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cfgrib: A Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes

Python interface to map GRIB files to the Unidata's Common Data Model v4 following the CF Conventions. The high level API is designed to support a GRIB engine for xarray and it is inspired by netCDF4-python and h5netcdf. Low level access and decoding is performed via the ECMWF ecCodes library and the eccodes python package.

Features with development status Beta:

  • enables the engine='cfgrib' option to read GRIB files with xarray,
  • reads most GRIB 1 and 2 files including heterogeneous ones with cfgrib.open_datasets,
  • supports all modern versions of Python 3.9, 3.8, 3.7 and PyPy3,
  • the 0.9.6.x series with support for Python 2 will stay active and receive critical bugfixes,
  • works wherever eccodes-python does: Linux, MacOS and Windows
  • conda-forge package on all supported platforms,
  • reads the data lazily and efficiently in terms of both memory usage and disk access,
  • allows larger-than-memory and distributed processing via xarray and dask,
  • supports translating coordinates to different data models and naming conventions,
  • supports writing the index of a GRIB file to disk, to save a full-file scan on open.

Work in progress:

  • Beta install a cfgrib utility that can convert a GRIB file to_netcdf with a optional conversion to a specific coordinates data model, see #40.
  • Alpha/Broken support writing carefully-crafted xarray.Dataset's to a GRIB1 or GRIB2 file, see the Advanced write usage section below, #18 and #156.

Limitations:

  • relies on ecCodes for the CF attributes of the data variables,
  • relies on ecCodes for anything related to coordinate systems / gridType, see #28.

Installation

The easiest way to install cfgrib and all its binary dependencies is via Conda:

$ conda install -c conda-forge cfgrib

alternatively, if you install the binary dependencies yourself, you can install the Python package from PyPI with:

$ pip install cfgrib

Binary dependencies

cfgrib depends on the eccodes python package to access the ECMWF ecCodes binary library, when not using conda please follow the System dependencies section there.

You may run a simple selfcheck command to ensure that your system is set up correctly:

$ python -m cfgrib selfcheck
Found: ecCodes v2.20.0.
Your system is ready.

Usage

First, you need a well-formed GRIB file, if you don't have one at hand you can download our ERA5 on pressure levels sample:

$ wget http://download.ecmwf.int/test-data/cfgrib/era5-levels-members.grib

Read-only xarray GRIB engine

Most of cfgrib users want to open a GRIB file as a xarray.Dataset and need to have xarray installed:

$ pip install xarray

In a Python interpreter try:

>>> import xarray as xr
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> ds
<xarray.Dataset>
Dimensions:        (number: 10, time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)
Coordinates:
  * number         (number) int64 0 1 2 3 4 5 6 7 8 9
  * time           (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
    step           timedelta64[ns] ...
  * isobaricInhPa  (isobaricInhPa) float64 850.0 500.0
  * latitude       (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
  * longitude      (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
    valid_time     (time) datetime64[ns] ...
Data variables:
    z              (number, time, isobaricInhPa, latitude, longitude) float32 ...
    t              (number, time, isobaricInhPa, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            1
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...

The cfgrib engine supports all read-only features of xarray like:

  • merge the content of several GRIB files into a single dataset using xarray.open_mfdataset,
  • work with larger-than-memory datasets with dask,
  • allow distributed processing with dask.distributed.

Read arbitrary GRIB keys

By default cfgrib reads a limited set of ecCodes recognised keys from the GRIB files and exposes them as Dataset or DataArray attributes with the GRIB_ prefix. It is possible to have cfgrib read additional keys to the attributes by adding the read_keys dictionary key to the backend_kwargs with values the list of desired GRIB keys:

>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib',
...                      backend_kwargs={'read_keys': ['experimentVersionNumber']})
>>> ds.t.attrs['GRIB_experimentVersionNumber']
'0001'

Translate to a custom data model

Contrary to netCDF the GRIB data format is not self-describing and several details of the mapping to the Unidata Common Data Model are arbitrarily set by the software components decoding the format. Details like names and units of the coordinates are particularly important because xarray broadcast and selection rules depend on them. cf2cfm is a small coordinate translation module distributed with cfgrib that make it easy to translate CF compliant coordinates, like the one provided by cfgrib, to a user-defined custom data model with set out_name, units and stored_direction.

For example to translate a cfgrib styled xr.Dataset to the classic ECMWF coordinate naming conventions you can:

>>> import cf2cdm
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> cf2cdm.translate_coords(ds, cf2cdm.ECMWF)
<xarray.Dataset>
Dimensions:     (number: 10, time: 4, level: 2, latitude: 61, longitude: 120)
Coordinates:
  * number      (number) int64 0 1 2 3 4 5 6 7 8 9
  * time        (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
    step        timedelta64[ns] ...
  * level       (level) float64 850.0 500.0
  * latitude    (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
  * longitude   (longitude) float64 0.0 3.0 6.0 9.0 ... 348.0 351.0 354.0 357.0
    valid_time  (time) datetime64[ns] ...
Data variables:
    z           (number, time, level, latitude, longitude) float32 ...
    t           (number, time, level, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            1
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...

To translate to the Common Data Model of the Climate Data Store use:

>>> import cf2cdm
>>> cf2cdm.translate_coords(ds, cf2cdm.CDS)
<xarray.Dataset>
Dimensions:                  (realization: 10, forecast_reference_time: 4, plev: 2, lat: 61, lon: 120)
Coordinates:
  * realization              (realization) int64 0 1 2 3 4 5 6 7 8 9
  * forecast_reference_time  (forecast_reference_time) datetime64[ns] 2017-01...
    leadtime                 timedelta64[ns] ...
  * plev                     (plev) float64 8.5e+04 5e+04
  * lat                      (lat) float64 -90.0 -87.0 -84.0 ... 84.0 87.0 90.0
  * lon                      (lon) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
    time                     (forecast_reference_time) datetime64[ns] ...
Data variables:
    z                        (realization, forecast_reference_time, plev, lat, lon) float32 ...
    t                        (realization, forecast_reference_time, plev, lat, lon) float32 ...
Attributes:
    GRIB_edition:            1
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...

Filter heterogeneous GRIB files

xr.open_dataset can open a GRIB file only if all the messages with the same shortName can be represented as a single hypercube. For example, a variable t cannot have both isobaricInhPa and hybrid typeOfLevel's, as this would result in multiple hypercubes for the same variable. Opening a non-conformant GRIB file will fail with a ValueError: multiple values for unique key... error message, see #2.

Furthermore if different variables depend on the same coordinate, for example step, the values of the coordinate must match exactly. For example, if variables t and z share the same step coordinate, they must both have exactly the same set of steps. Opening a non-conformant GRIB file will fail with a ValueError: key present and new value is different... error message, see #13.

In most cases you can handle complex GRIB files containing heterogeneous messages by passing the filter_by_keys key in backend_kwargs to select which GRIB messages belong to a well formed set of hypercubes.

For example to open US National Weather Service complex GRIB2 files you can use:

>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',
...     backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}})
<xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] ...
    step        timedelta64[ns] ...
    surface     float64 ...
    latitude    (y, x) float64 ...
    longitude   (y, x) float64 ...
    valid_time  datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    gust        (y, x) float32 ...
    sp          (y, x) float32 ...
    orog        (y, x) float32 ...
    tp          (y, x) float32 ...
    acpcp       (y, x) float32 ...
    csnow       (y, x) float32 ...
    cicep       (y, x) float32 ...
    cfrzr       (y, x) float32 ...
    crain       (y, x) float32 ...
    cape        (y, x) float32 ...
    cin         (y, x) float32 ...
    hpbl        (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ...
>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',
...     backend_kwargs={'filter_by_keys': {'typeOfLevel': 'heightAboveGround', 'level': 2}})
<xarray.Dataset>
Dimensions:            (y: 65, x: 93)
Coordinates:
    time               datetime64[ns] ...
    step               timedelta64[ns] ...
    heightAboveGround  float64 ...
    latitude           (y, x) float64 ...
    longitude          (y, x) float64 ...
    valid_time         datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    t2m                (y, x) float32 ...
    r2                 (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ...

Automatic filtering

cfgrib also provides a function that automate the selection of appropriate filter_by_keys and returns a list of all valid xarray.Dataset's in the GRIB file.

>>> import cfgrib
>>> cfgrib.open_datasets('nam.t00z.awp21100.tm00.grib2')
[<xarray.Dataset>
Dimensions:                (y: 65, x: 93)
Coordinates:
    time                   datetime64[ns] 2018-09-17
    step                   timedelta64[ns] 00:00:00
    atmosphereSingleLayer  float64 0.0
    latitude               (y, x) float64 ...
    longitude              (y, x) float64 ...
    valid_time             datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    pwat                   (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    cloudBase   float64 0.0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    pres        (y, x) float32 ...
    gh          (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    cloudTop    float64 0.0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    pres        (y, x) float32 ...
    t           (y, x) float32 ...
    gh          (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:            (y: 65, x: 93)
Coordinates:
    time               datetime64[ns] 2018-09-17
    step               timedelta64[ns] 00:00:00
    heightAboveGround  float64 10.0
    latitude           (y, x) float64 ...
    longitude          (y, x) float64 ...
    valid_time         datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    u10                (y, x) float32 ...
    v10                (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:            (y: 65, x: 93)
Coordinates:
    time               datetime64[ns] 2018-09-17
    step               timedelta64[ns] 00:00:00
    heightAboveGround  float64 2.0
    latitude           (y, x) float64 12.19 12.39 12.58 ... 57.68 57.49 57.29
    longitude          (y, x) float64 226.5 227.2 227.9 ... 308.5 309.6 310.6
    valid_time         datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    t2m                (y, x) float32 ...
    r2                 (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:                 (heightAboveGroundLayer: 2, y: 65, x: 93)
Coordinates:
    time                    datetime64[ns] 2018-09-17
    step                    timedelta64[ns] 00:00:00
  * heightAboveGroundLayer  (heightAboveGroundLayer) float64 1e+03 3e+03
    latitude                (y, x) float64 ...
    longitude               (y, x) float64 ...
    valid_time              datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    hlcy                    (heightAboveGroundLayer, y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:        (isobaricInhPa: 19, y: 65, x: 93)
Coordinates:
    time           datetime64[ns] 2018-09-17
    step           timedelta64[ns] 00:00:00
  * isobaricInhPa  (isobaricInhPa) float64 1e+03 950.0 900.0 ... 150.0 100.0
    latitude       (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude      (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time     datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    t              (isobaricInhPa, y, x) float32 ...
    u              (isobaricInhPa, y, x) float32 ...
    v              (isobaricInhPa, y, x) float32 ...
    w              (isobaricInhPa, y, x) float32 ...
    gh             (isobaricInhPa, y, x) float32 ...
    r              (isobaricInhPa, y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:        (isobaricInhPa: 5, y: 65, x: 93)
Coordinates:
    time           datetime64[ns] 2018-09-17
    step           timedelta64[ns] 00:00:00
  * isobaricInhPa  (isobaricInhPa) float64 1e+03 850.0 700.0 500.0 250.0
    latitude       (y, x) float64 ...
    longitude      (y, x) float64 ...
    valid_time     datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    absv           (isobaricInhPa, y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:       (y: 65, x: 93)
Coordinates:
    time          datetime64[ns] 2018-09-17
    step          timedelta64[ns] 00:00:00
    isothermZero  float64 0.0
    latitude      (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude     (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time    datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    gh            (y, x) float32 ...
    r             (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    maxWind     float64 0.0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    pres        (y, x) float32 ...
    u           (y, x) float32 ...
    v           (y, x) float32 ...
    gh          (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    meanSea     float64 0.0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    prmsl       (y, x) float32 ...
    mslet       (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:                  (pressureFromGroundLayer: 2, y: 65, x: 93)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
  * pressureFromGroundLayer  (pressureFromGroundLayer) float64 9e+03 1.8e+04
    latitude                 (y, x) float64 12.19 12.39 12.58 ... 57.49 57.29
    longitude                (y, x) float64 226.5 227.2 227.9 ... 309.6 310.6
    valid_time               datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    cape                     (pressureFromGroundLayer, y, x) float32 ...
    cin                      (pressureFromGroundLayer, y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:                  (pressureFromGroundLayer: 5, y: 65, x: 93)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
  * pressureFromGroundLayer  (pressureFromGroundLayer) float64 3e+03 ... 1.5e+04
    latitude                 (y, x) float64 12.19 12.39 12.58 ... 57.49 57.29
    longitude                (y, x) float64 226.5 227.2 227.9 ... 309.6 310.6
    valid_time               datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    t                        (pressureFromGroundLayer, y, x) float32 ...
    u                        (pressureFromGroundLayer, y, x) float32 ...
    v                        (pressureFromGroundLayer, y, x) float32 ...
    r                        (pressureFromGroundLayer, y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:                  (y: 65, x: 93)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
    pressureFromGroundLayer  float64 3e+03
    latitude                 (y, x) float64 ...
    longitude                (y, x) float64 ...
    valid_time               datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    pli                      (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:                  (y: 65, x: 93)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
    pressureFromGroundLayer  float64 1.8e+04
    latitude                 (y, x) float64 ...
    longitude                (y, x) float64 ...
    valid_time               datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
    4lftx                    (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    surface     float64 0.0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    cape        (y, x) float32 ...
    sp          (y, x) float32 ...
    acpcp       (y, x) float32 ...
    cin         (y, x) float32 ...
    orog        (y, x) float32 ...
    tp          (y, x) float32 ...
    crain       (y, x) float32 ...
    cfrzr       (y, x) float32 ...
    cicep       (y, x) float32 ...
    csnow       (y, x) float32 ...
    gust        (y, x) float32 ...
    hpbl        (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP , <xarray.Dataset>
Dimensions:     (y: 65, x: 93)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    tropopause  float64 0.0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: y, x
Data variables:
    t           (y, x) float32 ...
    u           (y, x) float32 ...
    v           (y, x) float32 ...
    trpp        (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP ]

Advanced usage

Write support

Please note that write support is Alpha. Only xarray.Dataset's in canonical form, that is, with the coordinates names matching exactly the cfgrib coordinates, can be saved at the moment:

>>> from cfgrib.xarray_to_grib import to_grib
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> ds
<xarray.Dataset>
Dimensions:        (number: 10, time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)
Coordinates:
  * number         (number) int64 0 1 2 3 4 5 6 7 8 9
  * time           (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
    step           timedelta64[ns] ...
  * isobaricInhPa  (isobaricInhPa) float64 850.0 500.0
  * latitude       (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
  * longitude      (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
    valid_time     (time) datetime64[ns] ...
Data variables:
    z              (number, time, isobaricInhPa, latitude, longitude) float32 ...
    t              (number, time, isobaricInhPa, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            1
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...
>>> to_grib(ds, 'out1.grib', grib_keys={'edition': 2})
>>> xr.open_dataset('out1.grib', engine='cfgrib')
<xarray.Dataset>
Dimensions:        (number: 10, time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)
Coordinates:
  * number         (number) int64 0 1 2 3 4 5 6 7 8 9
  * time           (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
    step           timedelta64[ns] ...
  * isobaricInhPa  (isobaricInhPa) float64 850.0 500.0
  * latitude       (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
  * longitude      (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
    valid_time     (time) datetime64[ns] ...
Data variables:
    z              (number, time, isobaricInhPa, latitude, longitude) float32 ...
    t              (number, time, isobaricInhPa, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...

Per-variable GRIB keys can be set by setting the attrs variable with key prefixed by GRIB_, for example:

>>> import numpy as np
>>> import xarray as xr
>>> ds2 = xr.DataArray(
...     np.zeros((5, 6)) + 300.,
...     coords=[
...         np.linspace(90., -90., 5),
...         np.linspace(0., 360., 6, endpoint=False),
...     ],
...     dims=['latitude', 'longitude'],
... ).to_dataset(name='skin_temperature')
>>> ds2.skin_temperature.attrs['GRIB_shortName'] = 'skt'
>>> to_grib(ds2, 'out2.grib')
>>> xr.open_dataset('out2.grib', engine='cfgrib')
<xarray.Dataset>
Dimensions:     (latitude: 5, longitude: 6)
Coordinates:
    time        datetime64[ns] ...
    step        timedelta64[ns] ...
    surface     float64 ...
  * latitude    (latitude) float64 90.0 45.0 0.0 -45.0 -90.0
  * longitude   (longitude) float64 0.0 60.0 120.0 180.0 240.0 300.0
    valid_time  datetime64[ns] ...
Data variables:
    skt         (latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             consensus
    GRIB_centreDescription:  Consensus
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             Consensus
    history:                 ...

Dataset / Variable API

The use of xarray is not mandatory and you can access the content of a GRIB file as an hypercube with the high level API in a Python interpreter:

>>> ds = cfgrib.open_file('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133
>>> ds = cfgrib.open_file('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133

GRIB index file

By default cfgrib saves the index of the GRIB file to disk appending .idx to the GRIB file name. Index files are an experimental and completely optional feature, feel free to remove them and try again in case of problems. Index files saving can be disable passing adding indexpath='' to the backend_kwargs keyword argument.

Project resources

Development https://github.com/ecmwf/cfgrib
Download https://pypi.org/project/cfgrib
User support https://stackoverflow.com/search?q=cfgrib
Code quality Coverage status on Codecov

Contributing

The main repository is hosted on GitHub, testing, bug reports and contributions are highly welcomed and appreciated:

https://github.com/ecmwf/cfgrib

Please see the CONTRIBUTING.rst document for the best way to help.

Lead developer:

Main contributors:

See also the list of contributors who participated in this project.

License

Copyright 2017-2021 European Centre for Medium-Range Weather Forecasts (ECMWF).

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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A Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes

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