Xarray engine: step range

Get input GRIB2 data containing precipitation forecast for step ranges.

[1]:

import earthkit.data as ekd
ds_fl = ekd.from_source("sample", "lsp_step_range.grib2")
ds_fl.ls(keys=["param", "step", "stepRange", "startStep", "endStep"])

[1]:

	param	step	stepRange	startStep	endStep
0	lsp	71-72	71-72	71	72
1	lsp	72-73	72-73	72	73

When we convert GRIB data to Xarray with to_xarray() the step dimension is defined by the “step” dimension role. By default, this role is using the “step_timedelta” generated metadata key that is the timedelta representation of the “endStep” GRIB key.

[2]:

ds = ds_fl.to_xarray()
ds

[2]:

<xarray.Dataset> Size: 2kB
Dimensions:    (step: 2, latitude: 7, longitude: 12)
Coordinates:
  * step       (step) timedelta64[ns] 16B 3 days 3 days 01:00:00
  * latitude   (latitude) float64 56B 90.0 60.0 30.0 0.0 -30.0 -60.0 -90.0
  * longitude  (longitude) float64 96B 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
Data variables:
    lsp        (step, latitude, longitude) float64 1kB ...
Attributes:
    param:        lsp
    paramId:      142
    class:        d1
    stream:       oper
    levtype:      sfc
    type:         fc
    expver:       0001
    date:         20250527
    time:         0
    domain:       g
    Conventions:  CF-1.8
    institution:  ECMWF

We can check the “step” coordinate in the dataset to see that it matches the “endStep” values.

[3]:

# convert to hours from ns
[int(x* 1E-9/(3600)) for x in ds["step"].values]

[3]:

[72, 73]

This default behaviour can be overridden by specifying custom dim_roles. E.g. to get the step from the “startStep” key we can use:

[4]:

ds = ds_fl.to_xarray(dim_roles={"step": "startStep"})
[int(x* 1E-9/(3600)) for x in ds["step"].values]

[4]:

[71, 72]

[ ]: