved and detrital material at 412 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.nerc.ac.uk/collection/P04/current/

    standard_name :

    volume_backwards_scattering_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    412

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bbp_443
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Particulate backscattering coefficient for dissolved and detrital material at 443 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.nerc.ac.uk/collection/P04/current/

    standard_name :

    volume_backwards_scattering_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    443

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bbp_490
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Particulate backscattering coefficient for dissolved and detrital material at 490 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.nerc.ac.uk/collection/P04/current/

    standard_name :

    volume_backwards_scattering_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    490

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bbp_510
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Particulate backscattering coefficient for dissolved and detrital material at 510 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.nerc.ac.uk/collection/P04/current/

    standard_name :

    volume_backwards_scattering_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    510

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bbp_560
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Particulate backscattering coefficient for dissolved and detrital material at 560 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.nerc.ac.uk/collection/P04/current/

    standard_name :

    volume_backwards_scattering_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    560

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bbp_665
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Particulate backscattering coefficient for dissolved and detrital material at 665 nm as derived using the QAA model, generated by SeaDAS for MERIS

    parameter_vocab_uri :

    http://vocab.nerc.ac.uk/collection/P04/current/

    standard_name :

    volume_backwards_scattering_coefficient_of_radiative_flux_in_sea_water

    units :

    m-1

    units_nonstandard :

    m^-1

    wavelength :

    665

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

  • crs
    (time)
    int32
    dask.array<chunksize=(16,), meta=np.ndarray>

    grid_mapping_name :

    latitude_longitude

    Array Chunk Bytes 1.09 kiB 64 B Shape (280,) (16,) Dask graph 18 chunks in 2 graph layers Data type int32 numpy.ndarray

  • total_nobs_sum
    (time, lat, lon)
    float32
    dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>

    long_name :

    Count of the total number of observations contributing to this bin cell

    number_of_files_composited :

    19

    Array Chunk Bytes 38.93 GiB 71.19 MiB Shape (280, 4320, 8640) (16, 1080, 1080) Dask graph 576 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.97916666666667,            89.9375,  89.89583333333333,
            89.85416666666667,            89.8125,  89.77083333333333,
            89.72916666666667,            89.6875,  89.64583333333333,
            89.60416666666667,
           ...
           -89.60416666666666, -89.64583333333331,           -89.6875,
           -89.72916666666666, -89.77083333333331,           -89.8125,
           -89.85416666666666, -89.89583333333331,           -89.9375,
           -89.97916666666666],
          dtype='float64', name='lat', length=4320))

  • lon
    PandasIndex

    PandasIndex(Index([-179.97916666666666,           -179.9375, -179.89583333333334,
           -179.85416666666666,           -179.8125, -179.77083333333334,
           -179.72916666666666,           -179.6875, -179.64583333333334,
           -179.60416666666666,
           ...
            179.60416666666663,  179.64583333333331,            179.6875,
            179.72916666666663,  179.77083333333331,            179.8125,
            179.85416666666663,  179.89583333333331,            179.9375,
            179.97916666666663],
          dtype='float64', name='lon', length=8640))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1997-09-04', '1997-10-01', '1997-11-01', '1997-12-01',
                   '1998-01-01', '1998-02-01', '1998-03-01', '1998-04-01',
                   '1998-05-01', '1998-06-01',
                   ...
                   '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01',
                   '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01',
                   '2020-11-01', '2020-12-01'],
                  dtype='datetime64[ns]', name='time', length=280, freq=None))

• Attributes: (51)

  Conventions :

  CF-1.7

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  catalogue_url :

  https://catalogue.ceda.ac.uk/uuid/88c2bc7af4f0402d8ceecad611c58cc5

  cdm_data_type :

  Grid

  comment :

  See summary attribute

  creation_date :

  Tue Feb 2 10:58:12 2021

  creator_email :

  help@esa-oceancolour-cci.org

  creator_name :

  Plymouth Marine Laboratory

  creator_url :

  http://esa-oceancolour-cci.org

  date_created :

  Tue Feb 2 10:58:12 2021

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  .04166666666666666666

  geospatial_lat_units :

  decimal degrees north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  .04166666666666666666

  geospatial_lon_units :

  decimal degrees east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  git_commit_hash :

  33d54b27cf9a6b4d9d9f8113854f910c95bc287d

  history :

  Source data were: ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201201-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201202-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201203-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201204-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201205-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201206-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201207-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201208-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201209-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201210-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201211-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201212-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201213-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201214-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201215-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201216-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201217-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201218-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201219-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201220-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201221-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201222-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201223-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201224-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201225-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201226-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201227-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201228-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201229-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201230-fv5.0.nc, ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-20201231-fv5.0.nc; netcdf_compositor_cci composites Rrs_412, Rrs_443, Rrs_490, Rrs_510, Rrs_560, Rrs_665, water_class1, water_class2, water_class3, water_class4, water_class5, water_class6, water_class7, water_class8, water_class9, water_class10, water_class11, water_class12, water_class13, water_class14, atot_412, atot_443, atot_490, atot_510, atot_560, atot_665, aph_412, aph_443, aph_490, aph_510, aph_560, aph_665, adg_412, adg_443, adg_490, adg_510, adg_560, adg_665, bbp_412, bbp_443, bbp_490, bbp_510, bbp_560, bbp_665, chlor_a, kd_490, Rrs_412_bias, Rrs_443_bias, Rrs_490_bias, Rrs_510_bias, Rrs_560_bias, Rrs_665_bias, chlor_a_log10_bias, aph_412_bias, aph_443_bias, aph_490_bias, aph_510_bias, aph_560_bias, aph_665_bias, adg_412_bias, adg_443_bias, adg_490_bias, adg_510_bias, adg_560_bias, adg_665_bias, kd_490_bias with --mean, Rrs_412_rmsd, Rrs_443_rmsd, Rrs_490_rmsd, Rrs_510_rmsd, Rrs_560_rmsd, Rrs_665_rmsd, chlor_a_log10_rmsd, aph_412_rmsd, aph_443_rmsd, aph_490_rmsd, aph_510_rmsd, aph_560_rmsd, aph_665_rmsd, adg_412_rmsd, adg_443_rmsd, adg_490_rmsd, adg_510_rmsd, adg_560_rmsd, adg_665_rmsd, kd_490_rmsd with --root-mean-square, and MODISA_nobs, VIIRS_nobs, OLCI_nobs, MERIS_nobs, SeaWiFS_nobs, total_nobs - with --total 1612270525 Subsetted from standardised_geo/ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-202012-fv5.0.nc to only include variables MERIS_nobs_sum,MODISA_nobs_sum,OLCI_nobs_sum,SeaWiFS_nobs_sum,VIIRS_nobs_sum,adg_412,adg_412_bias,adg_412_rmsd,adg_443,adg_443_bias,adg_443_rmsd,adg_490,adg_490_bias,adg_490_rmsd,adg_510,adg_510_bias,adg_510_rmsd,adg_560,adg_560_bias,adg_560_rmsd,adg_665,adg_665_bias,adg_665_rmsd,aph_412,aph_412_bias,aph_412_rmsd,aph_443,aph_443_bias,aph_443_rmsd,aph_490,aph_490_bias,aph_490_rmsd,aph_510,aph_510_bias,aph_510_rmsd,aph_560,aph_560_bias,aph_560_rmsd,aph_665,aph_665_bias,aph_665_rmsd,atot_412,atot_443,atot_490,atot_510,atot_560,atot_665,bbp_412,bbp_443,bbp_490,bbp_510,bbp_560,bbp_665,crs,lat,lon,time,total_nobs_sum

  id :

  ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-202012-fv5.0.nc

  institution :

  Plymouth Marine Laboratory

  keywords :

  satellite,observation,ocean,ocean colour

  keywords_vocabulary :

  none

  license :

  ESA CCI Data Policy: free and open access. When referencing, please use: Ocean Colour Climate Change Initiative dataset, Version <Version Number>, European Space Agency, available online at http://www.esa-oceancolour-cci.org. We would also appreciate being notified of publications so that we can list them on the project website at http://www.esa-oceancolour-cci.org/?q=publications

  naming_authority :

  uk.ac.pml

  netcdf_file_type :

  NETCDF4_CLASSIC

  number_of_bands_used_to_classify :

  4

  number_of_files_composited :

  31

  number_of_optical_water_types :

  14

  platform :

  Orbview-2,Aqua,Envisat,Suomi-NPP, Sentinel-3a

  processing_level :

  Level-3

  product_version :

  5.0

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-oceancolour-cci.org/

  sensor :

  SeaWiFS,MODIS,MERIS,VIIRS,OLCI

  sensors_present :

  MODISA OLCIa VIIRSN

  source :

  NASA SeaWiFS L1A and L2 R2018.0 LAC and GAC, MODIS-Aqua L1A and L2 R2018.0, MERIS L1B 3rd reprocessing inc OCL corrections, NASA VIIRS L1A and L2 R2018.0, OLCI L1B

  spatial_resolution :

  4km nominal at equator

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Conventions Version 1.7

  start_date :

  01-DEC-2020 00:00:00.000000

  stop_date :

  31-DEC-2020 23:59:00.000000

  summary :

  Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are monthly composites of merged sensor (MERIS, MODIS Aqua, SeaWiFS LAC & GAC, VIIRS, OLCI) products. MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30. VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer. The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP. Derived products were generally computed with the standard algorithmsthrough SeaDAS. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

  time_coverage_duration :

  P1M

  time_coverage_end :

  19700101T000000Z

  time_coverage_resolution :

  P1M

  time_coverage_start :

  19700101T000000Z

  title :

  ESA CCI Ocean Colour Product

  tracking_id :

  95e99081-7011-4044-8f2b-d7db84276a4e

Get an overview of the parameter scheme of the datasets in the zarr_store and create subsets accordingly

In [19]:

zarr_store.get_open_data_params_schema(
    "ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr"
)

zarr_store.get_open_data_params_schema( "ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr" )

Out[19]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7f13cc35e7b0>

In [20]:

def open_zarrstore(filename, time_range, variables):
    ds = zarr_store.open_data(filename)
    subset = ds.sel(time=slice(time_range[0], time_range[1]))
    subset = subset[variables]

    return subset


zarr_dataset = open_zarrstore(
    "ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr",
    time_range=[datetime.datetime(1982, 1, 1), datetime.datetime(2016, 12, 31)],
    variables=["analysed_sst"],
)

def open_zarrstore(filename, time_range, variables): ds = zarr_store.open_data(filename) subset = ds.sel(time=slice(time_range[0], time_range[1])) subset = subset[variables] return subset zarr_dataset = open_zarrstore( "ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr", time_range=[datetime.datetime(1982, 1, 1), datetime.datetime(2016, 12, 31)], variables=["analysed_sst"], )

In [21]:

zarr_dataset

zarr_dataset

Out[21]:

<xarray.Dataset> Size: 106GB
Dimensions:       (time: 12783, lat: 720, lon: 1440)
Coordinates:
  * lat           (lat) float32 3kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88
  * lon           (lon) float32 6kB -179.9 -179.6 -179.4 ... 179.4 179.6 179.9
  * time          (time) datetime64[ns] 102kB 1982-01-01T12:00:00 ... 2016-12...
Data variables:
    analysed_sst  (time, lat, lon) float64 106GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
Attributes: (12/47)
    Conventions:                CF-1.4
    acknowledgment:             Funded by ESA
    cdm_data_type:              grid
    comment:                    
    creator_email:              science.leader@esa-sst-cci.org
    creator_name:               SST_cci
    ...                         ...
    summary:                    An ensemble product with input from a number ...
    time_coverage_end:          20170101T000000Z
    time_coverage_start:        20161231T000000Z
    title:                      Global SST Ensemble, L4 GMPE
    uuid:                       dc0c5b25-93bf-4943-aba1-7f0de9109620
    westernmost_longitude:      -180.0

xarray.Dataset

• Dimensions:
  • time: 12783
  • lat: 720
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float32
    -89.88 -89.62 ... 89.62 89.88

    axis :

    Y

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],
          shape=(720,), dtype=float32)

  • lon
    (lon)
    float32
    -179.9 -179.6 ... 179.6 179.9

    axis :

    X

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],
          shape=(1440,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    1982-01-01T12:00:00 ... 2016-12-...

    axis :

    T

    long_name :

    reference time of sst field

    standard_name :

    time

    array(['1982-01-01T12:00:00.000000000', '1982-01-02T12:00:00.000000000',
           '1982-01-03T12:00:00.000000000', ..., '2016-12-28T12:00:00.000000000',
           '2016-12-29T12:00:00.000000000', '2016-12-30T12:00:00.000000000'],
          shape=(12783,), dtype='datetime64[ns]')

• Data variables: (1)
  • analysed_sst
    (time, lat, lon)
    float64
    dask.array<chunksize=(16, 720, 720), meta=np.ndarray>

    long_name :

    median SST from GMPE

    source :

    SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST

    standard_name :

    sea_surface_foundation_temperature

    units :

    kelvin

    valid_max :

    4500

    valid_min :

    -300

    Array Chunk Bytes 98.75 GiB 63.28 MiB Shape (12783, 720, 1440) (16, 720, 720) Dask graph 1598 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
           -87.875, -87.625,
           ...
            87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
            89.625,  89.875],
          dtype='float32', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float32', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1982-01-01 12:00:00', '1982-01-02 12:00:00',
                   '1982-01-03 12:00:00', '1982-01-04 12:00:00',
                   '1982-01-05 12:00:00', '1982-01-06 12:00:00',
                   '1982-01-07 12:00:00', '1982-01-08 12:00:00',
                   '1982-01-09 12:00:00', '1982-01-10 12:00:00',
                   ...
                   '2016-12-21 12:00:00', '2016-12-22 12:00:00',
                   '2016-12-23 12:00:00', '2016-12-24 12:00:00',
                   '2016-12-25 12:00:00', '2016-12-26 12:00:00',
                   '2016-12-27 12:00:00', '2016-12-28 12:00:00',
                   '2016-12-29 12:00:00', '2016-12-30 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=12783, freq=None))

• Attributes: (47)

  Conventions :

  CF-1.4

  acknowledgment :

  Funded by ESA

  cdm_data_type :

  grid

  comment :

  creator_email :

  science.leader@esa-sst-cci.org

  creator_name :

  SST_cci

  creator_url :

  http://www.esa-sst-cci.org

  date_created :

  20180711T172223Z

  easternmost_longitude :

  180.0

  file_quality_level :

  3

  gds_version_id :

  2.0

  geospatial_lat_resolution :

  0.25 degree

  geospatial_lat_units :

  degrees north

  geospatial_lon_resolution :

  0.25 degree

  geospatial_lon_units :

  degrees east

  history :

  NULL

  id :

  UKMO-L4LRens-GLOB-GMPE

  institution :

  UKMO

  keywords :

  Oceans > Ocean Temperature > Sea Surface Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Creative Commons Licence by attribution (https://creativecommons.org/licenses/by/4.0/

  metadata_conventions :

  Unidata Observation Dataset v1.0

  metadata_link :

  not available

  naming_authority :

  org.ghrsst

  netcdf_version_id :

  3.6

  northernmost_latitude :

  90.0

  platform :

  processing_level :

  GMPE

  product_version :

  3.0

  project :

  Climate Change Initiative - European Space Agency

  publisher_email :

  science.leader@esa-sst-cci.org

  publisher_name :

  ESACCI

  publisher_url :

  http://www.esa-sst-cci.org

  references :

  Martin et al., Deep Sea Research II, 2011

  sensor :

  source :

  SST CCI V2, SST CCI V1.1, MyOcean, AVHRR_OI, CMC, HadISST.2.2.0.0_r0, HadISST.2.2.0.0_r1, HadISST.2.2.0.0_r2, HadISST.2.2.0.0_r3, HadISST.2.2.0.0_r4, HadISST.2.2.0.0_r5, HadISST.2.2.0.0_r6, HadISST.2.2.0.0_r7, HadISST.2.2.0.0_r8, HadISST.2.2.0.0_r9, MGDSST

  southernmost_latitude :

  -90.0

  spatial_resolution :

  0.25 degree

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  start_time :

  20161231T000000Z

  stop_time :

  20170101T000000Z

  summary :

  An ensemble product with input from a number of L4 SST analyses

  time_coverage_end :

  20170101T000000Z

  time_coverage_start :

  20161231T000000Z

  title :

  Global SST Ensemble, L4 GMPE

  uuid :

  dc0c5b25-93bf-4943-aba1-7f0de9109620

  westernmost_longitude :

  -180.0

In [22]:

zarr_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")

zarr_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")

Out[22]:

<matplotlib.image.AxesImage at 0x7f13cc1ed590>

In [ ]: