Example for using concat operator on cmip6 decadal without average¶

Rooki calls climate data operations on the rook processing service.

In [1]:

import os
os.environ['ROOK_URL'] = 'http://rook.dkrz.de/wps'

from rooki import operators as ops

Check attributes of original decadal dataset¶

In [2]:

wf = ops.Subset(
        ops.Input(
                'tas', [
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r5i1p1f2.Amon.tas.gn.v20200417'
                ]
        ),
        time="1996-01-01/1996-01-31"
)

resp = wf.orchestrate()
resp.ok

Out[2]:

True

In [3]:

dsets = resp.datasets()

Downloading to /var/folders/qb/mg0csz190wd4rxybhhnwjln80000gn/T/metalink_ilqtp6k6/tas_Amon_HadGEM3-GC31-MM_dcppA-hindcast_r5i1p1f2_gn_19960116-19960116.nc.

In [4]:

ds = dsets[0]
ds

Out[4]:

<xarray.Dataset>
Dimensions:    (time: 1, bnds: 2, lat: 324, lon: 432)
Coordinates:
  * time       (time) object 1996-01-16 00:00:00
  * lat        (lat) float64 -89.72 -89.17 -88.61 -88.06 ... 88.61 89.17 89.72
  * lon        (lon) float64 0.4167 1.25 2.083 2.917 ... 357.1 357.9 358.7 359.6
    height     float64 ...
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) object ...
    lat_bnds   (lat, bnds) float64 ...
    lon_bnds   (lon, bnds) float64 ...
    tas        (time, lat, lon) float32 ...
Attributes: (12/43)
    Conventions:            CF-1.7 CMIP-6.2
    activity_id:            DCPP
    branch_method:          no parent
    branch_time_in_child:   0.0
    branch_time_in_parent:  0.0
    creation_date:          2020-05-26T09:33:34Z
    ...                     ...
    tracking_id:            hdl:21.14100/2b29c580-0356-4ced-bf2f-2a6554d4027c
    variable_id:            tas
    variable_name:          tas
    variant_label:          r5i1p1f2
    license:                CMIP6 model data produced by Met Office Hadley Ce...
    cmor_version:           3.4.0

In [5]:

ds.attrs.get('realization_index')

Out[5]:

Run concat¶

https://docs.xarray.dev/en/stable/generated/xarray.concat.html

In [6]:

wf = ops.Concat(
        ops.Input(
                'tas', [
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r1i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r2i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r3i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r4i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r5i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r6i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r7i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r8i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r9i1p1f2.Amon.tas.gn.v20200417',
                    'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r10i1p1f2.Amon.tas.gn.v20200417'
                ]
        ),
        dims="realization",
        time="1996-01-01/1996-01-31"
)

# skip optional average
# wf = ops.Average(wf, dims="realization")

# optional subset
wf = ops.Subset(wf, time="1996-01-01/1996-01-31")

resp = wf.orchestrate()
resp.ok

Out[6]:

True

open with xarray¶

In [7]:

resp.size_in_mb

Out[7]:

2.561394691467285

In [8]:

dsets = resp.datasets()

Downloading to /var/folders/qb/mg0csz190wd4rxybhhnwjln80000gn/T/metalink_cisy9b5a/tas_Amon_HadGEM3-GC31-MM_dcppA-hindcast_r10i1p1f2_gn_19960116-19960116.nc.

In [9]:

ds = dsets[0]
ds

In [10]:

ds.cf

Out[10]:

Coordinates:
             CF Axes: * X: ['lon']
                      * Y: ['lat']
                        Z: ['height']
                      * T: ['time']

      CF Coordinates: * longitude: ['lon']
                      * latitude: ['lat']
                        vertical: ['height']
                      * time: ['time']

       Cell Measures:   area, volume: n/a

      Standard Names:   forecast_period: ['leadtime']
                        forecast_reference_time: ['reftime']
                        height: ['height']
                      * latitude: ['lat']
                      * longitude: ['lon']
                      * realization: ['realization_index']
                      * time: ['time']

              Bounds:   n/a

       Grid Mappings:   n/a

Data Variables:
       Cell Measures:   area, volume: n/a

      Standard Names:   air_temperature: ['tas']

              Bounds:   T: ['time_bnds']
                        X: ['lon_bnds']
                        Y: ['lat_bnds']
                        lat: ['lat_bnds']
                        latitude: ['lat_bnds']
                        lon: ['lon_bnds']
                        longitude: ['lon_bnds']
                        time: ['time_bnds']

       Grid Mappings:   n/a

provenance¶

In [11]:

prov_plot_url = resp.provenance_image()
prov_plot_url

Out[11]:

'http://rook4.cloud.dkrz.de:80/outputs/rook/75f7ffe4-7a61-11ee-97e8-fa163ed6c06f/provenance.png'

In [12]:

from IPython.display import Image
Image(prov_plot_url)

Out[12]:

select specific realization¶

using coordinate realization_index

In [13]:

print(ds.realization_index.values)

[0 1 2 3 4 5 6 7 8 9]

In [14]:

ds.realization_index.standard_name

Out[14]:

'realization'

In [15]:

ds_r = ds.sel(realization_index=5)
ds_r

In [16]:

print(ds_r.realization_index.values)

using cf standard name of realization¶

https://cf-xarray.readthedocs.io/en/latest/index.html

In [17]:

print(ds.cf["realization"].values)

[0 1 2 3 4 5 6 7 8 9]

In [18]:

ds_r = ds.cf.sel(realization=5)
ds_r