Store Affine GeoTransform and CRS in grid_mapping variable

Load example dataset from NetCDF into Xarray

import json

import cf_xarray  # noqa
import panel
import rasterio
import rioxarray  # noqa
import xarray as xr
import zarr

# For zarr_format=2 encoding
from numcodecs import Zstd

fp_base = "20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1"
input = f"../data/{fp_base}.nc"
v2_output = f"../output/v2/{fp_base}_geotransform.zarr"
v3_output = f"../output/v3/{fp_base}_geotransform.zarr"

ds = xr.open_dataset(input)

Check that all variables have a CF-compliant standard name

standard_names = ds.cf.standard_names
vars_with_standard_names = [v[0] for v in ds.cf.standard_names.values()]
compliant_vars = []
non_complaint_vars = []
for var in ds.variables:
    if var not in vars_with_standard_names:
        non_complaint_vars.append(var)
    else:
        compliant_vars.append(var)
        assert ds[var].attrs["standard_name"]

print(f"These variables do NOT have a CF-compliant standard name: {non_complaint_vars}")
print(f"These variables have a CF-compliant standard name: {compliant_vars}")

These variables do NOT have a CF-compliant standard name: ['analysis_error', 'mask']
These variables have a CF-compliant standard name: ['time', 'lat', 'lon', 'analysed_sst', 'sea_ice_fraction']

Not all the variables in this dataset have a CF-compliant standard name. See https://github.com/zarr-developers/geozarr-spec/issues/60 for a recommendation that CF-compliant standard names should be a “SHOULD” rather than a “MUST” condition in the GeoZarr spec. For now, let’s subset to the variables that do use CF-compliant standard names.

ds = ds[compliant_vars]

Assign CRS and geotransform using rioxarray and rasterio

First, let’s specify the CRS for the dataset

ds = ds.rio.write_crs("epsg:4326")

# Specify which variable contains CRS information using grid_mapping
for var in ds.data_vars:
    ds[var].attrs["grid_mapping"] = "spatial_ref"

Next, let’s get the appropriate GeoTransform from the bounds, width, and height of one of the data variables.

length, height, width = ds.analysed_sst.shape
transform = rasterio.transform.from_bounds(*ds.analysed_sst.rio.bounds(), width, height)

Now, let’s store the GeoTransform as a space separated string in the GeoTransform attribute of the spatial_ref auxiliary variable.

# Convert transform to GDAL's format
transform = transform.to_gdal()
# Convert transform to space separated string
transform = " ".join([str(i) for i in transform])
# Save as an attribute in the `spatial_ref` variable
ds["spatial_ref"].attrs["GeoTransform"] = transform

Finally, we’ll remove the explicit coordinates that are redundant with the GeoTransform.

ds = ds.drop_vars(["lat", "lon"])
ds

<xarray.Dataset> Size: 10GB
Dimensions:           (time: 1, lat: 17999, lon: 36000)
Coordinates:
  * time              (time) datetime64[ns] 8B 2002-06-01T09:00:00
    spatial_ref       int64 8B 0
Dimensions without coordinates: lat, lon
Data variables:
    analysed_sst      (time, lat, lon) float64 5GB ...
    sea_ice_fraction  (time, lat, lon) float64 5GB ...
Attributes: (12/47)
    Conventions:                CF-1.5
    title:                      Daily MUR SST, Final product
    summary:                    A merged, multi-sensor L4 Foundation SST anal...
    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...
    institution:                Jet Propulsion Laboratory
    history:                    created at nominal 4-day latency; replaced nr...
    ...                         ...
    project:                    NASA Making Earth Science Data Records for Us...
    publisher_name:             GHRSST Project Office
    publisher_url:              http://www.ghrsst.org
    publisher_email:            [email protected]
    processing_level:           L4
    cdm_data_type:              grid

xarray.Dataset

• Dimensions:
  • time: 1
  • lat: 17999
  • lon: 36000
• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2002-06-01T09:00:00

    long_name :

    reference time of sst field

    standard_name :

    time

    axis :

    T

    comment :

    Nominal time of analyzed fields

    array(['2002-06-01T09:00:00.000000000'], dtype='datetime64[ns]')

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    -179.99500549324037 0.01000000015259213 0.0 89.99499786365084 0.0 -0.009999999762614682

    array(0)

• Data variables: (2)
  • analysed_sst
    (time, lat, lon)
    float64
    ...

    long_name :

    analysed sea surface temperature

    standard_name :

    sea_surface_foundation_temperature

    units :

    kelvin

    valid_min :

    -32767

    valid_max :

    32767

    comment :

    "Final" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation

    source :

    AMSRE-REMSS, AVHRR_Pathfinder-PFV5.2-NODC_day, AVHRR_Pathfinder-PFV5.2-NODC_night, MODIS_T-JPL, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF

    grid_mapping :

    spatial_ref

    [647964000 values with dtype=float64]

  • sea_ice_fraction
    (time, lat, lon)
    float64
    ...

    long_name :

    sea ice area fraction

    standard_name :

    sea ice area fraction

    units :

    fraction (between 0 and 1)

    valid_min :

    0

    valid_max :

    100

    source :

    EUMETSAT OSI-SAF, copyright EUMETSAT

    comment :

    ice data interpolated by a nearest neighbor approach.

    grid_mapping :

    spatial_ref

    [647964000 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-06-01 09:00:00'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (47)

  Conventions :

  CF-1.5

  title :

  Daily MUR SST, Final product

  summary :

  A merged, multi-sensor L4 Foundation SST analysis product from JPL.

  references :

  http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST

  institution :

  Jet Propulsion Laboratory

  history :

  created at nominal 4-day latency; replaced nrt (1-day latency) version.

  comment :

  MUR = "Multi-scale Ultra-high Reolution"

  license :

  These data are available free of charge under data policy of JPL PO.DAAC.

  id :

  MUR-JPL-L4-GLOB-v04.1

  naming_authority :

  org.ghrsst

  product_version :

  04.1

  uuid :

  27665bc0-d5fc-11e1-9b23-0800200c9a66

  gds_version_id :

  2.0

  netcdf_version_id :

  4.1

  date_created :

  20150819T103929Z

  start_time :

  20020601T090000Z

  stop_time :

  20020601T090000Z

  time_coverage_start :

  20020531T210000Z

  time_coverage_end :

  20020601T210000Z

  file_quality_level :

  1

  source :

  AMSRE-REMSS, AVHRR_Pathfinder-PFV5.2-NODC_day, AVHRR_Pathfinder-PFV5.2-NODC_night, MODIS_T-JPL, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF

  platform :

  Aqua, DMSP, NOAA-POES, Suomi-NPP, Terra

  sensor :

  AMSR-E, AVHRR, MODIS, SSM/I, VIIRS, in-situ

  Metadata_Conventions :

  Unidata Observation Dataset v1.0

  metadata_link :

  http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1

  keywords :

  Oceans > Ocean Temperature > Sea Surface Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  southernmost_latitude :

  -90.0

  northernmost_latitude :

  90.0

  westernmost_longitude :

  -180.0

  easternmost_longitude :

  180.0

  spatial_resolution :

  0.01 degrees

  geospatial_lat_units :

  degrees north

  geospatial_lat_resolution :

  0.01 degrees

  geospatial_lon_units :

  degrees east

  geospatial_lon_resolution :

  0.01 degrees

  acknowledgment :

  Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.

  creator_name :

  JPL MUR SST project

  creator_email :

  [email protected]

  creator_url :

  http://mur.jpl.nasa.gov

  project :

  NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program

  publisher_name :

  GHRSST Project Office

  publisher_url :

  http://www.ghrsst.org

  publisher_email :

  [email protected]

  processing_level :

  L4

  cdm_data_type :

  grid

Specify encoding and write to Zarr V2 format

spatial_chunk = 4096
compressor = Zstd(level=1)
encoding = {
    "analysed_sst": {
        "chunks": (1, spatial_chunk, spatial_chunk),
        "compressor": compressor,
    },
    "sea_ice_fraction": {
        "chunks": (1, spatial_chunk, spatial_chunk),
        "compressor": compressor,
    },
}
ds.to_zarr(v2_output, mode="w", consolidated=True, zarr_format=2, encoding=encoding)

<xarray.backends.zarr.ZarrStore at 0x16aacde10>

Inspect Zarr V2 store

First, let’s look at the structure of Zarr arrays using zarr’s Group.tree() method

root = zarr.open_group(v2_output)
root.tree()

/
├── analysed_sst (1, 17999, 36000) float64
├── sea_ice_fraction (1, 17999, 36000) float64
├── spatial_ref () int64
└── time (1,) int32

Second, let’s look at what’s actually recorded in the Zarr metadata using the consolidated metadata at the root of the Zarr store.

In order to match valid JSON, we convert the nan fill_value entries to “nan”.

Key observations

• For each array, metadata is stored under '.zattrs'.
• All arrays contain a .zattrs/standard_name.
• The root group specifies that the metadata follows CF conventions, which should be validated.
• .zattrs/_ARRAY_DIMENSIONS for time contains a list with only the the name of the array, indicating that it is a coordinates variable.
• .zattrs/_ARRAY_DIMENSIONS for spatial_ref contains an empty list, indicating that it is an auxiliary coordinate.
• .zattrs/_ARRAY_DIMENSIONS for analysed_sst, sea_ice_fraction contain a list referring to other arrays, indicating that they are data variables rather than coordinate variables.
• .zattrs/grid_mapping for analysed_sst, sea_ice_fraction is "spatial_ref" indicating that CRS information is included in that auxiliary variable’s metadata.
• spatial_ref/.zattrs contains the OGC WKT for the CRS with a GeoTransform attribute containing a string separated GDAL format Affine GeoTransform
• .zattrs/coordinates for analysed_sst, sea_ice_fraction include lat and lon indicating that the GeoTransform produces those coordinates. The part of the stack that adds this metadata coordinates should be further investigated.

panel.extension()
consolidated_metadata_file = f"{v2_output}/.zmetadata"
with open(consolidated_metadata_file) as f:
    metadata = json.load(f)["metadata"]
metadata["sea_ice_fraction/.zarray"]["fill_value"] = str(
    metadata["sea_ice_fraction/.zarray"]["fill_value"]
)
metadata["analysed_sst/.zarray"]["fill_value"] = str(
    metadata["sea_ice_fraction/.zarray"]["fill_value"]
)
panel.pane.JSON(metadata, name="JSON")

Specify encoding and write to Zarr V3 format

While GeoZarr v0.4 is Zarr V2 specific, let’s write a Zarr V3 store to get an idea about how GeoZarr could be adapted for Zarr format 3.

spatial_chunk = 4096
compressor = zarr.codecs.ZstdCodec(level=1)
encoding = {
    "analysed_sst": {
        "chunks": (1, spatial_chunk, spatial_chunk),
        "compressors": compressor,
    },
    "sea_ice_fraction": {
        "chunks": (1, spatial_chunk, spatial_chunk),
        "compressors": compressor,
    },
}
ds.to_zarr(v3_output, mode="w", consolidated=True, zarr_format=3, encoding=encoding)

/Users/max/Documents/Code/developmentseed/geozarr-examples/.pixi/envs/test/lib/python3.13/site-packages/zarr/api/asynchronous.py:203: UserWarning: Consolidated metadata is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.
  warnings.warn(

<xarray.backends.zarr.ZarrStore at 0x16aacd510>

Key observations

• For each group and array, metadata is stored under the ‘attributes’ key in ‘zarr.json’
• All arrays contain a attributes/standard_name
• The dimensions associated with an array are stored under zarr.json/dimension_names (separately from the attributes) rather than _ARRAY_DIMENSIONS
• the node_type specifies whether the key holds a Zarr Array or a Zarr Group
• The coordinates associated with an array are still specified within the array metadata. Currently this is an Xarray implementation detail rather than a part of the GeoZarr specification.
• The fill_value for sea_ice_fraction and analysed_sst is 0 instead of nan. This is likely an error with the fill value not being explicitly specified.

panel.extension()
consolidated_metadata_file = f"{v3_output}/zarr.json"
with open(consolidated_metadata_file) as f:
    metadata = json.load(f)["consolidated_metadata"]["metadata"]
panel.pane.JSON(metadata, name="JSON")