gdalxarray

The goal of gdalxarray is to integrate GDAL with xarray, especially for the multidimensional API which is still relatively underutilized.

Requirements

Users must install GDAL through their system package manager, conda-forge, or Docker before pip install gdalxarray. There are no wheels on PyPi for GDAL, so we can’t support binary installs. We will have installation recommendations and advisable pathways to use docker etc, and feel free to contact about options by creating issues.

https://github.com/hypertidy/gdalxarray/issues

Todo

apply xarray indexes when relevant in Raster
apply xarray indexes when relevant in Multidim
define stance on representation of “default transform”, and when GCPs, RPCs, or geolocation arrays are present
explore when we need to control driver choice (netcdf and hdf in particular)
explore registering as an xarray backend, via engine = "gdalxarray"

Here’s a basic example:

from gdalxarray import GDALBackendEntrypoint
backend = GDALBackendEntrypoint()
dsn =  "/vsicurl/https://projects.pawsey.org.au/idea-sealevel-glo-phy-l4-nrt-008-046/data.marine.copernicus.eu/SEALEVEL_GLO_PHY_L4_NRT_008_046/cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202506/2025/08/nrt_global_allsat_phy_l4_20250825_20250825.nc"
ds = backend.open_dataset(f'vrt://{dsn}?sd_name=vgos', chunks = {}, multidim = False)
ds1 = backend.open_dataset(dsn, multidim = True, chunks = {})

We have a Raster xarray:

ds

<xarray.Dataset> Size: 17MB
Dimensions:  (x: 2880, y: 1440)
Coordinates:
  * x        (x) float64 23kB -180.0 -179.9 -179.8 -179.6 ... 179.6 179.8 179.9
  * y        (y) float64 12kB 90.0 89.88 89.75 89.62 ... -89.62 -89.75 -89.88
Data variables:
    band_1   (y, x) int32 17MB dask.array<chunksize=(1440, 2880), meta=np.ndarray>
Attributes:
    crs:           GEOGCS["unknown",DATUM["unnamed",SPHEROID["Spheroid",63781...
    geotransform:  (-180.0, 0.125, 0.0, 90.0, 0.0, -0.125)

and a Multidim xarray:

ds1

<xarray.Dataset> Size: 166MB
Dimensions:    (latitude: 1440, nv: 2, longitude: 2880, time: 1)
Coordinates:
  * latitude   (latitude) float32 6kB -89.94 -89.81 -89.69 ... 89.69 89.81 89.94
  * nv         (nv) int32 8B 0 1
  * longitude  (longitude) float32 12kB -179.9 -179.8 -179.7 ... 179.8 179.9
  * time       (time) float32 4B 2.763e+04
Data variables:
    lat_bnds   (latitude, nv) float32 12kB dask.array<chunksize=(1440, 2), meta=np.ndarray>
    lon_bnds   (longitude, nv) float32 23kB dask.array<chunksize=(2880, 2), meta=np.ndarray>
    sla        (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    err_sla    (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    ugosa      (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    err_ugosa  (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    vgosa      (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    err_vgosa  (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    adt        (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    ugos       (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    vgos       (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
    flag_ice   (time, latitude, longitude) int32 17MB dask.array<chunksize=(1, 1440, 2880), meta=np.ndarray>
Attributes: (12/44)
    Conventions:                     CF-1.6
    Metadata_Conventions:            Unidata Dataset Discovery v1.0
    cdm_data_type:                   Grid
    comment:                         Sea Surface Height measured by Altimetry...
    contact:                         servicedesk.cmems@mercator-ocean.eu
    creator_email:                   servicedesk.cmems@mercator-ocean.eu
    ...                              ...
    summary:                         DUACS Near-Real-Time Level-4 sea surface...
    time_coverage_duration:          P1D
    time_coverage_end:               2025-08-25T12:00:00Z
    time_coverage_resolution:        P1D
    time_coverage_start:             2025-08-24T12:00:00Z
    title:                           NRT merged all satellites Global Ocean G...

There’s one variable called ‘band_1’ for the raster:

ds.band_1.isel(x = 0)
# <xarray.DataArray 'band_1' (y: 1440)> Size: 6kB
# dask.array<getitem, shape=(1440,), dtype=int32, chunksize=(1440,), chunktype=numpy.ndarray>
# Coordinates:
#     x        float64 8B -180.0
#   * y        (y) float64 12kB 90.0 89.88 89.75 89.62 ... -89.62 -89.75 -89.88
# Attributes:
#     nodata:   -2147483647.0
#     scale:    0.0001
#     offset:   0.0

we can access actual values

## the raw values for now
ds.band_1.sel(x = 100, y = -50, method = "nearest").values
#> array(441, dtype=int32)

ds1.sla.isel(longitude = 0, latitude = 1000).values
#> array([2404], dtype=int32)

Open ECMWF AIFS Single forecast (Icechunk on S3) with gdalxarray

Note this requires the in-dev rouault/icechunk branch (as of 2026-06-12).

import os
os.environ["AWS_NO_SIGN_REQUEST"] =  "YES"
os.environ["AWS_REGION"] =  "us-west-2"

import gdalxarray
from gdalxarray import GDALBackendEntrypoint

backend = GDALBackendEntrypoint()
xds = backend.open_dataset(
    "/vsis3/dynamical-ecmwf-aifs-single/ecmwf-aifs-single-forecast/v0.1.0.icechunk",
    multidim=True,
)

print(xds.encoding.get("gdal_dataset").GetDriver().GetDescription())
# 'Icechunk'

print(xds)

<xarray.Dataset> Size: 14TB
Dimensions:                                     (init_time: 3205,
                                                 latitude: 721, lead_time: 61,
                                                 longitude: 1440)
Coordinates:
  * init_time                                   (init_time) datetime64[ns] 26kB ...
  * latitude                                    (latitude) float64 6kB 90.0 ....
  * lead_time                                   (lead_time) int64 488B 0 ... ...
  * longitude                                   (longitude) float64 12kB -180...
Data variables: (12/21)
    dew_point_temperature_2m                    (init_time, lead_time, latitude, longitude) float32 812GB ...
    downward_long_wave_radiation_flux_surface   (init_time, lead_time, latitude, longitude) float32 812GB ...
    downward_short_wave_radiation_flux_surface  (init_time, lead_time, latitude, longitude) float32 812GB ...
    expected_forecast_length                    (init_time) float32 13kB ...
    geopotential_height_500hpa                  (init_time, lead_time, latitude, longitude) float32 812GB ...
    geopotential_height_850hpa                  (init_time, lead_time, latitude, longitude) float32 812GB ...
    ...                                          ...
    total_cloud_cover_atmosphere                (init_time, lead_time, latitude, longitude) float32 812GB ...
    valid_time                                  (init_time, lead_time) float32 782kB ...
    wind_u_100m                                 (init_time, lead_time, latitude, longitude) float32 812GB ...
    wind_u_10m                                  (init_time, lead_time, latitude, longitude) float32 812GB ...
    wind_v_100m                                 (init_time, lead_time, latitude, longitude) float32 812GB ...
    wind_v_10m                                  (init_time, lead_time, latitude, longitude) float32 812GB ...
Attributes:
    attribution:          ECMWF AIFS Single forecast data processed by dynami...
    dataset_id:           ecmwf-aifs-single-forecast
    dataset_version:      0.1.0
    description:          Weather forecasts from the ECMWF Artificial Intelli...
    forecast_domain:      Forecast lead time 0-360 hours (0-15 days) ahead
    forecast_resolution:  6 hourly
    license:              CC-BY-4.0
    name:                 ECMWF AIFS Single forecast
    spatial_domain:       Global
    spatial_resolution:   0.25 degrees (~20km)
    time_domain:          Forecasts initialized 2024-04-01 00:00:00 UTC to Pr...
    time_resolution:      Forecasts initialized every 6 hours

ZARR from CMEMS

from gdalxarray import GDALBackendEntrypoint
dsn = 'ZARR:"/vsicurl/https://s3.waw3-1.cloudferro.com/mdl-arco-time-045/arco/SEALEVEL_GLO_PHY_L4_MY_008_047/cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411/timeChunked.zarr"'
backend = GDALBackendEntrypoint()
#ds = backend.open_dataset(filename_or_obj, multidim = True, chunks = None)
ds = backend.open_dataset(dsn, multidim = True, chunks = {})
ds

# <xarray.Dataset> Size: 2TB
# Dimensions:         (latitude: 1440, longitude: 2880, time: 11902, nv: 2)
# Coordinates:
#   * latitude        (latitude) float32 6kB -89.94 -89.81 -89.69 ... 89.81 89.94
#   * longitude       (longitude) float32 12kB -179.9 -179.8 ... 179.8 179.9
#   * time            (time) float32 48kB 1.571e+04 1.571e+04 ... 2.761e+04
#   * nv              (nv) int32 8B 0 1
# Data variables: (12/13)
#     adt             (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     err_sla         (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     err_ugosa       (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     err_vgosa       (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     flag_ice        (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     lat_bnds        (latitude, nv) float32 12kB dask.array<chunksize=(1440, 2), meta=np.ndarray>
#     ...              ...
#     sla             (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     tpa_correction  (time) int32 48kB dask.array<chunksize=(1,), meta=np.ndarray>
#     ugos            (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     ugosa           (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     vgos            (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
#     vgosa           (time, latitude, longitude) int32 197GB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
# Attributes: (12/43)
#     Conventions:                     CF-1.6
#     Metadata_Conventions:            Unidata Dataset Discovery v1.0
#     cdm_data_type:                   Grid
#     comment:                         Sea Surface Height measured by Altimetry...
#     contact:                         servicedesk.cmems@mercator-ocean.eu
#     coordinates:                     lat_bnds lon_bnds
#     ...                              ...
#     summary:                         SSALTO/DUACS Delayed-Time Level-4 sea su...
#     time_coverage_duration:          P1D
#     time_coverage_end:               2023-12-31T12:00:00Z
#     time_coverage_resolution:        P1D
#     time_coverage_start:             2023-12-30T12:00:00Z
#     title:                           DT merged all satellites Global Ocean Gr...

This example is a virtualized mosaic of NetCDF in multidim VRT.

big_virtual_mdim = "/vsicurl/https://gist.githubusercontent.com/mdsumner/18c5d302d00b9a456bb73d30ac758764/raw/f26e1b2e202f759d6aace4d7deb3e04ea3c85f15/mdim.vrt"

bvm = backend.open_dataset(big_virtual_mdim, multidim = True, chunks = {})
# <xarray.Dataset> Size: 3TB
# Dimensions:   (Time: 5479, st_ocean: 51, yt_ocean: 1500, xt_ocean: 3600)
# Coordinates:
#   * Time      (Time) float64 44kB 1.132e+04 1.132e+04 ... 1.68e+04 1.68e+04
#   * st_ocean  (st_ocean) float64 408B 2.5 7.5 12.5 ... 3.603e+03 4.509e+03
#   * yt_ocean  (yt_ocean) float64 12kB -74.95 -74.85 -74.75 ... 74.75 74.85 74.95
#   * xt_ocean  (xt_ocean) float64 29kB 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0
# Data variables:
#     temp      (Time, st_ocean, yt_ocean, xt_ocean) int16 3TB dask.array<chunksize=(5479, 51, 1500, 3600), meta=np.ndarray>
    

bvm.sel(xt_ocean = slice(140, 150), yt_ocean = slice(-55, -45), st_ocean = slice(8, 13)).isel(Time = -1).temp.values

# array([[[-30770, -30784, -30799, ..., -30418, -30424, -30445],
#         [-30755, -30771, -30788, ..., -30418, -30425, -30446],
#         [-30744, -30764, -30788, ..., -30417, -30426, -30448],
#         ...,
#         [-29852, -29868, -29889, ..., -29413, -29338, -29325],
#         [-29835, -29851, -29883, ..., -29385, -29327, -29324],
#         [-29821, -29840, -29879, ..., -29353, -29319, -29322]]],
#       shape=(1, 100, 100), dtype=int16)

A grib example

What a joy to simply be able to use GDAL for what it is good at without intermediate layers.

from gdalxarray import GDALBackendEntrypoint
backend = GDALBackendEntrypoint()
backend.open_dataset("/vsicurl/https://noaa-nbm-grib2-pds.s3.amazonaws.com/blend.20251031/16/core/blend.t16z.core.f260.co.grib2", multidim = False, chunks = None)

<xarray.Dataset> Size: 989MB
Dimensions:                                                                           (
                                                                                       y: 1597,
                                                                                       x: 2345)
Coordinates:
  * x                                                                                 (x) float64 19kB ...
  * y                                                                                 (y) float64 13kB ...
  * crs                                                                               int64 8B ...
Data variables: (12/33)
    APTMP:2 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
    CAPE:surface:260 hour fcst                                                        (y, x) float64 30MB ...
    CWASP:surface:260 hour fcst                                                       (y, x) float64 30MB ...
    DPT:2 m above ground:260 hour fcst                                                (y, x) float64 30MB ...
    DSWRF:surface:260 hour fcst                                                       (y, x) float64 30MB ...
    FICEAC:surface:254-260 hour acc fcst                                              (y, x) float64 30MB ...
    ...                                                                                ...
    WDIR:80 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
    WDIR:10 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
    GUST:10 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
    WIND:30 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
    WIND:80 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
    WIND:10 m above ground:260 hour fcst                                              (y, x) float64 30MB ...
Indexes:
  ┌ x        RasterIndex
  └ y
    crs      CRSIndex (crs=PROJCS["unnamed",GEOGCS["Coordinate System imported from GRIB file" ...)

Run in docker

We have a pre-defined docker image that is ready to build/install gdalxarray.

docker run --rm -it ghcr.io/hypertidy/gdal-r-python:latest bash
# inside the container:
pip install gdalxarray   # (once on PyPI)
# or for development:
pip install -e /path/to/gdalxarray

(Add --security-opt seccomp=unconfined to docker run if you need remote NetCDF).

There’s a lot more to do, scaling works but I turned that off to test for now. .

Template a list of netcdf files and mosaic them to VRT, then open with this xarray backend. (Note this requires GDAL>=3.12.0 ).

month = "202501"
url = [f"/vsicurl/https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/{month}/oisst-avhrr-v02r01.{month}{(day+1):02d}.nc" for day in range(31)]
gdal.Run("mdim mosaic", input = url, output =  "oisst.vrt", array = "sst")
from gdalxarray import GDALBackendEntrypoint
backend = GDALBackendEntrypoint()

backend.open_dataset("oisst.vrt", multidim = True)

# <xarray.Dataset> Size: 64MB
# Dimensions:  (lat: 720, lon: 1440, time: 31, zlev: 1)
# Coordinates:
#   * lat      (lat) float64 6kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88
#   * lon      (lon) float64 12kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9
#   * time     (time) float64 248B 1.717e+04 1.72e+04 ... 1.717e+04 1.717e+04
#   * zlev     (zlev) float64 8B 0.0
# Data variables:
#     sst      (time, zlev, lat, lon) int16 64MB ...
#

Debugging

Debug messages are sprinkled here and there, we might do more work to be more systematic here.

import logging
logging.basicConfig()                                    # installs root handler at WARNING
logging.getLogger("gdalxarray").setLevel(logging.DEBUG)  # let gdalxarray's DEBUG through

Code of Conduct

Please note that the gdalxarray project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Name		Name	Last commit message	Last commit date
Latest commit History 44 Commits
docs		docs
gdalxarray		gdalxarray
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
INSTALL.md		INSTALL.md
LICENSE		LICENSE
README.Rmd		README.Rmd
README.md		README.md
environment.yml		environment.yml
pyproject.toml		pyproject.toml

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gdalxarray

Requirements

Todo

Open ECMWF AIFS Single forecast (Icechunk on S3) with gdalxarray

ZARR from CMEMS

A grib example

Run in docker

Debugging

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gdalxarray

Requirements

Todo

Open ECMWF AIFS Single forecast (Icechunk on S3) with gdalxarray

ZARR from CMEMS

A grib example

Run in docker

Debugging

Code of Conduct

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