Walk-through: Visualising Data¶

Description & purpose: This Notebook is designed to showcase the functionality of the Earth Observation Data Hub (EODH). It provides a snapshot of the Hub, the pyeodh API client and the various datasets as of February 2025. Much of what follows is still relevant, although some user interface components may have changes. Contact [EODH enquiries](mailto: enquiries@eodatahub.org.uk) if further help is required.

Set-up¶

The following cell only needs to be run on the EODH AppHub. If you have a local Python environment running, please install the required packages as you would normally. If running locally, it's easiest to install all dependencies using uv (https://docs.astral.sh/uv/getting-started/installation/) by running uv sync in the root directory of the repository.

In [ ]:

# If needed you can install a package in the current AppHub Jupyter environment using pip
# For instance, we will need at least the following libraries
%pip install --upgrade pyeodh geopandas matplotlib numpy folium xarray geoviews hvplot holoviews datashader cartopy

# If needed you can install a package in the current AppHub Jupyter environment using pip # For instance, we will need at least the following libraries %pip install --upgrade pyeodh geopandas matplotlib numpy folium xarray geoviews hvplot holoviews datashader cartopy

Introduction¶

In this notebook we will explore different ways to visualise data held on the Hub. The Notebooks can be run on the JupyterHub instance on the Hub (linked to your user account) or locally with a suitably set up environment.

In [5]:

# Imports
import os

# Import the Python API Client
import pyeodh

import folium
import xarray as xr

import hvplot.xarray  # Needed for xarray plotting with geoviews
import holoviews as hv
import datashader as ds
from holoviews import opts
from holoviews.operation.datashader import rasterize
import cartopy.crs as ccrs  # For coordinate reference systems
import geoviews as gv

import requests
from IPython.display import Image, display

# Parameterise geoviews
gv.extension("bokeh", "matplotlib")

# Imports import os # Import the Python API Client import pyeodh import folium import xarray as xr import hvplot.xarray # Needed for xarray plotting with geoviews import holoviews as hv import datashader as ds from holoviews import opts from holoviews.operation.datashader import rasterize import cartopy.crs as ccrs # For coordinate reference systems import geoviews as gv import requests from IPython.display import Image, display # Parameterise geoviews gv.extension("bokeh", "matplotlib")

Accessing Different DataTypes¶

For this part of the workshop exercises we are going to look at how to visualise two different datasets. This should give you the foundation and understanding about how you could do this for other data held on the Hub and elsewhere. The first thing we need to do is connect to the EODH and get information about the collections we are interested in: cmip6 and the sentinel 2 ARD.

In [6]:

# Connect to the Hub
# Change base_url to point to the server that you want to connect to

client = pyeodh.Client(base_url="https://eodatahub.org.uk").get_catalog_service()

catalog = client.get_catalog("public/catalogs/ceda-stac-catalogue")

# Get each collection
cmip6 = catalog.get_collection("cmip6")
sentinel2_ard = catalog.get_collection("sentinel2_ard")

# Connect to the Hub # Change base_url to point to the server that you want to connect to client = pyeodh.Client(base_url="https://eodatahub.org.uk").get_catalog_service() catalog = client.get_catalog("public/catalogs/ceda-stac-catalogue") # Get each collection cmip6 = catalog.get_collection("cmip6") sentinel2_ard = catalog.get_collection("sentinel2_ard")

We can interrogate the temporal and spatial extents of the data holdings. Below, we have printed out one of each for each dataset that we have connected to. This can help us understand what date ranges or spatial extents we can use when visualising the data.

In [7]:

# Get the collection metadata
extent = ["spatial", "temporal"]

print("Dataset extent - CMIP6: ", cmip6.extent.to_dict()[extent[1]])
print("Dataset extent - Sentinel 2 ARD: ", sentinel2_ard.extent.to_dict()[extent[0]])

# Get the collection metadata extent = ["spatial", "temporal"] print("Dataset extent - CMIP6: ", cmip6.extent.to_dict()[extent[1]]) print("Dataset extent - Sentinel 2 ARD: ", sentinel2_ard.extent.to_dict()[extent[0]])

Dataset extent - CMIP6:  {'interval': [['1850-01-01T00:00:00Z', '4114-12-16T12:00:00Z']]}
Dataset extent - Sentinel 2 ARD:  {'bbox': [[-9.00034454651177, 49.48562028352171, 3.1494256015866995, 61.33444247301668]]}

As this is an exercise, we have already chosen some data to look at.

The CMIP6 item has been generated using the CIESM model, running the high-emission SSP5-8.5 scenario. It contains monthly-averaged upward shortwave radiation at the surface (rsus) on a regular grid.

The Sentinel 2 ARD image covers an area acros sthe south of England, including the Solent.

In [8]:

# Look for a specific item
cmip6_item = cmip6.get_item(
    "CMIP6.ScenarioMIP.THU.CIESM.ssp585.r1i1p1f1.Amon.rsus.gr.v20200806"
)
sentinel_item = sentinel2_ard.get_item(
    "neodc.sentinel_ard.data.sentinel_2.2023.11.17.S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb"
)

# Look for a specific item cmip6_item = cmip6.get_item( "CMIP6.ScenarioMIP.THU.CIESM.ssp585.r1i1p1f1.Amon.rsus.gr.v20200806" ) sentinel_item = sentinel2_ard.get_item( "neodc.sentinel_ard.data.sentinel_2.2023.11.17.S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb" )

In [9]:

# Get information and links to the item assets held in STAC

print("---" * 20, "CMIP6", "---" * 20)

print(cmip6_item.assets)

print("")
print("---" * 20, "SENTINEL 2", "---" * 20)

print(sentinel_item.assets)

# Get information and links to the item assets held in STAC print("---" * 20, "CMIP6", "---" * 20) print(cmip6_item.assets) print("") print("---" * 20, "SENTINEL 2", "---" * 20) print(sentinel_item.assets)

------------------------------------------------------------ CMIP6 ------------------------------------------------------------
{'reference_file': <Asset href=https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/ScenarioMIP/THU/CIESM/kr1.0/CMIP6_ScenarioMIP_THU_CIESM_ssp585_r1i1p1f1_Amon_rsus_gr_v20200806_kr1.0.json>, 'data0001': <Asset href=https://dap.ceda.ac.uk/badc/cmip6/data/CMIP6/ScenarioMIP/THU/CIESM/ssp585/r1i1p1f1/Amon/rsus/gr/v20200806/rsus_Amon_CIESM_ssp585_r1i1p1f1_gr_402901-411412.nc>}

------------------------------------------------------------ SENTINEL 2 ------------------------------------------------------------
{'cloud': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_clouds.tif>, 'cloud_probability': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_clouds_prob.tif>, 'cog': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_vmsk_sharp_rad_srefdem_stdsref.tif>, 'metadata': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_vmsk_sharp_rad_srefdem_stdsref_meta.xml>, 'saturated_pixels': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_sat.tif>, 'thumbnail': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_vmsk_sharp_rad_srefdem_stdsref_thumbnail.jpg>, 'topographic_shadow': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_toposhad.tif>, 'valid_pixels': <Asset href=https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_valid.tif>}

We can then use the information above to gather links to the asset data

In [10]:

cmip6_kerchunk_asset = cmip6_item.assets["reference_file"]
sentinel2_ard_cog_asset = sentinel_item.assets["cog"]

# print the href
print(cmip6_kerchunk_asset.href)
print(sentinel2_ard_cog_asset.href)

cmip6_kerchunk_asset = cmip6_item.assets["reference_file"] sentinel2_ard_cog_asset = sentinel_item.assets["cog"] # print the href print(cmip6_kerchunk_asset.href) print(sentinel2_ard_cog_asset.href)

https://dap.ceda.ac.uk/badc/cmip6/metadata/kerchunk/pipeline1/ScenarioMIP/THU/CIESM/kr1.0/CMIP6_ScenarioMIP_THU_CIESM_ssp585_r1i1p1f1_Amon_rsus_gr_v20200806_kr1.0.json
https://dap.ceda.ac.uk/neodc/sentinel_ard/data/sentinel_2/2023/11/17/S2A_20231117_latn509lonw0008_T30UXB_ORB137_20231117131218_utm30n_osgb_vmsk_sharp_rad_srefdem_stdsref.tif

Manipulating and plotting data¶

Plot a single timestep¶

For the next part of this exercise we will undertake the following steps:

• Access our CMIP6 Dataset
• Extract the dataset (Kerchunk)
• Select a single timestep
• Plot resulting image

In [11]:

# Get information on the cloud product
product = cmip6_item.get_cloud_products()
product

# Get information on the cloud product product = cmip6_item.get_cloud_products() product

Out[11]:

<DataPointCloudProduct: CMIP6.ScenarioMIP.THU.CIESM.ssp585.r1i1p1f1.Amon.rsus.gr.v20200806-reference_file (Format: kerchunk)>
 - bbox: [-180.0, -90.0, 178.75, 90.0]
 - asset_id: CMIP6.ScenarioMIP.THU.CIESM.ssp585.r1i1p1f1.Amon.rsus.gr.v20200806-reference_file
 - cloud_format: kerchunk
Attributes:
 - title: CMIP6.ScenarioMIP.THU.CIESM.ssp585.r1i1p1f1.Amon.rsus.gr.v20200806
 - datetime: 4072-01-01T00:00:00Z
 - created: 2025-01-24T14:29:23.741213Z
 - updated: 2025-08-29T03:10:04.291804Z
 - start_datetime: 4029-01-16T12:00:00Z
 - end_datetime: 4114-12-16T12:00:00Z
 - cmip6:further_info_url: https://furtherinfo.es-doc.org/CMIP6.THU.CIESM.ssp585.none.r1i1p1f1
 - cmip6:source_type: AOGCM
 - cmip6:institution_id: THU
 - cmip6:variable_long_name: Surface Upwelling Shortwave Radiation
 - cmip6:mip_era: CMIP6
 - project: CMIP6
 - cmip6:access: ['HTTPServer']
 - cmip6:nominal_resolution: 100 km
 - cmip6:sub_experiment_id: none
 - cmip6:frequency: mon
 - cmip6:experiment_id: ssp585
 - cmip6:table_id: Amon
 - cmip6:activity_id: ScenarioMIP
 - product: model-output
 - model_cohort: Registered
 - cmip6:grid_label: gr
 - cmip6:cf_standard_name: surface_upwelling_shortwave_flux_in_air
 - cmip6:data_specs_version: 01.00.29
 - cmip6:variable_id: rsus
 - cmip6:variable_units: W m-2
 - cmip6:source_id: CIESM
 - cmip6:experiment_title: update of RCP8.5 based on SSP5
 - cmip6:citation_url: http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.ScenarioMIP.THU.CIESM.ssp585.r1i1p1f1.Amon.rsus.gr.v20200806.json
 - cmip6:variant_label: r1i1p1f1
 - realm: ['atmos']
 - cmip6:retracted: False
 - cmip6:grid: gs1x1

We can see that this item contains a single product, a kerchunk reference file which allows us to access the dataset as a single object. The format doesn't actually matter here, as the tools we are using here allows us to open any recognised format into xarray with minimal understanding of the data format on the user side.

The following code cell opens the file as an xarray dataset and presents the user with information about the array structure.

In [12]:

ds = product.open_dataset()
ds

ds = product.open_dataset() ds

Out[12]:

<xarray.Dataset> Size: 228MB
Dimensions:    (lat: 192, bnds: 2, lon: 288, time: 1032)
Coordinates:
  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
  * time       (time) object 8kB 4029-01-16 12:00:00 ... 4114-12-16 12:00:00
Dimensions without coordinates: bnds
Data variables:
    lat_bnds   (lat, bnds) float64 3kB dask.array<chunksize=(192, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 5kB dask.array<chunksize=(288, 2), meta=np.ndarray>
    rsus       (time, lat, lon) float32 228MB dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
    time_bnds  (time, bnds) object 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>
Attributes: (12/46)
    Conventions:            CF-1.7 CMIP-6.2
    activity_id:            ScenarioMIP
    branch_method:          standard
    branch_time_in_child:   735110.0
    branch_time_in_parent:  735110.0
    cmor_version:           3.6.0
    ...                     ...
    table_id:               Amon
    table_info:             Creation Date:(20 February 2019) MD5:510997cd0a2c...
    title:                  CIESM output prepared for CMIP6
    tracking_id:            hdl:21.14100/26602daf-2379-491b-ad98-2ebb2f581db7
    variable_id:            rsus
    variant_label:          r1i1p1f1

xarray.Dataset

• Dimensions:
  • lat: 192
  • bnds: 2
  • lon: 288
  • time: 1032
• Coordinates: (3)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
           -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
           -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
           -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
           -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
           -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
           -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
           -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
           -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
           -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
           -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
           -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
            23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
            28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
            34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
            40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
            45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
            51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
            57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
            62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
            68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
            73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
            79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
            85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75], shape=(288,))

  • time
    (time)
    object
    4029-01-16 12:00:00 ... 4114-12-...

    axis :

    T

    bounds :

    time_bnds

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeNoLeap(4029, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(4029, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(4029, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(4114, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(4114, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(4114, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          shape=(1032,), dtype=object)

• Data variables: (4)
  • lat_bnds
    (lat, bnds)
    float64
    dask.array<chunksize=(192, 2), meta=np.ndarray>

    Array Chunk Bytes 3.00 kiB 3.00 kiB Shape (192, 2) (192, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_bnds
    (lon, bnds)
    float64
    dask.array<chunksize=(288, 2), meta=np.ndarray>

    Array Chunk Bytes 4.50 kiB 4.50 kiB Shape (288, 2) (288, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rsus
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    The surface called 'surface' means the lower boundary of the atmosphere. 'shortwave' means shortwave radiation. Upwelling radiation is radiation from below. It does not mean 'net upward'. When thought of as being incident on a surface, a radiative flux is sometimes called 'irradiance'. In addition, it is identical with the quantity measured by a cosine-collector light-meter and sometimes called 'vector irradiance'. In accordance with common usage in geophysical disciplines, 'flux' implies per unit area, called 'flux density' in physics.

    long_name :

    Surface Upwelling Shortwave Radiation

    original_name :

    rsus

    standard_name :

    surface_upwelling_shortwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 217.69 MiB 216.00 kiB Shape (1032, 192, 288) (1, 192, 288) Dask graph 1032 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 16.12 kiB 16 B Shape (1032, 2) (1, 2) Dask graph 1032 chunks in 2 graph layers Data type object numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([             -90.0, -89.05759162303664,  -88.1151832460733,
           -87.17277486910994,  -86.2303664921466, -85.28795811518324,
            -84.3455497382199, -83.40314136125654, -82.46073298429319,
           -81.51832460732984,
           ...
            81.51832460732984,   82.4607329842932,  83.40314136125653,
            84.34554973821989,  85.28795811518324,   86.2303664921466,
            87.17277486910996,  88.11518324607329,  89.05759162303664,
                         90.0],
          dtype='float64', name='lat', length=192))

  • lon
    PandasIndex

    PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
            11.25,
           ...
            347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
           358.75],
          dtype='float64', name='lon', length=288))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([4029-01-16 12:00:00, 4029-02-15 00:00:00, 4029-03-16 12:00:00,
                 4029-04-16 00:00:00, 4029-05-16 12:00:00, 4029-06-16 00:00:00,
                 4029-07-16 12:00:00, 4029-08-16 12:00:00, 4029-09-16 00:00:00,
                 4029-10-16 12:00:00,
                 ...
                 4114-03-16 12:00:00, 4114-04-16 00:00:00, 4114-05-16 12:00:00,
                 4114-06-16 00:00:00, 4114-07-16 12:00:00, 4114-08-16 12:00:00,
                 4114-09-16 00:00:00, 4114-10-16 12:00:00, 4114-11-16 00:00:00,
                 4114-12-16 12:00:00],
                dtype='object', length=1032, calendar='noleap', freq=None))

• Attributes: (46)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  ScenarioMIP

  branch_method :

  standard

  branch_time_in_child :

  735110.0

  branch_time_in_parent :

  735110.0

  cmor_version :

  3.6.0

  contact :

  yanluan@tsinghua.edu.cn

  creation_date :

  2020-07-22T10:30:50Z

  data_specs_version :

  01.00.29

  experiment :

  update of RCP8.5 based on SSP5

  experiment_id :

  ssp585

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.THU.CIESM.ssp585.none.r1i1p1f1

  grid :

  gs1x1

  grid_label :

  gr

  history :

  2020-07-22T10:30:17Z ;rewrote data to be consistent with ScenarioMIP for variable rlus found in table Amon.; Output from CIESM

  initialization_index :

  1

  institution :

  Department of Earth System Science, Tsinghua University, Beijing 100084, China

  institution_id :

  THU

  license :

  CMIP6 model data produced by Department of Earth System Science, Tsinghua University is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  historical

  parent_mip_era :

  CMIP6

  parent_source_id :

  CIESM

  parent_time_units :

  days since 0001-01-01

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  run_variant :

  1rd realization

  source :

  CIESM (2019): aerosol: prescribed MACv2-SP atmos: CIESM-AM1.0 (Modified CAM5; 1 degree spectral element; 48602 cells; 30 levels; top level 2.255 hPa) atmosChem: none land: CIESM-LM1.0 (Modified CLM4.0) landIce: none ocean: CIESM-OM1.0 (Modified POP2; 320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m) ocnBgchem: none seaIce: CICE4

  source_id :

  CIESM

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  table_info :

  Creation Date:(20 February 2019) MD5:510997cd0a2c38131ae713cdf8560209

  title :

  CIESM output prepared for CMIP6

  tracking_id :

  hdl:21.14100/26602daf-2379-491b-ad98-2ebb2f581db7

  variable_id :

  rsus

  variant_label :

  r1i1p1f1

Looking at the output above we can see that the dataset has four Data variables. We can perform various selections on the data but specifically we need to choose the variable containng the data to plot, and then select the time step we are interested in. We will use the geoviews package to plot the resulting map.

In [13]:

data_var = list(ds.data_vars)[2]  # Choose the data variable (modify if needed)
print("Variable: ", data_var)

timestep = 555  # choose any number within the range presented in the xarray oiutput i.e. up to 1032 in this case

data_var = list(ds.data_vars)[2] # Choose the data variable (modify if needed) print("Variable: ", data_var) timestep = 555 # choose any number within the range presented in the xarray oiutput i.e. up to 1032 in this case

Variable:  rsus

In [14]:

selected_data = ds[data_var].isel(time=timestep)  # Select the data for the time step

# Plot the data
plot = selected_data.hvplot.quadmesh(
    x="lon",
    y="lat",
    cmap="plasma",
    geo=True,
    projection=ccrs.PlateCarree(),
    coastline=True,
    title=f"Time Step: {ds.time.values[timestep]}",
)
plot

selected_data = ds[data_var].isel(time=timestep) # Select the data for the time step # Plot the data plot = selected_data.hvplot.quadmesh( x="lon", y="lat", cmap="plasma", geo=True, projection=ccrs.PlateCarree(), coastline=True, title=f"Time Step: {ds.time.values[timestep]}", ) plot

/home/figi/software/work/eodh/eodh-training/.venv/lib/python3.10/site-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

Out[14]: