Polar Data¶

Description & purpose: This Notebook is designed to demonstrate how to find and plot EOCIS related data held on the Earth Observation Data Hub. It is assumed that this notebook will be running within the Notebook Service on the Hub.

The first thing to do is ensure that the most recent version of pyeodh is installed on your system. It is good practice to run the following cell if you have not installed pyeodh or have not used it in a while. The cell will also install the plotting and spatial libraries required.

In [ ]:

# Run this cell if pyeodh is not installed, or needs updating
%pip install --upgrade pyeodh
%pip install folium shapely xarray fsspec rioxarray cartopy pyproj

# Run this cell if pyeodh is not installed, or needs updating %pip install --upgrade pyeodh %pip install folium shapely xarray fsspec rioxarray cartopy pyproj

Imports¶

In [ ]:

import pyeodh

import cartopy.crs as ccrs
import folium
import fsspec
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd

import shapely as sh
import xarray as xr
from pyproj import Transformer
from shapely.geometry import Point

import pyeodh import cartopy.crs as ccrs import folium import fsspec import geopandas as gpd import matplotlib.pyplot as plt import pandas as pd import shapely as sh import xarray as xr from pyproj import Transformer from shapely.geometry import Point

Set geographic locations¶

In [3]:

# Point of interest - ice/land (Nuuk, Greenland)
ipnt = sh.Point(-51.7216, 64.1835)

# Point of interest - ocean (Baffin Bay, Greenland)
olon, olat = [-54.22884, 83.11055]
opnt = sh.Point(olon, olat)

# Point of interest - ice/land (Nuuk, Greenland) ipnt = sh.Point(-51.7216, 64.1835) # Point of interest - ocean (Baffin Bay, Greenland) olon, olat = [-54.22884, 83.11055] opnt = sh.Point(olon, olat)

In [ ]:

# Create a folium map centred between the points
m = folium.Map(location=[(ipnt.y + opnt.y)/2, (ipnt.x + opnt.x)/2], zoom_start=3)

# Add points to the map
for pt, name in [(ipnt, "Nuuk"), (opnt, "Arctic Ocean")]:
    folium.Marker([pt.y, pt.x], popup=name).add_to(m)

m

# Create a folium map centred between the points m = folium.Map(location=[(ipnt.y + opnt.y)/2, (ipnt.x + opnt.x)/2], zoom_start=3) # Add points to the map for pt, name in [(ipnt, "Nuuk"), (opnt, "Arctic Ocean")]: folium.Marker([pt.y, pt.x], popup=name).add_to(m) m

Exploring the Resource Catalogue¶

Now we are ready to investigate the Resource Catalogue.

We need to create an instance of the Client, which is our entrypoint to EODH APIs. From there we can start to search the collections held within the platform. First we'll look at the paths to the data.

In [5]:

# Connect to the Hub
client = pyeodh.Client(base_url="https://eodatahub.org.uk").get_catalog_service()

for c in client.get_catalogs():
    print(c._pystac_object.self_href.removeprefix("https://eodatahub.org.uk/api/catalogue/stac/catalogs/"))

# Connect to the Hub client = pyeodh.Client(base_url="https://eodatahub.org.uk").get_catalog_service() for c in client.get_catalogs(): print(c._pystac_object.self_href.removeprefix("https://eodatahub.org.uk/api/catalogue/stac/catalogs/"))

commercial/catalogs/airbus
user/catalogs/npl/catalogs/processing-results/catalogs/qa-workflow/catalogs/airbus_phr_qa
user/catalogs/tjellicoetpzuk/catalogs/processing-results/catalogs/snuggs/catalogs/catalog
public/catalogs/ceda-stac-catalogue
commercial
user/catalogs/npl
commercial/catalogs/planet
user/catalogs/npl/catalogs/processing-results/catalogs/qa-workflow/catalogs/planet_psscene_qa
user/catalogs/npl/catalogs/processing-results
user/catalogs/tjellicoetpzuk/catalogs/processing-results

Now we'll look explicitly for the EOCIS climate data.

In [6]:

for index, collect in enumerate(
        [c for c in client.get_collections() if "eocis" in c.id],
        start=1):
    print(f"{index} -- {collect.id}")

for index, collect in enumerate( [c for c in client.get_collections() if "eocis" in c.id], start=1): print(f"{index} -- {collect.id}")

1 -- eocis-sst-cdrv3
2 -- eocis-soil-moisture-africa
3 -- eocis-lst-s3b-night
4 -- eocis-lst-s3b-day
5 -- eocis-lst-s3a-night
6 -- eocis-lst-s3a-day
7 -- eocis-chuk-land-vegetation-lai
8 -- eocis-chuk-land-vegetation-fapar
9 -- eocis-chuk-geospatial-landcover
10 -- eocis-chuk-geospatial-elevation
11 -- eocis-chuk-geospatial-builtarea
12 -- eocis-chuk-geospatial
13 -- eocis-arctic-sea-ice-thickness-monthly

Sea Ice¶

Note: For additional details see this link.

We need to connect to the Sea Ice collection and get some simple information about it

In [7]:

datasets = client.get_catalog(
    "public/catalogs/ceda-stac-catalogue").get_collection(
    'eocis-arctic-sea-ice-thickness-monthly')

print("id: ", datasets.id)
print("title: ", datasets.title)
print("description: ", datasets.description)
print("")
print(
    "DATASET TEMPORAL EXTENT: ",
    [str(d) for d in datasets.extent.temporal.intervals[0]],
)

datasets = client.get_catalog( "public/catalogs/ceda-stac-catalogue").get_collection( 'eocis-arctic-sea-ice-thickness-monthly') print("id: ", datasets.id) print("title: ", datasets.title) print("description: ", datasets.description) print("") print( "DATASET TEMPORAL EXTENT: ", [str(d) for d in datasets.extent.temporal.intervals[0]], )

id:  eocis-arctic-sea-ice-thickness-monthly
title:  EOCIS Arctic Monthly Gridded Sea Ice Thickness Product from CryoSat-2
description:  The sea ice products provide a 5x5 km grid of Arctic sea ice thickness (for the whole Arctic region and of 17 sub-regions), delivered as NetCDF files.  EOCIS sea ice thickness NetCDF products are generated monthly by the Centre for Polar Observation and Modelling (CPOM) from radar altimetry measurements taken from the ESA CryoSAT-2 satellite during the winter months (Oct-Apr). Sea ice thickness is only reliably measured from satellite radar altimetry during the winter months. During summer, melt ponds can form on the sea ice floes making it difficult for the satellite to differentiate between floes and leads, and hence calculate sea ice freeboard (and subsequently thickness). Measurement during summer months using radar altimetry is an area of active research (Landy et al, 2022) but is not yet operationally processed.

DATASET TEMPORAL EXTENT:  ['2010-11-15 00:00:00+00:00', '2024-11-15 00:00:00+00:00']

It would be useful to understand more about the items that are held within the catalogue. The attributes of a catalogue are mapped to a series of properties. For instance, we can find out the properties such asid, title and description.

In [8]:

# Now we want to access the first item and see what properties are available. Need to convert an iterator to a list first. 
items = list(datasets.get_items())
print("Number of items available: ", len(items))
print ("-----")

item = items[1]   # second item
print(f"\n{item.id}")
for k, v in item.properties.items():
    print(f"  {k}: {v}")

# Now we want to access the first item and see what properties are available. Need to convert an iterator to a list first. items = list(datasets.get_items()) print("Number of items available: ", len(items)) print ("-----") item = items[1] # second item print(f"\n{item.id}") for k, v in item.properties.items(): print(f" {k}: {v}")

Number of items available:  198
-----

0a2cdd11-d4e1-4806-ab4f-1a66849cf30a
  title: EOCIS Arctic Monthly Gridded Sea Ice Thickness 2024-11-15
  description: The sea ice products provide a 5x5 km grid of Arctic sea ice thickness (for the whole Arctic region and of 17 sub-regions), delivered as NetCDF files.  EOCIS sea ice thickness NetCDF products are generated monthly by the Centre for Polar Observation and Modelling (CPOM) from radar altimetry measurements taken from the ESA CryoSAT-2 satellite during the winter months (Oct-Apr). Sea ice thickness is only reliably measured from satellite radar altimetry during the winter months. During summer, melt ponds can form on the sea ice floes making it difficult for the satellite to differentiate between floes and leads, and hence calculate sea ice freeboard (and subsequently thickness). Measurement during summer months using radar altimetry is an area of active research (Landy et al, 2022) but is not yet operationally processed.
  datetime: 2024-11-15T00:00:00Z
  created: 2025-10-09T10:39:36.790911Z
  updated: 2025-10-09T10:39:36.790911Z
  license: cc-by-4.0
  platform: CryoSat-2
  instruments: ['SIRAL']
  sci:doi: https://dx.doi.org/10.5285/4c246cb11aa04651a0182b2d329a84f9
  sci:citation: Ridout, A.; Palmer, B.; Muir, A.; Swiggs, A.; McMillan, M.; Maddalena, J. (2025): EOCIS: Arctic Sea Ice Thickness Grids, v1.00. NERC EDS Centre for Environmental Data Analysis, 20 March 2025. doi:10.5285/4c246cb11aa04651a0182b2d329a84f9.
  sci:publications: []
  cube:dimensions: {'xc': {'type': 'spatial', 'axis': 'xc', 'extent': [-3850000, 3749999], 'reference_system': 3413, 'unit': 'm'}, 'yc': {'type': 'spatial', 'axis': 'yc', 'extent': [-5350000, 5849999], 'reference_system': 3413, 'unit': 'm'}, 'time': {'type': 'temporal', 'step': 'P1M', 'values': ['2024-11-15T00:00:00Z']}}
  cube:variables: {'sea_ice_thickness': {'description': 'sea ice thickness', 'dimensions': ['time', 'yc', 'xc'], 'type': 'data', 'unit': 'm'}, 'sea_ice_thickness_stdev': {'description': 'standard deviation of sea ice thickness, defined as the standard deviation of the thickness in the grid cell', 'dimensions': ['time', 'yc', 'xc'], 'type': 'data', 'unit': 'm'}, 'n_thickness_measurements': {'description': 'number of contributing thickness measurements', 'dimensions': ['time', 'yc', 'xc'], 'type': 'auxiliary'}}
  renders: {'sea_ice_thickness': {'title': 'Sea Ice Thickness', 'assets': ['reference_file'], 'reference': True, 'variable': 'sea_ice_thickness', 'colormap_name': 'blues_r', 'rescale': [0, 3.5]}, 'sea_ice_thickness_stdev': {'title': 'Standard deviation of Sea Ice Thickness', 'assets': ['reference_file'], 'reference': True, 'variable': 'sea_ice_thickness_stdev', 'colormap_name': 'viridis', 'rescale': [0, 2]}}
  project: UK Earth Observation Climate Information Service (EOCIS)
  institution: EOCIS UK
  resolution: 5 km

We can then get the item id, date and name for all items. It is recommended to only look at the first few as the number of items could be large.

In [9]:

# Warning: without the limit to 10 items this will take a long time for large catalogues
for item in datasets.get_items()[:10]:
    print(item.id, "---", item.properties["datetime"], "---", item.properties["title"])

# Warning: without the limit to 10 items this will take a long time for large catalogues for item in datasets.get_items()[:10]: print(item.id, "---", item.properties["datetime"], "---", item.properties["title"])

ea60feff-3a21-49d1-b79f-9179c052cfc0 --- 2024-11-15T00:00:00Z --- EOCIS Sea-Surface Temperatures V3 2024-11-15
0a2cdd11-d4e1-4806-ab4f-1a66849cf30a --- 2024-11-15T00:00:00Z --- EOCIS Arctic Monthly Gridded Sea Ice Thickness 2024-11-15
f2a5764b-5f1e-4011-a8c2-d1eb75995d3b --- 2024-10-15T00:00:00Z --- EOCIS Sea-Surface Temperatures V3 2024-10-15
c2a3829e-ea0c-4379-be8c-da4f7e0e6c07 --- 2024-10-15T00:00:00Z --- EOCIS Arctic Monthly Gridded Sea Ice Thickness 2024-10-15
f476f3a5-14ca-4055-9b4d-d735946338ff --- 2024-04-15T00:00:00Z --- EOCIS Arctic Monthly Gridded Sea Ice Thickness 2024-04-15
d3ee4814-f522-4bf9-99d6-45bff8018cec --- 2024-04-15T00:00:00Z --- EOCIS Sea-Surface Temperatures V3 2024-04-15
aa5d9ec6-d7f3-4a23-b55b-ee6751bb7170 --- 2024-03-15T00:00:00Z --- EOCIS Arctic Monthly Gridded Sea Ice Thickness 2024-03-15
113583bf-a0d3-499d-9252-ea6ec096abe7 --- 2024-03-15T00:00:00Z --- EOCIS Sea-Surface Temperatures V3 2024-03-15
d9caf30d-ca73-4673-a079-17342019c598 --- 2024-02-14T00:00:00Z --- EOCIS Arctic Monthly Gridded Sea Ice Thickness 2024-02-14
8d23432c-9f46-4ae9-9854-27b880d675f3 --- 2024-02-14T00:00:00Z --- EOCIS Sea-Surface Temperatures V3 2024-02-14

The next thing to do is find out what assets are held within each item. The asset is the actual data that we are interested in. We will look at the asset information for the first item returned in the list.

In [10]:

for item in items[:1]:  # Process only the second item
    print(f"Item ID: {item.id}")
    print(f"Item name: {item.properties["title"]}")
    print("Assets:")
    
    if not item.assets:
        print("  No assets available.")
    else:
        for asset_key, asset in item.assets.items():
            print(f"  - {asset_key}: {asset.to_dict()}")  # Convert asset to dict for readable output
            print("-" * 40)  # Separator for better readability

for item in items[:1]: # Process only the second item print(f"Item ID: {item.id}") print(f"Item name: {item.properties["title"]}") print("Assets:") if not item.assets: print(" No assets available.") else: for asset_key, asset in item.assets.items(): print(f" - {asset_key}: {asset.to_dict()}") # Convert asset to dict for readable output print("-" * 40) # Separator for better readability

Item ID: ea60feff-3a21-49d1-b79f-9179c052cfc0
Item name: EOCIS Sea-Surface Temperatures V3 2024-11-15
Assets:
  - reference_file: {'href': 'https://gws-access.jasmin.ac.uk/public/nceo_uor/eocis-stac/eocis-arctic-sea-ice-thickness-monthly-aux/items/2024/11/EOCIS-SEAICE-L3C-SITHICK-CS2-5KM-202411-fv1.0-kerchunk.json', 'type': 'application/zstd', 'cloud_format': 'kerchunk', 'checksum_type': None, 'open_kwargs': {'open_mapper_kwargs': {}, 'open_zarr_kwargs': {}, 'open_xarray_kwargs': {}}, 'size': None, 'checksum': None, 'roles': ['reference', 'data']}
----------------------------------------
  - EOCIS-SEAICE-L3C-SITHICK-CS2-5KM-202411-fv1.0: {'href': 'https://dap.ceda.ac.uk/neodc/eocis/data/global_and_regional/arctic_sea_ice/arctic_sea_ice_thickness_grids/L3C/monthly/v1.0/EOCIS-SEAICE-L3C-SITHICK-CS2-5KM-202411-fv1.0.nc', 'type': 'application/netcdf', 'roles': ['data']}
----------------------------------------

Now we are ready to load some data and visualise it. To do this we will need to use xarray.

-- Notes --

The data are provided in NetCDF (Network Common Data Form) file format, a widely used standard for storing, sharing, and analyzing scientific data. NetCDF is designed to handle large, multi-dimensional datasets, such as temperature, precipitation, and wind speed, across space and time. The CEDA STAC library also contains reference files in Kerchunk format. Kerchunk is a lightweight reference file format that maps chunks of data in archival formats such as NetCDF that can be lazily loaded to access only the required data chunks for any given request. Rather than converting the source NetCDF to a different format, Kerchunk contains pointers to individual chunks of data and uses the fsspec library to assemble those chunks into the required array in a tool like xarray.

In [18]:

item = items[1]

# --- Identify the kerchunk reference asset if it exists ---
asset = None

if "reference_file" in item.assets:
    asset = item.assets["reference_file"]
    href = asset.href
    is_kerchunk = True
else:
    # Fallback: use the first 'data' asset
    for k, a in item.assets.items():
        if "data" in a.roles or a.type == "application/netcdf":
            asset = a
            href = asset.href
            is_kerchunk = False
            break

print("Selected asset:", asset)

# Load the dataset depending on asset type
if is_kerchunk: # kerchunk: open via fsspec + zarr
    fs = fsspec.filesystem("reference", fo=href)
    mapper = fs.get_mapper("")
    ds = xr.open_dataset(mapper, engine="zarr", consolidated=False)
else:
    # netcdf: direct open file
    ds = xr.open_dataset(href)

print("\n=== DATASET SUMMARY ===")
print(ds)

print("\n=== VARIABLES ===")
print(list(ds.data_vars))

print("\n=== COORDINATES ===")
print(list(ds.coords))

print("\n=== GLOBAL ATTRIBUTES ===")
for k, v in ds.attrs.items():
    print(f"{k}: {v}")

item = items[1] # --- Identify the kerchunk reference asset if it exists --- asset = None if "reference_file" in item.assets: asset = item.assets["reference_file"] href = asset.href is_kerchunk = True else: # Fallback: use the first 'data' asset for k, a in item.assets.items(): if "data" in a.roles or a.type == "application/netcdf": asset = a href = asset.href is_kerchunk = False break print("Selected asset:", asset) # Load the dataset depending on asset type if is_kerchunk: # kerchunk: open via fsspec + zarr fs = fsspec.filesystem("reference", fo=href) mapper = fs.get_mapper("") ds = xr.open_dataset(mapper, engine="zarr", consolidated=False) else: # netcdf: direct open file ds = xr.open_dataset(href) print("\n=== DATASET SUMMARY ===") print(ds) print("\n=== VARIABLES ===") print(list(ds.data_vars)) print("\n=== COORDINATES ===") print(list(ds.coords)) print("\n=== GLOBAL ATTRIBUTES ===") for k, v in ds.attrs.items(): print(f"{k}: {v}")

Selected asset: <Asset href=https://gws-access.jasmin.ac.uk/public/nceo_uor/eocis-stac/eocis-arctic-sea-ice-thickness-monthly-aux/items/2024/11/EOCIS-SEAICE-L3C-SITHICK-CS2-5KM-202411-fv1.0-kerchunk.json>

=== DATASET SUMMARY ===
<xarray.Dataset> Size: 68MB
Dimensions:                   (yc: 2240, xc: 1520, time: 1, nv: 2)
Coordinates:
    lat                       (yc, xc) float32 14MB ...
    lon                       (yc, xc) float32 14MB ...
  * time                      (time) datetime64[ns] 8B 2024-11-15
  * xc                        (xc) float32 6kB -3.85e+06 -3.845e+06 ... 3.75e+06
  * yc                        (yc) float32 9kB -5.35e+06 -5.345e+06 ... 5.85e+06
Dimensions without coordinates: nv
Data variables:
    n_thickness_measurements  (time, yc, xc) int32 14MB ...
    polar_stereographic       float32 4B ...
    sea_ice_thickness         (time, yc, xc) float32 14MB ...
    sea_ice_thickness_stdev   (time, yc, xc) float32 14MB ...
    time_bnds                 (time, nv) datetime64[ns] 16B ...
Attributes: (12/32)
    Conventions:               CF-1.10
    conventions:               CF-1.10
    creator_name:              Andy Ridout, Alan Muir (CPOM, University Colle...
    creator_url:               http://www.cpom.org.uk
    date_created:              20250114T095946Z
    end_day:                   30
    ...                        ...
    start_year:                2024
    time_coverage_duration:    P1M
    time_coverage_end:         20241130T235959Z
    time_coverage_resolution:  P1M
    time_coverage_start:       20241101T000000Z
    title:                     Arctic Monthly Gridded Sea Ice Thickness Produ...

=== VARIABLES ===
['n_thickness_measurements', 'polar_stereographic', 'sea_ice_thickness', 'sea_ice_thickness_stdev', 'time_bnds']

=== COORDINATES ===
['lat', 'lon', 'time', 'xc', 'yc']

=== GLOBAL ATTRIBUTES ===
Conventions: CF-1.10
conventions: CF-1.10
creator_name: Andy Ridout, Alan Muir (CPOM, University College London)
creator_url: http://www.cpom.org.uk
date_created: 20250114T095946Z
end_day: 30
end_month: 11
end_year: 2024
geospatial_lat_max: 88.2284
geospatial_lat_min: 67.1128
geospatial_lon_max: 359.9999
geospatial_lon_min: 0.0002
geospatial_vertical_max: 0.0
geospatial_vertical_min: 0.0
institution: Centre for Polar Observation and Modelling (CPOM), U.K
keywords: Sea Ice, Thickness, Radar Altimeters
license: Creative Commons Attribution 4.0 International (CC BY 4.0)
ndays: 30
platform: CryoSat-2
processing_level: Level-3C
project: EOCIS
sensor: SIRAL
spatial_resolution: 5 km
standard_name_vocabulary: CF
start_day: 1
start_month: 11
start_year: 2024
time_coverage_duration: P1M
time_coverage_end: 20241130T235959Z
time_coverage_resolution: P1M
time_coverage_start: 20241101T000000Z
title: Arctic Monthly Gridded Sea Ice Thickness Product from CryoSat-2

Now we need to reproject and plot

In [12]:

# set the projection
ds3413 = ds.rio.write_crs("EPSG:3413", inplace=False)
sit = ds3413["sea_ice_thickness"].squeeze()

proj = ccrs.Stereographic(central_latitude=90, central_longitude=-45)  # NSIDC default

plt.figure(figsize=(10,10))
ax = plt.axes(projection=proj)

# Transform from EPSG:3413 to map projection
sit.plot(
    ax=ax,
    transform=ccrs.epsg(3413),
    cmap="viridis",
    cbar_kwargs={"label": "Sea Ice Thickness (m)"}
)

ax.coastlines()
ax.set_title("Sea Ice Thickness (EPSG:3413)")
plt.show()

# set the projection ds3413 = ds.rio.write_crs("EPSG:3413", inplace=False) sit = ds3413["sea_ice_thickness"].squeeze() proj = ccrs.Stereographic(central_latitude=90, central_longitude=-45) # NSIDC default plt.figure(figsize=(10,10)) ax = plt.axes(projection=proj) # Transform from EPSG:3413 to map projection sit.plot( ax=ax, transform=ccrs.epsg(3413), cmap="viridis", cbar_kwargs={"label": "Sea Ice Thickness (m)"} ) ax.coastlines() ax.set_title("Sea Ice Thickness (EPSG:3413)") plt.show()

In [13]:

# Transform lon/lat for ocean point to EPSG:3413
to_3413 = Transformer.from_crs("EPSG:4326", "EPSG:3413", always_xy=True)
x0, y0 = to_3413.transform(olon, olat)

# 50 km buffer in metres
aoi_buffer = Point(x0, y0).buffer(50_000)   # 50 km radius

# Transform lon/lat for ocean point to EPSG:3413 to_3413 = Transformer.from_crs("EPSG:4326", "EPSG:3413", always_xy=True) x0, y0 = to_3413.transform(olon, olat) # 50 km buffer in metres aoi_buffer = Point(x0, y0).buffer(50_000) # 50 km radius

In [14]:

# Build GeoDataFrame in EPSG:3413
gdf = gpd.GeoDataFrame(
    {"geometry": [aoi_buffer]},
    crs="EPSG:3413"
)

# Clip to buffer polygon
sit_clip = sit.rio.clip(gdf.geometry, gdf.crs)

# Build GeoDataFrame in EPSG:3413 gdf = gpd.GeoDataFrame( {"geometry": [aoi_buffer]}, crs="EPSG:3413" ) # Clip to buffer polygon sit_clip = sit.rio.clip(gdf.geometry, gdf.crs)

In [15]:

plt.figure(figsize=(8,8))
ax = plt.axes(projection=proj)

sit_clip.plot(
    ax=ax,
    transform=ccrs.epsg(3413),
    cmap="viridis",
    cbar_kwargs={"label": "Sea Ice Thickness (m)"}
)

# Draw AOI point
ax.plot(x0, y0, "ro", markersize=6, transform=ccrs.epsg(3413))

ax.coastlines()
ax.set_title("Sea Ice Thickness – 50 km AOI Buffer (EPSG:3413)")
plt.show()

plt.figure(figsize=(8,8)) ax = plt.axes(projection=proj) sit_clip.plot( ax=ax, transform=ccrs.epsg(3413), cmap="viridis", cbar_kwargs={"label": "Sea Ice Thickness (m)"} ) # Draw AOI point ax.plot(x0, y0, "ro", markersize=6, transform=ccrs.epsg(3413)) ax.coastlines() ax.set_title("Sea Ice Thickness – 50 km AOI Buffer (EPSG:3413)") plt.show()

Sea Surface Temperature¶

We can do something similar for other datasets. We would usually follow the data discovery steps above for each dataset we are interested in. For brevity we can access the href that we require by completing an online search and copying the reference file href into the cell below.

In [16]:

nhref = "https://gws-access.jasmin.ac.uk/public/nceo_uor/eocis-stac/sst-cdrv3-aux/items/2024/06/20240621120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_ICDR3.0-v02.0-fv01.0-kerchunk.json"

# Load the dataset 
fsst = fsspec.filesystem("reference", fo=nhref)
mapper1 = fsst.get_mapper("")
dsst = xr.open_dataset(mapper1, engine="zarr", consolidated=False)

print("\n=== DATASET SUMMARY ===")
print(dsst)

print("\n=== VARIABLES ===")
print(list(dsst.data_vars))

print("\n=== COORDINATES ===")
print(list(dsst.coords))

print("\n=== GLOBAL ATTRIBUTES ===")
for k, v in dsst.attrs.items():
    print(f"{k}: {v}")

nhref = "https://gws-access.jasmin.ac.uk/public/nceo_uor/eocis-stac/sst-cdrv3-aux/items/2024/06/20240621120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_ICDR3.0-v02.0-fv01.0-kerchunk.json" # Load the dataset fsst = fsspec.filesystem("reference", fo=nhref) mapper1 = fsst.get_mapper("") dsst = xr.open_dataset(mapper1, engine="zarr", consolidated=False) print("\n=== DATASET SUMMARY ===") print(dsst) print("\n=== VARIABLES ===") print(list(dsst.data_vars)) print("\n=== COORDINATES ===") print(list(dsst.coords)) print("\n=== GLOBAL ATTRIBUTES ===") for k, v in dsst.attrs.items(): print(f"{k}: {v}")

=== DATASET SUMMARY ===
<xarray.Dataset> Size: 726MB
Dimensions:                   (time: 1, lat: 3600, lon: 7200, bnds: 2)
Coordinates:
  * lat                       (lat) float32 14kB -89.97 -89.93 ... 89.93 89.97
  * lon                       (lon) float32 29kB -180.0 -179.9 ... 179.9 180.0
  * time                      (time) datetime64[ns] 8B 2024-06-21T12:00:00
Dimensions without coordinates: bnds
Data variables:
    analysed_sst              (time, lat, lon) float64 207MB ...
    analysed_sst_uncertainty  (time, lat, lon) float64 207MB ...
    lat_bnds                  (lat, bnds) float32 29kB ...
    lon_bnds                  (lon, bnds) float32 58kB ...
    mask                      (time, lat, lon) float32 104MB ...
    sea_ice_fraction          (time, lat, lon) float64 207MB ...
    time_bnds                 (time, bnds) datetime64[ns] 16B ...
Attributes: (12/66)
    Conventions:                    CF-1.5, Unidata Observation Dataset v1.0
    Metadata_Conventions:           Unidata Dataset Discovery v1.0
    acknowledgment:                 The European Space Agency (ESA) funded th...
    cdm_data_type:                  grid
    comment:                        These data were produced by the Met Offic...
    contact:                        https://climate.esa.int/en/projects/sea-s...
    ...                             ...
    time_coverage_resolution:       P1D
    time_coverage_start:            20240621T000000Z
    title:                          ESA SST CCI Analysis v3.0
    tracking_id:                    ef4779ea-3d9a-42b5-8423-9da05b7b7d7d
    uuid:                           ef4779ea-3d9a-42b5-8423-9da05b7b7d7d
    westernmost_longitude:          -180.0

=== VARIABLES ===
['analysed_sst', 'analysed_sst_uncertainty', 'lat_bnds', 'lon_bnds', 'mask', 'sea_ice_fraction', 'time_bnds']

=== COORDINATES ===
['lat', 'lon', 'time']

=== GLOBAL ATTRIBUTES ===
Conventions: CF-1.5, Unidata Observation Dataset v1.0
Metadata_Conventions: Unidata Dataset Discovery v1.0
acknowledgment: The European Space Agency (ESA) funded the research and development of software to generate these data (grant reference ESA/AO/1-9322/18/I-NB), in addition to funding the production of the Climate Data Record (CDR) for 1980 to 2021. The Copernicus Climate Change Service (C3S) funded the development of the Interim-CDR (ICDR) extension and production of ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Natural Environment Research Council (NERC grant reference number NE/X019071/1, Earth Observation Climate Information Service) and the UK Marine and Climate Advisory Service (UKMCAS), benefitting from the Earth Observation Investment Package of the Department of Science, Innovation and Technology.
cdm_data_type: grid
comment: These data were produced by the Met Office as part of the MCAS project. WARNING Some applications are unable to properly handle signed byte values. If values are encountered > 127, please subtract 256 from this reported value
contact: https://climate.esa.int/en/projects/sea-surface-temperature
contributor_name: JASMIN
contributor_role: This work used JASMIN, the UK's collaborative data analysis environment (https://jasmin.ac.uk)
creation_date: 2024-07-04T15:56:27Z
creator_email: ml-ostia@metoffice.gov.uk
creator_institution: Met Office
creator_name: Met Office
creator_type: institution
creator_url: https://www.metoffice.gov.uk
date_created: 20240704T155627Z
doi: 10.5285/4a9654136a7148e39b7feb56f8bb02d2
easternmost_longitude: 180.00001525878906
file_quality_level: 3
gds_version_id: 2.0
geospatial_lat_max: 90.0
geospatial_lat_min: -90.0
geospatial_lat_resolution: 0.05000000074505806
geospatial_lat_units: degrees_north
geospatial_lon_max: 180.0
geospatial_lon_min: -180.0
geospatial_lon_resolution: 0.05000000074505806
geospatial_lon_units: degrees_east
geospatial_vertical_max: -0.20000000298023224
geospatial_vertical_min: -0.20000000298023224
history: Created using OSTIA reanalysis system ICDR3.0
id: OSTIA-ESACCI-L4-GLOB_ICDR-v3.0
institution: ESACCI
key_variables: analysed_sst,sea_ice_fraction
keywords: Oceans > Ocean Temperature > Sea Surface Temperature
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords
license: Creative Commons Attribution 4.0 https://creativecommons.org/licenses/by/4.0/ 
Users of these data should cite the dataset along with the dataset paper: Embury, O. et al. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data (2024). https://doi.org/10.1038/s41597-024-03147-w
metadata_link: https://doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2
naming_authority: org.ghrsst
netcdf_version_id: 4.3.2
northernmost_latitude: 90.0
platform: MetOpB, Sentinel-3A, Sentinel-3B
processing_level: L4
product_specification_version: SST_CCI-PSD-UKMO-201-Issue-2
product_version: 3.0.1
project: UK Marine and Climate Advisory Service
publisher_email: support@ceda.ac.uk
publisher_name: NERC EDS Centre for Environmental Data Analysis
publisher_type: institution
publisher_url: https://www.ceda.ac.uk
references: Embury, O. et al. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data (2024). https://doi.org/10.1038/s41597-024-03147-w
sensor: AVHRR, SLSTR
source: AVHRRMTB-UKEOCIS-L3U-ICDR-v3.0, SLSTRA-UKEOCIS-L3U-ICDR-v3.0, SLSTRB-UKEOCIS-L3U-ICDR-v3.0, EUMETSAT_OSI-SAF-ICE-OSI-430-a
southernmost_latitude: -90.0
spatial_resolution: 0.05 degree
standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Convention
start_time: 20240621T000000Z
stop_time: 20240622T000000Z
summary: European Space Agency Sea Surface Temperature Climate Change Initiative: Analysis product version 3.0
time_coverage_duration: P1D
time_coverage_end: 20240622T000000Z
time_coverage_resolution: P1D
time_coverage_start: 20240621T000000Z
title: ESA SST CCI Analysis v3.0
tracking_id: ef4779ea-3d9a-42b5-8423-9da05b7b7d7d
uuid: ef4779ea-3d9a-42b5-8423-9da05b7b7d7d
westernmost_longitude: -180.0

In [17]:

# Extract SST variable
sst = dsst["analysed_sst"].squeeze()   # already in EPSG:4326

proj = ccrs.PlateCarree()  # map projection = EPSG:4326

plt.figure(figsize=(10,10))
ax = plt.axes(projection=proj)

sst.plot(
    ax=ax,
    transform=ccrs.PlateCarree(),   # IMPORTANT: matches data CRS
    cmap="viridis",
    cbar_kwargs={"label": "Sea Surface Temperature (°C)"}
)

ax.coastlines()
ax.set_title("Sea Surface Temperature")
plt.show()

# Extract SST variable sst = dsst["analysed_sst"].squeeze() # already in EPSG:4326 proj = ccrs.PlateCarree() # map projection = EPSG:4326 plt.figure(figsize=(10,10)) ax = plt.axes(projection=proj) sst.plot( ax=ax, transform=ccrs.PlateCarree(), # IMPORTANT: matches data CRS cmap="viridis", cbar_kwargs={"label": "Sea Surface Temperature (°C)"} ) ax.coastlines() ax.set_title("Sea Surface Temperature") plt.show()

In [ ]: