Save cube to team storage

Save a datacube to your team storage¶

A DeepESDL example notebook¶

This notebook demonstrates how save a generated datacube to your team storage. In the example the CCI data store is used, for details concerning the CCI store are given in example notebook 03_Generate_CCI_cubes.ipynb

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

Brockmann Consult, 2024

---

This notebook runs with the python environment deepesdl-xcube-1.7.0, please checkout the documentation for help on changing the environment.

In [1]:

# mandatory imports
import datetime
import os

import matplotlib.pyplot as plt
import shapely.geometry
from xcube.core.store import new_data_store

# mandatory imports import datetime import os import matplotlib.pyplot as plt import shapely.geometry from xcube.core.store import 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

Provide mandatory parameters to instantiate the store class:

In [3]:

store = new_data_store("ccizarr")

store = new_data_store("ccizarr")

Let's open an example dataset:

In [4]:

def open_zarrstore(filename, time_range, variables, bbox):
    min_lat, min_lon, max_lat, max_lon = bbox
    ds = store.open_data(filename)

    subset = ds.sel(
        lat=slice(min_lat, max_lat),
        lon=slice(min_lon, max_lon),
        time=slice(time_range[0], time_range[1]),
    )
    subset = subset[variables]

    return subset


dataset = open_zarrstore(
    "ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-2002-2018-fv3.00.zarr",
    time_range=[datetime.datetime(2016, 10, 1), datetime.datetime(2016, 10, 2)],
    variables=["lst"],
    bbox=[35, -10, 70, 30],
)

def open_zarrstore(filename, time_range, variables, bbox): min_lat, min_lon, max_lat, max_lon = bbox ds = store.open_data(filename) subset = ds.sel( lat=slice(min_lat, max_lat), lon=slice(min_lon, max_lon), time=slice(time_range[0], time_range[1]), ) subset = subset[variables] return subset dataset = open_zarrstore( "ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-2002-2018-fv3.00.zarr", time_range=[datetime.datetime(2016, 10, 1), datetime.datetime(2016, 10, 2)], variables=["lst"], bbox=[35, -10, 70, 30], )

In [5]:

dataset

dataset

Out[5]:

<xarray.Dataset> Size: 224MB
Dimensions:  (time: 2, lat: 3500, lon: 4000)
Coordinates:
  * lat      (lat) float32 14kB 35.01 35.01 35.02 35.03 ... 69.97 69.98 69.99
  * lon      (lon) float32 16kB -9.995 -9.985 -9.975 ... 29.98 29.98 29.99
  * time     (time) datetime64[ns] 16B 2016-10-01 2016-10-02
Data variables:
    lst      (time, lat, lon) float64 224MB dask.array<chunksize=(1, 1500, 1000), meta=np.ndarray>
Attributes: (12/42)
    Conventions:                CF-1.8
    catalogue_url:              https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d...
    cdm_data_type:              grid
    comment:                    These data were produced as part of the ESA L...
    creator_email:              djg20@le.ac.uk
    creator_name:               University of Leicester Surface Temperature G...
    ...                         ...
    summary:                    This file contains level L3C global land surf...
    time_coverage_duration:     PT04M59S
    time_coverage_end:          19700101T000001Z
    time_coverage_resolution:   P1D
    time_coverage_start:        19700101T000001Z
    title:                      ESA LST CCI land surface temperature data at ...

xarray.Dataset

• Dimensions:
  • time: 2
  • lat: 3500
  • lon: 4000
• Coordinates: (3)
  • lat
    (lat)
    float32
    35.01 35.01 35.02 ... 69.98 69.99

    long_name :

    latitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([35.005   , 35.014996, 35.024998, ..., 69.975   , 69.98499 , 69.99499 ],
          dtype=float32)

  • lon
    (lon)
    float32
    -9.995 -9.985 ... 29.98 29.99

    long_name :

    longitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-9.995   , -9.985005, -9.975011, ..., 29.975   , 29.984995, 29.99499 ],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2016-10-01 2016-10-02

    long_name :

    reference time of file

    standard_name :

    time

    array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • lst
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 1500, 1000), meta=np.ndarray>

    long_name :

    land surface temperature

    units :

    kelvin

    valid_max :

    7685

    valid_min :

    -8315

    Array Chunk Bytes 213.62 MiB 30.52 MiB Shape (2, 3500, 4000) (1, 2000, 2000) Dask graph 12 chunks in 3 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([35.005001068115234,  35.01499557495117,  35.02499771118164,
            35.03499984741211,  35.04500198364258, 35.054996490478516,
           35.064998626708984,  35.07500076293945,  35.08499526977539,
            35.09499740600586,
           ...
            69.90499114990234,  69.91500091552734,  69.92499542236328,
            69.93498992919922,  69.94499969482422,  69.95499420166016,
             69.9649887084961,   69.9749984741211,  69.98499298095703,
            69.99498748779297],
          dtype='float32', name='lat', length=3500))

  • lon
    PandasIndex

    PandasIndex(Index([-9.994999885559082, -9.985005378723145, -9.975010871887207,
           -9.965001106262207,  -9.95500659942627,  -9.94499683380127,
           -9.935002326965332, -9.925007820129395, -9.914998054504395,
           -9.905003547668457,
           ...
           29.904993057250977, 29.914987564086914, 29.924997329711914,
            29.93499183654785,  29.94500160217285,  29.95499610900879,
           29.964990615844727, 29.975000381469727, 29.984994888305664,
             29.9949893951416],
          dtype='float32', name='lon', length=4000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-10-01', '2016-10-02'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (42)

  Conventions :

  CF-1.8

  catalogue_url :

  https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d247bcb3da6aed42f4b59a

  cdm_data_type :

  grid

  comment :

  These data were produced as part of the ESA LST CCI project.

  creator_email :

  djg20@le.ac.uk

  creator_name :

  University of Leicester Surface Temperature Group

  creator_url :

  https://climate.esa.int/en/projects/land-surface-temperature

  date_created :

  20211204T115246Z

  doi :

  10.5285/6babb8d9a8d247bcb3da6aed42f4b59a

  format_version :

  CCI Data Standards v2.2

  geospatial_lat_max :

  -68.13521575927734

  geospatial_lat_min :

  -89.99625396728516

  geospatial_lat_resolution :

  0.009999999776482582

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  179.9976348876953

  geospatial_lon_min :

  -179.99952697753906

  geospatial_lon_resolution :

  0.009999999776482582

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  history :

  Created using software developed at University of Leicester

  id :

  ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-20181231000000-fv3.00.nc

  institution :

  University of Leicester

  key_variables :

  land_surface_temperature

  keywords :

  Earth Science; Land Surface; Land Temperature; Land Surface Temperature

  keywords_vocabulary :

  NASA Global change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  le.ac.uk

  platform :

  Aqua

  product_version :

  3.00

  project :

  Climate Change Initiative - European Space Agency

  references :

  https://climate.esa.int/en/projects/land-surface-temperature

  sensor :

  MODIS

  source :

  ESA LST CCI MODISA L3U V3.00

  spatial_resolution :

  0.01 degree

  standard_name_vocabulary :

  CF Standard Name Table v71

  summary :

  This file contains level L3C global land surface temperatures from MODIS. L3C data are derived from multiple orbits of single sensor L3U data combined onto a space and/or time grid.

  time_coverage_duration :

  PT04M59S

  time_coverage_end :

  19700101T000001Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  19700101T000001Z

  title :

  ESA LST CCI land surface temperature data at product level L3C from Moderate Resolution Imaging Spectroradiometer.

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

In [6]:

dataset.lst.isel(time=0).plot.imshow(cmap="plasma")

dataset.lst.isel(time=0).plot.imshow(cmap="plasma")

Out[6]:

<matplotlib.image.AxesImage at 0x7f7ebf4a8950>

To store the cube in your teams user space, please first retrieve the details from your environment variables as the following:

In [7]:

S3_USER_STORAGE_KEY = os.environ["S3_USER_STORAGE_KEY"]
S3_USER_STORAGE_SECRET = os.environ["S3_USER_STORAGE_SECRET"]
S3_USER_STORAGE_BUCKET = os.environ["S3_USER_STORAGE_BUCKET"]

S3_USER_STORAGE_KEY = os.environ["S3_USER_STORAGE_KEY"] S3_USER_STORAGE_SECRET = os.environ["S3_USER_STORAGE_SECRET"] S3_USER_STORAGE_BUCKET = os.environ["S3_USER_STORAGE_BUCKET"]

You need to instantiate a s3 datastore pointing to the team bucket:

In [8]:

team_store = new_data_store(
    "s3",
    root=S3_USER_STORAGE_BUCKET,
    storage_options=dict(
        anon=False, key=S3_USER_STORAGE_KEY, secret=S3_USER_STORAGE_SECRET
    ),
)

team_store = new_data_store( "s3", root=S3_USER_STORAGE_BUCKET, storage_options=dict( anon=False, key=S3_USER_STORAGE_KEY, secret=S3_USER_STORAGE_SECRET ), )

If you have stored no data to your user space, the returned list will be empty:

In [9]:

list(team_store.get_data_ids())

list(team_store.get_data_ids())

Out[9]:

['SST.levels',
 'amazonas_v8.zarr',
 'amazonas_v9.zarr',
 'noise_trajectory.zarr',
 'reanalysis-era5-single-levels-monthly-means-subset-2001-2010_TMH.zarr']

Save in levels format to generate data pyramid for efficient visualisation

In [10]:

team_store.write_data(
    dataset, "analysed_lst.levels", replace=True, use_saved_levels=True
)

team_store.write_data( dataset, "analysed_lst.levels", replace=True, use_saved_levels=True )

Out[10]:

'analysed_lst.levels'

If you list the content of you datastore again, you will now see the newly written dataset in the list:

In [11]:

list(team_store.get_data_ids())

list(team_store.get_data_ids())

Out[11]:

['SST.levels',
 'amazonas_v8.zarr',
 'amazonas_v9.zarr',
 'analysed_lst.levels',
 'noise_trajectory.zarr',
 'reanalysis-era5-single-levels-monthly-means-subset-2001-2010_TMH.zarr']

Open data from your team storage:

In [12]:

ml_dataset = team_store.open_data("analysed_lst.levels")
ml_dataset

ml_dataset = team_store.open_data("analysed_lst.levels") ml_dataset

Out[12]:

<xcube.core.mldataset.fs.FsMultiLevelDataset at 0x7f7ebe387290>

Check, how many levels were written:

In [13]:

ml_dataset.num_levels

ml_dataset.num_levels

Out[13]:

2

Print metadata details for each multilevel dataset

In [14]:

for level in range(ml_dataset.num_levels):
    dataset_i = ml_dataset.get_dataset(level)
    display(dataset_i)

for level in range(ml_dataset.num_levels): dataset_i = ml_dataset.get_dataset(level) display(dataset_i)

<xarray.Dataset> Size: 224MB
Dimensions:  (lat: 3500, lon: 4000, time: 2)
Coordinates:
  * lat      (lat) float32 14kB 35.01 35.01 35.02 35.03 ... 69.97 69.98 69.99
  * lon      (lon) float32 16kB -9.995 -9.985 -9.975 ... 29.98 29.98 29.99
  * time     (time) datetime64[ns] 16B 2016-10-01 2016-10-02
Data variables:
    lst      (time, lat, lon) float64 224MB dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>
Attributes: (12/42)
    Conventions:                CF-1.8
    catalogue_url:              https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d...
    cdm_data_type:              grid
    comment:                    These data were produced as part of the ESA L...
    creator_email:              djg20@le.ac.uk
    creator_name:               University of Leicester Surface Temperature G...
    ...                         ...
    summary:                    This file contains level L3C global land surf...
    time_coverage_duration:     PT04M59S
    time_coverage_end:          19700101T000001Z
    time_coverage_resolution:   P1D
    time_coverage_start:        19700101T000001Z
    title:                      ESA LST CCI land surface temperature data at ...

xarray.Dataset

• Dimensions:
  • lat: 3500
  • lon: 4000
  • time: 2
• Coordinates: (3)
  • lat
    (lat)
    float32
    35.01 35.01 35.02 ... 69.98 69.99

    long_name :

    latitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([35.005   , 35.014996, 35.024998, ..., 69.975   , 69.98499 , 69.99499 ],
          dtype=float32)

  • lon
    (lon)
    float32
    -9.995 -9.985 ... 29.98 29.99

    long_name :

    longitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-9.995   , -9.985005, -9.975011, ..., 29.975   , 29.984995, 29.99499 ],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2016-10-01 2016-10-02

    long_name :

    reference time of file

    standard_name :

    time

    array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • lst
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

    long_name :

    land surface temperature

    units :

    kelvin

    valid_max :

    7685

    valid_min :

    -8315

    Array Chunk Bytes 213.62 MiB 30.52 MiB Shape (2, 3500, 4000) (1, 2000, 2000) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([35.005001068115234,  35.01499557495117,  35.02499771118164,
            35.03499984741211,  35.04500198364258, 35.054996490478516,
           35.064998626708984,  35.07500076293945,  35.08499526977539,
            35.09499740600586,
           ...
            69.90499114990234,  69.91500091552734,  69.92499542236328,
            69.93498992919922,  69.94499969482422,  69.95499420166016,
             69.9649887084961,   69.9749984741211,  69.98499298095703,
            69.99498748779297],
          dtype='float32', name='lat', length=3500))

  • lon
    PandasIndex

    PandasIndex(Index([-9.994999885559082, -9.985005378723145, -9.975010871887207,
           -9.965001106262207,  -9.95500659942627,  -9.94499683380127,
           -9.935002326965332, -9.925007820129395, -9.914998054504395,
           -9.905003547668457,
           ...
           29.904993057250977, 29.914987564086914, 29.924997329711914,
            29.93499183654785,  29.94500160217285,  29.95499610900879,
           29.964990615844727, 29.975000381469727, 29.984994888305664,
             29.9949893951416],
          dtype='float32', name='lon', length=4000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-10-01', '2016-10-02'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (42)

  Conventions :

  CF-1.8

  catalogue_url :

  https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d247bcb3da6aed42f4b59a

  cdm_data_type :

  grid

  comment :

  These data were produced as part of the ESA LST CCI project.

  creator_email :

  djg20@le.ac.uk

  creator_name :

  University of Leicester Surface Temperature Group

  creator_url :

  https://climate.esa.int/en/projects/land-surface-temperature

  date_created :

  20211204T115246Z

  doi :

  10.5285/6babb8d9a8d247bcb3da6aed42f4b59a

  format_version :

  CCI Data Standards v2.2

  geospatial_lat_max :

  -68.13521575927734

  geospatial_lat_min :

  -89.99625396728516

  geospatial_lat_resolution :

  0.009999999776482582

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  179.9976348876953

  geospatial_lon_min :

  -179.99952697753906

  geospatial_lon_resolution :

  0.009999999776482582

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  history :

  Created using software developed at University of Leicester

  id :

  ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-20181231000000-fv3.00.nc

  institution :

  University of Leicester

  key_variables :

  land_surface_temperature

  keywords :

  Earth Science; Land Surface; Land Temperature; Land Surface Temperature

  keywords_vocabulary :

  NASA Global change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  le.ac.uk

  platform :

  Aqua

  product_version :

  3.00

  project :

  Climate Change Initiative - European Space Agency

  references :

  https://climate.esa.int/en/projects/land-surface-temperature

  sensor :

  MODIS

  source :

  ESA LST CCI MODISA L3U V3.00

  spatial_resolution :

  0.01 degree

  standard_name_vocabulary :

  CF Standard Name Table v71

  summary :

  This file contains level L3C global land surface temperatures from MODIS. L3C data are derived from multiple orbits of single sensor L3U data combined onto a space and/or time grid.

  time_coverage_duration :

  PT04M59S

  time_coverage_end :

  19700101T000001Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  19700101T000001Z

  title :

  ESA LST CCI land surface temperature data at product level L3C from Moderate Resolution Imaging Spectroradiometer.

<xarray.Dataset> Size: 56MB
Dimensions:  (lat: 1750, lon: 2000, time: 2)
Coordinates:
  * lat      (lat) float32 7kB 35.01 35.02 35.05 35.06 ... 69.94 69.96 69.98
  * lon      (lon) float32 8kB -9.995 -9.975 -9.955 -9.935 ... 29.95 29.96 29.98
  * time     (time) datetime64[ns] 16B 2016-10-01 2016-10-02
Data variables:
    lst      (time, lat, lon) float64 56MB dask.array<chunksize=(1, 1750, 2000), meta=np.ndarray>
Attributes: (12/42)
    Conventions:                CF-1.8
    catalogue_url:              https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d...
    cdm_data_type:              grid
    comment:                    These data were produced as part of the ESA L...
    creator_email:              djg20@le.ac.uk
    creator_name:               University of Leicester Surface Temperature G...
    ...                         ...
    summary:                    This file contains level L3C global land surf...
    time_coverage_duration:     PT04M59S
    time_coverage_end:          19700101T000001Z
    time_coverage_resolution:   P1D
    time_coverage_start:        19700101T000001Z
    title:                      ESA LST CCI land surface temperature data at ...

xarray.Dataset

• Dimensions:
  • lat: 1750
  • lon: 2000
  • time: 2
• Coordinates: (3)
  • lat
    (lat)
    float32
    35.01 35.02 35.05 ... 69.96 69.98

    long_name :

    latitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([35.005   , 35.024998, 35.045002, ..., 69.945   , 69.96499 , 69.98499 ],
          dtype=float32)

  • lon
    (lon)
    float32
    -9.995 -9.975 ... 29.96 29.98

    long_name :

    longitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-9.995   , -9.975011, -9.955007, ..., 29.945002, 29.96499 , 29.984995],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2016-10-01 2016-10-02

    long_name :

    reference time of file

    standard_name :

    time

    array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • lst
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 1750, 2000), meta=np.ndarray>

    long_name :

    land surface temperature

    units :

    kelvin

    valid_max :

    7685

    valid_min :

    -8315

    Array Chunk Bytes 53.41 MiB 26.70 MiB Shape (2, 1750, 2000) (1, 1750, 2000) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([35.005001068115234,  35.02499771118164,  35.04500198364258,
           35.064998626708984,  35.08499526977539,  35.10499954223633,
           35.124996185302734,  35.14500045776367,  35.16499710083008,
           35.185001373291016,
           ...
            69.80500030517578,  69.82498931884766,   69.8449935913086,
            69.86499786376953,   69.8849868774414,  69.90499114990234,
            69.92499542236328,  69.94499969482422,   69.9649887084961,
            69.98499298095703],
          dtype='float32', name='lat', length=1750))

  • lon
    PandasIndex

    PandasIndex(Index([-9.994999885559082, -9.975010871887207,  -9.95500659942627,
           -9.935002326965332, -9.914998054504395,  -9.89500904083252,
           -9.875004768371582, -9.855000495910645, -9.834996223449707,
           -9.815007209777832,
           ...
            29.80498695373535,  29.82499122619629, 29.844995498657227,
           29.864999771118164,  29.88498878479004, 29.904993057250977,
           29.924997329711914,  29.94500160217285, 29.964990615844727,
           29.984994888305664],
          dtype='float32', name='lon', length=2000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-10-01', '2016-10-02'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (42)

  Conventions :

  CF-1.8

  catalogue_url :

  https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d247bcb3da6aed42f4b59a

  cdm_data_type :

  grid

  comment :

  These data were produced as part of the ESA LST CCI project.

  creator_email :

  djg20@le.ac.uk

  creator_name :

  University of Leicester Surface Temperature Group

  creator_url :

  https://climate.esa.int/en/projects/land-surface-temperature

  date_created :

  20211204T115246Z

  doi :

  10.5285/6babb8d9a8d247bcb3da6aed42f4b59a

  format_version :

  CCI Data Standards v2.2

  geospatial_lat_max :

  -68.13521575927734

  geospatial_lat_min :

  -89.99625396728516

  geospatial_lat_resolution :

  0.009999999776482582

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  179.9976348876953

  geospatial_lon_min :

  -179.99952697753906

  geospatial_lon_resolution :

  0.009999999776482582

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  history :

  Created using software developed at University of Leicester

  id :

  ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-20181231000000-fv3.00.nc

  institution :

  University of Leicester

  key_variables :

  land_surface_temperature

  keywords :

  Earth Science; Land Surface; Land Temperature; Land Surface Temperature

  keywords_vocabulary :

  NASA Global change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  le.ac.uk

  platform :

  Aqua

  product_version :

  3.00

  project :

  Climate Change Initiative - European Space Agency

  references :

  https://climate.esa.int/en/projects/land-surface-temperature

  sensor :

  MODIS

  source :

  ESA LST CCI MODISA L3U V3.00

  spatial_resolution :

  0.01 degree

  standard_name_vocabulary :

  CF Standard Name Table v71

  summary :

  This file contains level L3C global land surface temperatures from MODIS. L3C data are derived from multiple orbits of single sensor L3U data combined onto a space and/or time grid.

  time_coverage_duration :

  PT04M59S

  time_coverage_end :

  19700101T000001Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  19700101T000001Z

  title :

  ESA LST CCI land surface temperature data at product level L3C from Moderate Resolution Imaging Spectroradiometer.

To work with a certain level:

In [15]:

dataset_1 = ml_dataset.get_dataset(1)

dataset_1 = ml_dataset.get_dataset(1)

In [16]:

dataset_1

dataset_1

Out[16]:

<xarray.Dataset> Size: 56MB
Dimensions:  (lat: 1750, lon: 2000, time: 2)
Coordinates:
  * lat      (lat) float32 7kB 35.01 35.02 35.05 35.06 ... 69.94 69.96 69.98
  * lon      (lon) float32 8kB -9.995 -9.975 -9.955 -9.935 ... 29.95 29.96 29.98
  * time     (time) datetime64[ns] 16B 2016-10-01 2016-10-02
Data variables:
    lst      (time, lat, lon) float64 56MB dask.array<chunksize=(1, 1750, 2000), meta=np.ndarray>
Attributes: (12/42)
    Conventions:                CF-1.8
    catalogue_url:              https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d...
    cdm_data_type:              grid
    comment:                    These data were produced as part of the ESA L...
    creator_email:              djg20@le.ac.uk
    creator_name:               University of Leicester Surface Temperature G...
    ...                         ...
    summary:                    This file contains level L3C global land surf...
    time_coverage_duration:     PT04M59S
    time_coverage_end:          19700101T000001Z
    time_coverage_resolution:   P1D
    time_coverage_start:        19700101T000001Z
    title:                      ESA LST CCI land surface temperature data at ...

xarray.Dataset

• Dimensions:
  • lat: 1750
  • lon: 2000
  • time: 2
• Coordinates: (3)
  • lat
    (lat)
    float32
    35.01 35.02 35.05 ... 69.96 69.98

    long_name :

    latitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([35.005   , 35.024998, 35.045002, ..., 69.945   , 69.96499 , 69.98499 ],
          dtype=float32)

  • lon
    (lon)
    float32
    -9.995 -9.975 ... 29.96 29.98

    long_name :

    longitude_coordinates

    reference_datum :

    geographical coordinates, WGS84 projection

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-9.995   , -9.975011, -9.955007, ..., 29.945002, 29.96499 , 29.984995],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2016-10-01 2016-10-02

    long_name :

    reference time of file

    standard_name :

    time

    array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • lst
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 1750, 2000), meta=np.ndarray>

    long_name :

    land surface temperature

    units :

    kelvin

    valid_max :

    7685

    valid_min :

    -8315

    Array Chunk Bytes 53.41 MiB 26.70 MiB Shape (2, 1750, 2000) (1, 1750, 2000) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([35.005001068115234,  35.02499771118164,  35.04500198364258,
           35.064998626708984,  35.08499526977539,  35.10499954223633,
           35.124996185302734,  35.14500045776367,  35.16499710083008,
           35.185001373291016,
           ...
            69.80500030517578,  69.82498931884766,   69.8449935913086,
            69.86499786376953,   69.8849868774414,  69.90499114990234,
            69.92499542236328,  69.94499969482422,   69.9649887084961,
            69.98499298095703],
          dtype='float32', name='lat', length=1750))

  • lon
    PandasIndex

    PandasIndex(Index([-9.994999885559082, -9.975010871887207,  -9.95500659942627,
           -9.935002326965332, -9.914998054504395,  -9.89500904083252,
           -9.875004768371582, -9.855000495910645, -9.834996223449707,
           -9.815007209777832,
           ...
            29.80498695373535,  29.82499122619629, 29.844995498657227,
           29.864999771118164,  29.88498878479004, 29.904993057250977,
           29.924997329711914,  29.94500160217285, 29.964990615844727,
           29.984994888305664],
          dtype='float32', name='lon', length=2000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-10-01', '2016-10-02'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (42)

  Conventions :

  CF-1.8

  catalogue_url :

  https://catalogue.ceda.ac.uk/uuid/6babb8d9a8d247bcb3da6aed42f4b59a

  cdm_data_type :

  grid

  comment :

  These data were produced as part of the ESA LST CCI project.

  creator_email :

  djg20@le.ac.uk

  creator_name :

  University of Leicester Surface Temperature Group

  creator_url :

  https://climate.esa.int/en/projects/land-surface-temperature

  date_created :

  20211204T115246Z

  doi :

  10.5285/6babb8d9a8d247bcb3da6aed42f4b59a

  format_version :

  CCI Data Standards v2.2

  geospatial_lat_max :

  -68.13521575927734

  geospatial_lat_min :

  -89.99625396728516

  geospatial_lat_resolution :

  0.009999999776482582

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  179.9976348876953

  geospatial_lon_min :

  -179.99952697753906

  geospatial_lon_resolution :

  0.009999999776482582

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  history :

  Created using software developed at University of Leicester

  id :

  ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-20181231000000-fv3.00.nc

  institution :

  University of Leicester

  key_variables :

  land_surface_temperature

  keywords :

  Earth Science; Land Surface; Land Temperature; Land Surface Temperature

  keywords_vocabulary :

  NASA Global change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  le.ac.uk

  platform :

  Aqua

  product_version :

  3.00

  project :

  Climate Change Initiative - European Space Agency

  references :

  https://climate.esa.int/en/projects/land-surface-temperature

  sensor :

  MODIS

  source :

  ESA LST CCI MODISA L3U V3.00

  spatial_resolution :

  0.01 degree

  standard_name_vocabulary :

  CF Standard Name Table v71

  summary :

  This file contains level L3C global land surface temperatures from MODIS. L3C data are derived from multiple orbits of single sensor L3U data combined onto a space and/or time grid.

  time_coverage_duration :

  PT04M59S

  time_coverage_end :

  19700101T000001Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  19700101T000001Z

  title :

  ESA LST CCI land surface temperature data at product level L3C from Moderate Resolution Imaging Spectroradiometer.

To delete a dataset from your team user space:

In [17]:

team_store.delete_data("analysed_lst.levels")

team_store.delete_data("analysed_lst.levels")

In [ ]: