Compute climate index TG (‘Mean daily mean temperature’). The computation in the back is taken from the python module xclim

%load_ext autoreload
%autoreload 2

import warnings

warnings.filterwarnings("ignore")

from pyhomogenize import open_xrdataset

import index_calculator as xcalc

Select one CORDEX tas file and open it with xarray.

ifile = "tas_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_20010101-20051231.nc"

ds = open_xrdataset(ifile)
ds

<xarray.Dataset>
Dimensions:                     (time: 1826, bnds: 2, rlat: 412, rlon: 424,
                                 vertices: 4)
Coordinates:
  * time                        (time) object 2001-01-01 12:00:00 ... 2005-12...
  * rlat                        (rlat) float64 -23.38 -23.27 ... 21.72 21.84
  * rlon                        (rlon) float64 -28.38 -28.27 ... 18.04 18.16
    lat                         (rlat, rlon) float32 dask.array<chunksize=(412, 424), meta=np.ndarray>
    lon                         (rlat, rlon) float32 dask.array<chunksize=(412, 424), meta=np.ndarray>
    height                      float64 ...
Dimensions without coordinates: bnds, vertices
Data variables:
    time_bnds                   (time, bnds) object dask.array<chunksize=(1, 2), meta=np.ndarray>
    rotated_latitude_longitude  int32 ...
    lat_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    lon_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    tas                         (time, rlat, rlon) float32 dask.array<chunksize=(1, 412, 424), meta=np.ndarray>
Attributes: (12/35)
    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...
    institute_id:                   GERICS
    experiment_id:                  historical
    source:                         GERICS-REMO2015
    model_id:                       GERICS-REMO2015
    forcing:                        N/A
    ...                             ...
    parent_experiment:              N/A
    modeling_realm:                 atmos
    realization:                    3
    cmor_version:                   2.9.1
    tracking_id:                    hdl:21.14103/55a844fd-666d-4799-8563-8fbd...
    CF_variables:                   ['tas']

xarray.Dataset

• Dimensions:
  • time: 1826
  • bnds: 2
  • rlat: 412
  • rlon: 424
  • vertices: 4
• Coordinates: (6)
  • time
    (time)
    object
    2001-01-01 12:00:00 ... 2005-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeProlepticGregorian(2001, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2001, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2001, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeProlepticGregorian(2005, 12, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 12, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 12, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • rlat
    (rlat)
    float64
    -23.38 -23.27 ... 21.72 21.84

    units :

    degrees

    axis :

    Y

    long_name :

    latitude in rotated pole grid

    standard_name :

    grid_latitude

    array([-23.375, -23.265, -23.155, ...,  21.615,  21.725,  21.835])

  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.04 18.16

    units :

    degrees

    axis :

    X

    long_name :

    longitude in rotated pole grid

    standard_name :

    grid_longitude

    array([-28.375, -28.265, -28.155, ...,  17.935,  18.045,  18.155])

  • lat
    (rlat, rlon)
    float32
    dask.array<chunksize=(412, 424), meta=np.ndarray>

    standard_name :

    latitude

    long_name :

    latitude coordinate

    units :

    degrees_north

    bounds :

    lat_vertices

    Array Chunk Bytes 682.38 kiB 682.38 kiB Shape (412, 424) (412, 424) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon
    (rlat, rlon)
    float32
    dask.array<chunksize=(412, 424), meta=np.ndarray>

    standard_name :

    longitude

    long_name :

    longitude coordinate

    units :

    degrees_east

    bounds :

    lon_vertices

    Array Chunk Bytes 682.38 kiB 682.38 kiB Shape (412, 424) (412, 424) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • height
    ()
    float64
    ...

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

• Data variables: (5)
  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 28.53 kiB 16 B Shape (1826, 2) (1, 2) Count 3653 Tasks 1826 Chunks Type object numpy.ndarray

  • rotated_latitude_longitude
    ()
    int32
    ...

    grid_mapping_name :

    rotated_latitude_longitude

    grid_north_pole_latitude :

    39.25

    grid_north_pole_longitude :

    -162.0

    north_pole_grid_longitude :

    0.0

    array(-2147483647, dtype=int32)

  • lat_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • tas
    (time, rlat, rlon)
    float32
    dask.array<chunksize=(1, 412, 424), meta=np.ndarray>

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    comment :

    daily-mean near-surface (usually, 2 meter) air temperature.

    units :

    K

    original_name :

    tas

    cell_methods :

    time: mean

    history :

    2019-06-19T15:31:38Z altered by CMOR: Treated scalar dimension: 'height'. 2019-06-19T15:31:38Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20).

    associated_files :

    tas_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_20010101-20051231.nc

    grid_mapping :

    rotated_latitude_longitude

    Array Chunk Bytes 1.19 GiB 682.38 kiB Shape (1826, 412, 424) (1, 412, 424) Count 3653 Tasks 1826 Chunks Type float32 numpy.ndarray

• Attributes: (35)

  institution :

  Helmholtz-Zentrum Geesthacht, Climate Service Center Germany

  institute_id :

  GERICS

  experiment_id :

  historical

  source :

  GERICS-REMO2015

  model_id :

  GERICS-REMO2015

  forcing :

  N/A

  parent_experiment_id :

  N/A

  parent_experiment_rip :

  N/A

  branch_time :

  0.0

  contact :

  gerics-cordex@hzg.de

  history :

  There is no special history for this experiment. 2019-06-19T15:31:38Z CMOR rewrote data to comply with CF standards and CORDEX requirements.

  comment :

  CORDEX Europe RCM REMO EUR-11. Data_policy: This data has been produced in the context of the C3S_34b_Lot1 and Lot 2 projects (PRINCIPLES/CORDEX4CDS) as a data provider for the Climate Data Store within the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/). While abiding by the highest scientific and technical standards, ECMWF cannot warrant that any information provided by the C3S will be entirely free from errors or omissions or that such errors or omissions can or will be rectified entirely. This applies to data from projects that continue to be developed, but are made publicly available for the purpose of feedback and testing. Some data or metadata may have been created or structured in files or formats that are not error-free. ECMWF accepts no responsibility with regard to such problems incurred as a result of using this data (see http://climate.copernicus.eu/disclaimer-privacy for the full disclaimer)

  references :

  http://www.remo-rcm.de

  initialization_method :

  1

  physics_version :

  1

  CORDEX_domain :

  EUR-11

  driving_experiment :

  MPI-M-MPI-ESM-LR, historical, r3i1p1

  driving_model_id :

  MPI-M-MPI-ESM-LR

  driving_model_ensemble_member :

  r3i1p1

  driving_experiment_name :

  historical

  rcm_version_id :

  v1

  product :

  output

  experiment :

  historical

  frequency :

  day

  creation_date :

  2019-06-19T15:31:38Z

  Conventions :

  CF-1.4

  project_id :

  CORDEX

  table_id :

  Table day (March 2015) 0fd7c9320dff3b351cc577fecbf99273

  title :

  GERICS-REMO2015 model output prepared for CORDEX historical

  parent_experiment :

  N/A

  modeling_realm :

  atmos

  realization :

  3

  cmor_version :

  2.9.1

  tracking_id :

  hdl:21.14103/55a844fd-666d-4799-8563-8fbd7002fd23

  CF_variables :

  ['tas']

Now do a preprocessing of the dataset and create an index_calculator.PreProcessing object. Preprocessor tasks are:

• select a user-given time range (parameter: time_range)

• check time axis for missing, redundant and duplicated time steps

• crop time axis to required left and right border

• write available and requested time range to object

preproc = xcalc.preprocessing(ds)
preproc.preproc

<xarray.Dataset>
Dimensions:                     (time: 1826, bnds: 2, rlat: 412, rlon: 424,
                                 vertices: 4)
Coordinates:
  * time                        (time) object 2001-01-01 12:00:00 ... 2005-12...
  * rlat                        (rlat) float64 -23.38 -23.27 ... 21.72 21.84
  * rlon                        (rlon) float64 -28.38 -28.27 ... 18.04 18.16
    lat                         (rlat, rlon) float32 dask.array<chunksize=(412, 424), meta=np.ndarray>
    lon                         (rlat, rlon) float32 dask.array<chunksize=(412, 424), meta=np.ndarray>
    height                      float64 ...
Dimensions without coordinates: bnds, vertices
Data variables:
    time_bnds                   (time, bnds) object dask.array<chunksize=(1, 2), meta=np.ndarray>
    rotated_latitude_longitude  int32 ...
    lat_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    lon_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    tas                         (time, rlat, rlon) float32 dask.array<chunksize=(1, 412, 424), meta=np.ndarray>
Attributes: (12/35)
    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...
    institute_id:                   GERICS
    experiment_id:                  historical
    source:                         GERICS-REMO2015
    model_id:                       GERICS-REMO2015
    forcing:                        N/A
    ...                             ...
    parent_experiment:              N/A
    modeling_realm:                 atmos
    realization:                    3
    cmor_version:                   2.9.1
    tracking_id:                    hdl:21.14103/55a844fd-666d-4799-8563-8fbd...
    CF_variables:                   ['tas']

xarray.Dataset

• Dimensions:
  • time: 1826
  • bnds: 2
  • rlat: 412
  • rlon: 424
  • vertices: 4
• Coordinates: (6)
  • time
    (time)
    object
    2001-01-01 12:00:00 ... 2005-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeProlepticGregorian(2001, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2001, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2001, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeProlepticGregorian(2005, 12, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 12, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 12, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • rlat
    (rlat)
    float64
    -23.38 -23.27 ... 21.72 21.84

    units :

    degrees

    axis :

    Y

    long_name :

    latitude in rotated pole grid

    standard_name :

    grid_latitude

    array([-23.375, -23.265, -23.155, ...,  21.615,  21.725,  21.835])

  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.04 18.16

    units :

    degrees

    axis :

    X

    long_name :

    longitude in rotated pole grid

    standard_name :

    grid_longitude

    array([-28.375, -28.265, -28.155, ...,  17.935,  18.045,  18.155])

  • lat
    (rlat, rlon)
    float32
    dask.array<chunksize=(412, 424), meta=np.ndarray>

    standard_name :

    latitude

    long_name :

    latitude coordinate

    units :

    degrees_north

    bounds :

    lat_vertices

    Array Chunk Bytes 682.38 kiB 682.38 kiB Shape (412, 424) (412, 424) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon
    (rlat, rlon)
    float32
    dask.array<chunksize=(412, 424), meta=np.ndarray>

    standard_name :

    longitude

    long_name :

    longitude coordinate

    units :

    degrees_east

    bounds :

    lon_vertices

    Array Chunk Bytes 682.38 kiB 682.38 kiB Shape (412, 424) (412, 424) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • height
    ()
    float64
    ...

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

• Data variables: (5)
  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 28.53 kiB 16 B Shape (1826, 2) (1, 2) Count 3653 Tasks 1826 Chunks Type object numpy.ndarray

  • rotated_latitude_longitude
    ()
    int32
    ...

    grid_mapping_name :

    rotated_latitude_longitude

    grid_north_pole_latitude :

    39.25

    grid_north_pole_longitude :

    -162.0

    north_pole_grid_longitude :

    0.0

    array(-2147483647, dtype=int32)

  • lat_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray

  • tas
    (time, rlat, rlon)
    float32
    dask.array<chunksize=(1, 412, 424), meta=np.ndarray>

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    comment :

    daily-mean near-surface (usually, 2 meter) air temperature.

    units :

    K

    original_name :

    tas

    cell_methods :

    time: mean

    history :

    2019-06-19T15:31:38Z altered by CMOR: Treated scalar dimension: 'height'. 2019-06-19T15:31:38Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20).

    associated_files :

    tas_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_20010101-20051231.nc

    grid_mapping :

    rotated_latitude_longitude

    Array Chunk Bytes 1.19 GiB 682.38 kiB Shape (1826, 412, 424) (1, 412, 424) Count 3653 Tasks 1826 Chunks Type float32 numpy.ndarray

• Attributes: (35)

  institution :

  Helmholtz-Zentrum Geesthacht, Climate Service Center Germany

  institute_id :

  GERICS

  experiment_id :

  historical

  source :

  GERICS-REMO2015

  model_id :

  GERICS-REMO2015

  forcing :

  N/A

  parent_experiment_id :

  N/A

  parent_experiment_rip :

  N/A

  branch_time :

  0.0

  contact :

  gerics-cordex@hzg.de

  history :

  There is no special history for this experiment. 2019-06-19T15:31:38Z CMOR rewrote data to comply with CF standards and CORDEX requirements.

  comment :

  CORDEX Europe RCM REMO EUR-11. Data_policy: This data has been produced in the context of the C3S_34b_Lot1 and Lot 2 projects (PRINCIPLES/CORDEX4CDS) as a data provider for the Climate Data Store within the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/). While abiding by the highest scientific and technical standards, ECMWF cannot warrant that any information provided by the C3S will be entirely free from errors or omissions or that such errors or omissions can or will be rectified entirely. This applies to data from projects that continue to be developed, but are made publicly available for the purpose of feedback and testing. Some data or metadata may have been created or structured in files or formats that are not error-free. ECMWF accepts no responsibility with regard to such problems incurred as a result of using this data (see http://climate.copernicus.eu/disclaimer-privacy for the full disclaimer)

  references :

  http://www.remo-rcm.de

  initialization_method :

  1

  physics_version :

  1

  CORDEX_domain :

  EUR-11

  driving_experiment :

  MPI-M-MPI-ESM-LR, historical, r3i1p1

  driving_model_id :

  MPI-M-MPI-ESM-LR

  driving_model_ensemble_member :

  r3i1p1

  driving_experiment_name :

  historical

  rcm_version_id :

  v1

  product :

  output

  experiment :

  historical

  frequency :

  day

  creation_date :

  2019-06-19T15:31:38Z

  Conventions :

  CF-1.4

  project_id :

  CORDEX

  table_id :

  Table day (March 2015) 0fd7c9320dff3b351cc577fecbf99273

  title :

  GERICS-REMO2015 model output prepared for CORDEX historical

  parent_experiment :

  N/A

  modeling_realm :

  atmos

  realization :

  3

  cmor_version :

  2.9.1

  tracking_id :

  hdl:21.14103/55a844fd-666d-4799-8563-8fbd7002fd23

  CF_variables :

  ['tas']

Now, use the above object to compute the climate index TG.

proc = xcalc.processing("TG", preproc_obj=preproc)
proc.proc

<xarray.Dataset>
Dimensions:                     (rlat: 412, rlon: 424, vertices: 4, time: 5,
                                 bounds: 2)
Coordinates:
    height                      float64 2.0
  * rlat                        (rlat) float64 -23.38 -23.27 ... 21.72 21.84
  * rlon                        (rlon) float64 -28.38 -28.27 ... 18.04 18.16
    lat                         (rlat, rlon) float32 21.99 22.03 ... 66.75 66.69
    lon                         (rlat, rlon) float32 349.9 350.0 ... 64.76 64.96
  * time                        (time) object 2001-07-03 00:00:00 ... 2005-07...
Dimensions without coordinates: vertices, bounds
Data variables:
    rotated_latitude_longitude  int32 ...
    lat_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    lon_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    TG                          (time, rlat, rlon) float32 dask.array<chunksize=(1, 412, 424), meta=np.ndarray>
    time_bnds                   (time, bounds) object 2001-01-01 12:00:00 ......
Attributes: (12/35)
    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...
    institute_id:                   GERICS
    experiment_id:                  historical
    source:                         GERICS-REMO2015
    model_id:                       GERICS-REMO2015
    forcing:                        N/A
    ...                             ...
    parent_experiment:              N/A
    modeling_realm:                 atmos
    realization:                    3
    cmor_version:                   2.9.1
    tracking_id:                    hdl:21.14103/55a844fd-666d-4799-8563-8fbd...
    CF_variables:                   ['tas']

xarray.Dataset

• Dimensions:
  • rlat: 412
  • rlon: 424
  • vertices: 4
  • time: 5
  • bounds: 2
• Coordinates: (6)
  • height
    ()
    float64
    2.0

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

  • rlat
    (rlat)
    float64
    -23.38 -23.27 ... 21.72 21.84

    units :

    degrees

    axis :

    Y

    long_name :

    latitude in rotated pole grid

    standard_name :

    grid_latitude

    array([-23.375, -23.265, -23.155, ...,  21.615,  21.725,  21.835])

  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.04 18.16

    units :

    degrees

    axis :

    X

    long_name :

    longitude in rotated pole grid

    standard_name :

    grid_longitude

    array([-28.375, -28.265, -28.155, ...,  17.935,  18.045,  18.155])

  • lat
    (rlat, rlon)
    float32
    21.99 22.03 22.07 ... 66.75 66.69

    standard_name :

    latitude

    long_name :

    latitude coordinate

    units :

    degrees_north

    bounds :

    lat_vertices

    array([[21.987913, 22.027924, 22.0678  , ..., 25.167871, 25.141191,
            25.114359],
           [22.088873, 22.128944, 22.168882, ..., 25.273981, 25.247255,
            25.220379],
           [22.189817, 22.22995 , 22.269953, ..., 25.380085, 25.353313,
            25.326391],
           ...,
           [60.055767, 60.13112 , 60.20637 , ..., 66.63879 , 66.57718 ,
            66.515366],
           [60.129982, 60.205444, 60.28081 , ..., 66.726585, 66.6648  ,
            66.60282 ],
           [60.203995, 60.27957 , 60.355053, ..., 66.8142  , 66.75225 ,
            66.690094]], dtype=float32)

  • lon
    (rlat, rlon)
    float32
    349.9 350.0 350.1 ... 64.76 64.96

    standard_name :

    longitude

    long_name :

    longitude coordinate

    units :

    degrees_east

    bounds :

    lon_vertices

    array([[349.9361  , 350.03607 , 350.13617 , ...,  36.198868,  36.306454,
             36.41397 ],
           [349.88898 , 349.9891  , 350.08932 , ...,  36.230915,  36.33868 ,
             36.446373],
           [349.8418  , 349.94205 , 350.04242 , ...,  36.263023,  36.370964,
             36.478832],
           ...,
           [315.7324  , 315.87106 , 316.01025 , ...,  64.21771 ,  64.42331 ,
             64.62799 ],
           [315.56955 , 315.70804 , 315.84708 , ...,  64.38513 ,  64.59088 ,
             64.795715],
           [315.40598 , 315.5443  , 315.68317 , ...,  64.55375 ,  64.75965 ,
             64.96463 ]], dtype=float32)

  • time
    (time)
    object
    2001-07-03 00:00:00 ... 2005-07-...

    array([cftime.DatetimeProlepticGregorian(2001, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2002, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2003, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2004, 7, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 7, 3, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (5)
  • rotated_latitude_longitude
    ()
    int32
    ...

    grid_mapping_name :

    rotated_latitude_longitude

    grid_north_pole_latitude :

    39.25

    grid_north_pole_longitude :

    -162.0

    north_pole_grid_longitude :

    0.0

    array(-2147483647, dtype=int32)

  • lat_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray

  • TG
    (time, rlat, rlon)
    float32
    dask.array<chunksize=(1, 412, 424), meta=np.ndarray>

    units :

    K

    cell_methods :

    time: mean time: mean over days

    history :

    2019-06-19T15:31:38Z altered by CMOR: Treated scalar dimension: 'height'. 2019-06-19T15:31:38Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). [2022-08-11 10:37:05] tg_mean: TG_MEAN(tas=tas, freq='YS') with options check_missing=any - xclim version: 0.37.0

    standard_name :

    air_temperature

    long_name :

    Mean daily mean temperature

    description :

    Annual mean of daily mean temperature.

    Array Chunk Bytes 3.33 MiB 682.38 kiB Shape (5, 412, 424) (1, 412, 424) Count 20167 Tasks 5 Chunks Type float32 numpy.ndarray

  • time_bnds
    (time, bounds)
    object
    2001-01-01 12:00:00 ... 2006-01-...

    array([[cftime.DatetimeProlepticGregorian(2001, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2002, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2002, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2003, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2003, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2004, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2004, 1, 1, 18, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2005, 1, 1, 6, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2005, 1, 1, 6, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2006, 1, 1, 18, 0, 0, 0, has_year_zero=True)]],
          dtype=object)

• Attributes: (35)

  institution :

  Helmholtz-Zentrum Geesthacht, Climate Service Center Germany

  institute_id :

  GERICS

  experiment_id :

  historical

  source :

  GERICS-REMO2015

  model_id :

  GERICS-REMO2015

  forcing :

  N/A

  parent_experiment_id :

  N/A

  parent_experiment_rip :

  N/A

  branch_time :

  0.0

  contact :

  gerics-cordex@hzg.de

  history :

  There is no special history for this experiment. 2019-06-19T15:31:38Z CMOR rewrote data to comply with CF standards and CORDEX requirements.

  comment :

  CORDEX Europe RCM REMO EUR-11. Data_policy: This data has been produced in the context of the C3S_34b_Lot1 and Lot 2 projects (PRINCIPLES/CORDEX4CDS) as a data provider for the Climate Data Store within the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/). While abiding by the highest scientific and technical standards, ECMWF cannot warrant that any information provided by the C3S will be entirely free from errors or omissions or that such errors or omissions can or will be rectified entirely. This applies to data from projects that continue to be developed, but are made publicly available for the purpose of feedback and testing. Some data or metadata may have been created or structured in files or formats that are not error-free. ECMWF accepts no responsibility with regard to such problems incurred as a result of using this data (see http://climate.copernicus.eu/disclaimer-privacy for the full disclaimer)

  references :

  http://www.remo-rcm.de

  initialization_method :

  1

  physics_version :

  1

  CORDEX_domain :

  EUR-11

  driving_experiment :

  MPI-M-MPI-ESM-LR, historical, r3i1p1

  driving_model_id :

  MPI-M-MPI-ESM-LR

  driving_model_ensemble_member :

  r3i1p1

  driving_experiment_name :

  historical

  rcm_version_id :

  v1

  product :

  output

  experiment :

  historical

  frequency :

  day

  creation_date :

  2019-06-19T15:31:38Z

  Conventions :

  CF-1.4

  project_id :

  CORDEX

  table_id :

  Table day (March 2015) 0fd7c9320dff3b351cc577fecbf99273

  title :

  GERICS-REMO2015 model output prepared for CORDEX historical

  parent_experiment :

  N/A

  modeling_realm :

  atmos

  realization :

  3

  cmor_version :

  2.9.1

  tracking_id :

  hdl:21.14103/55a844fd-666d-4799-8563-8fbd7002fd23

  CF_variables :

  ['tas']

Finally, do some postprocessing. The postprocessor writes some additional attributes to ds’ global attributes.

postproc = xcalc.postprocessing(
    project="CORDEX",
    proc_obj=proc,
    institution="Helmholtz-Zentrum hereon GmbH, Climate Service Center Germany",
    institution_id="GERICS",
)
postproc.postproc

<xarray.Dataset>
Dimensions:                     (rlat: 412, rlon: 424, vertices: 4, time: 5,
                                 bounds: 2)
Coordinates:
    height                      float64 2.0
  * rlat                        (rlat) float64 -23.38 -23.27 ... 21.72 21.84
  * rlon                        (rlon) float64 -28.38 -28.27 ... 18.04 18.16
    lat                         (rlat, rlon) float32 21.99 22.03 ... 66.75 66.69
    lon                         (rlat, rlon) float32 349.9 350.0 ... 64.76 64.96
  * time                        (time) object 2001-07-03 00:00:00 ... 2005-07...
Dimensions without coordinates: vertices, bounds
Data variables:
    rotated_latitude_longitude  int32 ...
    lat_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    lon_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    TG                          (time, rlat, rlon) float32 dask.array<chunksize=(1, 412, 424), meta=np.ndarray>
    time_bnds                   (time, bounds) object 2001-01-01 12:00:00 ......
Attributes: (12/45)
    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...
    institute_id:                   GERICS
    experiment_id:                  historical
    source:                         GERICS-REMO2015
    model_id:                       GERICS-REMO2015
    forcing:                        N/A
    ...                             ...
    ci_name:                        TG
    ci_frequency:                   year
    ci_timerange_index:             2001-2005
    ci_timerange_source:            20010101-20051231
    ci_package_name:                xclim_0.37.0
    ci_package_reference:           xcalc_0.3.2

xarray.Dataset

• Dimensions:
  • rlat: 412
  • rlon: 424
  • vertices: 4
  • time: 5
  • bounds: 2
• Coordinates: (6)
  • height
    ()
    float64
    2.0

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

  • rlat
    (rlat)
    float64
    -23.38 -23.27 ... 21.72 21.84

    units :

    degrees

    axis :

    Y

    long_name :

    latitude in rotated pole grid

    standard_name :

    grid_latitude

    array([-23.375, -23.265, -23.155, ...,  21.615,  21.725,  21.835])

  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.04 18.16

    units :

    degrees

    axis :

    X

    long_name :

    longitude in rotated pole grid

    standard_name :

    grid_longitude

    array([-28.375, -28.265, -28.155, ...,  17.935,  18.045,  18.155])

  • lat
    (rlat, rlon)
    float32
    21.99 22.03 22.07 ... 66.75 66.69

    standard_name :

    latitude

    long_name :

    latitude coordinate

    units :

    degrees_north

    bounds :

    lat_vertices

    array([[21.987913, 22.027924, 22.0678  , ..., 25.167871, 25.141191,
            25.114359],
           [22.088873, 22.128944, 22.168882, ..., 25.273981, 25.247255,
            25.220379],
           [22.189817, 22.22995 , 22.269953, ..., 25.380085, 25.353313,
            25.326391],
           ...,
           [60.055767, 60.13112 , 60.20637 , ..., 66.63879 , 66.57718 ,
            66.515366],
           [60.129982, 60.205444, 60.28081 , ..., 66.726585, 66.6648  ,
            66.60282 ],
           [60.203995, 60.27957 , 60.355053, ..., 66.8142  , 66.75225 ,
            66.690094]], dtype=float32)

  • lon
    (rlat, rlon)
    float32
    349.9 350.0 350.1 ... 64.76 64.96

    standard_name :

    longitude

    long_name :

    longitude coordinate

    units :

    degrees_east

    bounds :

    lon_vertices

    array([[349.9361  , 350.03607 , 350.13617 , ...,  36.198868,  36.306454,
             36.41397 ],
           [349.88898 , 349.9891  , 350.08932 , ...,  36.230915,  36.33868 ,
             36.446373],
           [349.8418  , 349.94205 , 350.04242 , ...,  36.263023,  36.370964,
             36.478832],
           ...,
           [315.7324  , 315.87106 , 316.01025 , ...,  64.21771 ,  64.42331 ,
             64.62799 ],
           [315.56955 , 315.70804 , 315.84708 , ...,  64.38513 ,  64.59088 ,
             64.795715],
           [315.40598 , 315.5443  , 315.68317 , ...,  64.55375 ,  64.75965 ,
             64.96463 ]], dtype=float32)

  • time
    (time)
    object
    2001-07-03 00:00:00 ... 2005-07-...

    array([cftime.DatetimeProlepticGregorian(2001, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2002, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2003, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2004, 7, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 7, 3, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (5)
  • rotated_latitude_longitude
    ()
    int32
    ...

    grid_mapping_name :

    rotated_latitude_longitude

    grid_north_pole_latitude :

    39.25

    grid_north_pole_longitude :

    -162.0

    north_pole_grid_longitude :

    0.0

    array(-2147483647, dtype=int32)

  • lat_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray

  • TG
    (time, rlat, rlon)
    float32
    dask.array<chunksize=(1, 412, 424), meta=np.ndarray>

    units :

    K

    cell_methods :

    time: mean time: mean over days

    history :

    2019-06-19T15:31:38Z altered by CMOR: Treated scalar dimension: 'height'. 2019-06-19T15:31:38Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). [2022-08-11 10:37:06] tg_mean: TG_MEAN(tas=tas, freq='YS') with options check_missing=any - xclim version: 0.37.0

    standard_name :

    air_temperature

    long_name :

    Mean daily mean temperature

    description :

    Mean of daily mean temperature

    associated_files :

    tas_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_20010101-20051231.nc

    Array Chunk Bytes 3.33 MiB 682.38 kiB Shape (5, 412, 424) (1, 412, 424) Count 20167 Tasks 5 Chunks Type float32 numpy.ndarray

  • time_bnds
    (time, bounds)
    object
    2001-01-01 12:00:00 ... 2006-01-...

    array([[cftime.DatetimeProlepticGregorian(2001, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2002, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2002, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2003, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2003, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2004, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2004, 1, 1, 18, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2005, 1, 1, 6, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2005, 1, 1, 6, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2006, 1, 1, 18, 0, 0, 0, has_year_zero=True)]],
          dtype=object)

• Attributes: (45)

  institution :

  Helmholtz-Zentrum Geesthacht, Climate Service Center Germany

  institute_id :

  GERICS

  experiment_id :

  historical

  source :

  GERICS-REMO2015

  model_id :

  GERICS-REMO2015

  forcing :

  N/A

  parent_experiment_id :

  N/A

  parent_experiment_rip :

  N/A

  branch_time :

  0.0

  contact :

  gerics-cordex@hzg.de

  history :

  There is no special history for this experiment. 2019-06-19T15:31:38Z CMOR rewrote data to comply with CF standards and CORDEX requirements.

  comment :

  CORDEX Europe RCM REMO EUR-11. Data_policy: This data has been produced in the context of the C3S_34b_Lot1 and Lot 2 projects (PRINCIPLES/CORDEX4CDS) as a data provider for the Climate Data Store within the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/). While abiding by the highest scientific and technical standards, ECMWF cannot warrant that any information provided by the C3S will be entirely free from errors or omissions or that such errors or omissions can or will be rectified entirely. This applies to data from projects that continue to be developed, but are made publicly available for the purpose of feedback and testing. Some data or metadata may have been created or structured in files or formats that are not error-free. ECMWF accepts no responsibility with regard to such problems incurred as a result of using this data (see http://climate.copernicus.eu/disclaimer-privacy for the full disclaimer)

  references :

  http://www.remo-rcm.de

  initialization_method :

  1

  physics_version :

  1

  CORDEX_domain :

  EUR-11

  driving_experiment :

  MPI-M-MPI-ESM-LR, historical, r3i1p1

  driving_model_id :

  MPI-M-MPI-ESM-LR

  driving_model_ensemble_member :

  r3i1p1

  driving_experiment_name :

  historical

  rcm_version_id :

  v1

  product :

  output

  experiment :

  historical

  frequency :

  day

  creation_date :

  2019-06-19T15:31:38Z

  Conventions :

  CF-1.4

  project_id :

  CORDEX

  table_id :

  Table day (March 2015) 0fd7c9320dff3b351cc577fecbf99273

  title :

  GERICS-REMO2015 model output prepared for CORDEX historical

  parent_experiment :

  N/A

  modeling_realm :

  atmos

  realization :

  3

  cmor_version :

  2.9.1

  tracking_id :

  hdl:21.14103/55a844fd-666d-4799-8563-8fbd7002fd23

  CF_variables :

  ['tas']

  ci_creation_date :

  2022-08-11T10:37:06Z

  ci_institute_id :

  GERICS

  ci_institution :

  Helmholtz-Zentrum hereon GmbH, Climate Service Center Germany

  ci_contact :

  N/A

  ci_name :

  TG

  ci_frequency :

  year

  ci_timerange_index :

  2001-2005

  ci_timerange_source :

  20010101-20051231

  ci_package_name :

  xclim_0.37.0

  ci_package_reference :

  xcalc_0.3.2

Optionaly, we can store the dataset as a netCDF file on disk. The output file naming depends on the user-given project.

xcalc.outputwriter(postproc_obj=postproc)

File written: ./cordex/climdex/EUR-11/GERICS/GERICS/MPI-M-MPI-ESM-LR/historical/r3i1p1/GERICS-REMO2015/v1/year/TG/TG_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_GERICS_year_2001-2005.nc

<index_calculator.OutputWriter at 0x7fff8dd41d20>

For simplyfication, we can do preprocessing, processing, postprocessing and output file storing by calling just one function.

idx_obj = xcalc.index_calculator(
    ds=ds,
    index="TG",
    project="CORDEX",
    institution_id="GERICS",
    institution="Helmholtz-Zentrum hereon GmbH, Climate Service Center Germany",
    write=True,
)

File written: ./cordex/climdex/EUR-11/GERICS/GERICS/MPI-M-MPI-ESM-LR/historical/r3i1p1/GERICS-REMO2015/v1/year/TG/TG_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_GERICS_year_2001-2005.nc

idx_obj.postproc

<xarray.Dataset>
Dimensions:                     (rlat: 412, rlon: 424, vertices: 4, time: 5,
                                 bounds: 2)
Coordinates:
    height                      float64 2.0
  * rlat                        (rlat) float64 -23.38 -23.27 ... 21.72 21.84
  * rlon                        (rlon) float64 -28.38 -28.27 ... 18.04 18.16
    lat                         (rlat, rlon) float32 21.99 22.03 ... 66.75 66.69
    lon                         (rlat, rlon) float32 349.9 350.0 ... 64.76 64.96
  * time                        (time) object 2001-07-03 00:00:00 ... 2005-07...
Dimensions without coordinates: vertices, bounds
Data variables:
    rotated_latitude_longitude  int32 ...
    lat_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    lon_vertices                (rlat, rlon, vertices) float32 dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    TG                          (time, rlat, rlon) float32 dask.array<chunksize=(1, 412, 424), meta=np.ndarray>
    time_bnds                   (time, bounds) object 2001-01-01 12:00:00 ......
Attributes: (12/45)
    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...
    institute_id:                   GERICS
    experiment_id:                  historical
    source:                         GERICS-REMO2015
    model_id:                       GERICS-REMO2015
    forcing:                        N/A
    ...                             ...
    ci_name:                        TG
    ci_frequency:                   year
    ci_timerange_index:             2001-2005
    ci_timerange_source:            20010101-20051231
    ci_package_name:                xclim_0.37.0
    ci_package_reference:           xcalc_0.3.2

xarray.Dataset

• Dimensions:
  • rlat: 412
  • rlon: 424
  • vertices: 4
  • time: 5
  • bounds: 2
• Coordinates: (6)
  • height
    ()
    float64
    2.0

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

  • rlat
    (rlat)
    float64
    -23.38 -23.27 ... 21.72 21.84

    units :

    degrees

    axis :

    Y

    long_name :

    latitude in rotated pole grid

    standard_name :

    grid_latitude

    array([-23.375, -23.265, -23.155, ...,  21.615,  21.725,  21.835])

  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.04 18.16

    units :

    degrees

    axis :

    X

    long_name :

    longitude in rotated pole grid

    standard_name :

    grid_longitude

    array([-28.375, -28.265, -28.155, ...,  17.935,  18.045,  18.155])

  • lat
    (rlat, rlon)
    float32
    21.99 22.03 22.07 ... 66.75 66.69

    standard_name :

    latitude

    long_name :

    latitude coordinate

    units :

    degrees_north

    bounds :

    lat_vertices

    array([[21.987913, 22.027924, 22.0678  , ..., 25.167871, 25.141191,
            25.114359],
           [22.088873, 22.128944, 22.168882, ..., 25.273981, 25.247255,
            25.220379],
           [22.189817, 22.22995 , 22.269953, ..., 25.380085, 25.353313,
            25.326391],
           ...,
           [60.055767, 60.13112 , 60.20637 , ..., 66.63879 , 66.57718 ,
            66.515366],
           [60.129982, 60.205444, 60.28081 , ..., 66.726585, 66.6648  ,
            66.60282 ],
           [60.203995, 60.27957 , 60.355053, ..., 66.8142  , 66.75225 ,
            66.690094]], dtype=float32)

  • lon
    (rlat, rlon)
    float32
    349.9 350.0 350.1 ... 64.76 64.96

    standard_name :

    longitude

    long_name :

    longitude coordinate

    units :

    degrees_east

    bounds :

    lon_vertices

    array([[349.9361  , 350.03607 , 350.13617 , ...,  36.198868,  36.306454,
             36.41397 ],
           [349.88898 , 349.9891  , 350.08932 , ...,  36.230915,  36.33868 ,
             36.446373],
           [349.8418  , 349.94205 , 350.04242 , ...,  36.263023,  36.370964,
             36.478832],
           ...,
           [315.7324  , 315.87106 , 316.01025 , ...,  64.21771 ,  64.42331 ,
             64.62799 ],
           [315.56955 , 315.70804 , 315.84708 , ...,  64.38513 ,  64.59088 ,
             64.795715],
           [315.40598 , 315.5443  , 315.68317 , ...,  64.55375 ,  64.75965 ,
             64.96463 ]], dtype=float32)

  • time
    (time)
    object
    2001-07-03 00:00:00 ... 2005-07-...

    array([cftime.DatetimeProlepticGregorian(2001, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2002, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2003, 7, 3, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2004, 7, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2005, 7, 3, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (5)
  • rotated_latitude_longitude
    ()
    int32
    ...

    grid_mapping_name :

    rotated_latitude_longitude

    grid_north_pole_latitude :

    39.25

    grid_north_pole_longitude :

    -162.0

    north_pole_grid_longitude :

    0.0

    array(-2147483647, dtype=int32)

  • lat_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray

  • lon_vertices
    (rlat, rlon, vertices)
    float32
    dask.array<chunksize=(412, 424, 4), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 2.67 MiB 2.67 MiB Shape (412, 424, 4) (412, 424, 4) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray

  • TG
    (time, rlat, rlon)
    float32
    dask.array<chunksize=(1, 412, 424), meta=np.ndarray>

    units :

    K

    cell_methods :

    time: mean time: mean over days

    history :

    2019-06-19T15:31:38Z altered by CMOR: Treated scalar dimension: 'height'. 2019-06-19T15:31:38Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). [2022-08-11 10:37:11] tg_mean: TG_MEAN(tas=tas, freq='YS') with options check_missing=any - xclim version: 0.37.0

    standard_name :

    air_temperature

    long_name :

    Mean daily mean temperature

    description :

    Mean of daily mean temperature

    associated_files :

    tas_EUR-11_MPI-M-MPI-ESM-LR_historical_r3i1p1_GERICS-REMO2015_v1_day_20010101-20051231.nc

    Array Chunk Bytes 3.33 MiB 682.38 kiB Shape (5, 412, 424) (1, 412, 424) Count 20167 Tasks 5 Chunks Type float32 numpy.ndarray

  • time_bnds
    (time, bounds)
    object
    2001-01-01 12:00:00 ... 2006-01-...

    array([[cftime.DatetimeProlepticGregorian(2001, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2002, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2002, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2003, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2003, 1, 1, 12, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2004, 1, 1, 12, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2004, 1, 1, 18, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2005, 1, 1, 6, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeProlepticGregorian(2005, 1, 1, 6, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeProlepticGregorian(2006, 1, 1, 18, 0, 0, 0, has_year_zero=True)]],
          dtype=object)

• Attributes: (45)

  institution :

  Helmholtz-Zentrum Geesthacht, Climate Service Center Germany

  institute_id :

  GERICS

  experiment_id :

  historical

  source :

  GERICS-REMO2015

  model_id :

  GERICS-REMO2015

  forcing :

  N/A

  parent_experiment_id :

  N/A

  parent_experiment_rip :

  N/A

  branch_time :

  0.0

  contact :

  gerics-cordex@hzg.de

  history :

  There is no special history for this experiment. 2019-06-19T15:31:38Z CMOR rewrote data to comply with CF standards and CORDEX requirements.

  comment :

  CORDEX Europe RCM REMO EUR-11. Data_policy: This data has been produced in the context of the C3S_34b_Lot1 and Lot 2 projects (PRINCIPLES/CORDEX4CDS) as a data provider for the Climate Data Store within the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/). While abiding by the highest scientific and technical standards, ECMWF cannot warrant that any information provided by the C3S will be entirely free from errors or omissions or that such errors or omissions can or will be rectified entirely. This applies to data from projects that continue to be developed, but are made publicly available for the purpose of feedback and testing. Some data or metadata may have been created or structured in files or formats that are not error-free. ECMWF accepts no responsibility with regard to such problems incurred as a result of using this data (see http://climate.copernicus.eu/disclaimer-privacy for the full disclaimer)

  references :

  http://www.remo-rcm.de

  initialization_method :

  1

  physics_version :

  1

  CORDEX_domain :

  EUR-11

  driving_experiment :

  MPI-M-MPI-ESM-LR, historical, r3i1p1

  driving_model_id :

  MPI-M-MPI-ESM-LR

  driving_model_ensemble_member :

  r3i1p1

  driving_experiment_name :

  historical

  rcm_version_id :

  v1

  product :

  output

  experiment :

  historical

  frequency :

  day

  creation_date :

  2019-06-19T15:31:38Z

  Conventions :

  CF-1.4

  project_id :

  CORDEX

  table_id :

  Table day (March 2015) 0fd7c9320dff3b351cc577fecbf99273

  title :

  GERICS-REMO2015 model output prepared for CORDEX historical

  parent_experiment :

  N/A

  modeling_realm :

  atmos

  realization :

  3

  cmor_version :

  2.9.1

  tracking_id :

  hdl:21.14103/55a844fd-666d-4799-8563-8fbd7002fd23

  CF_variables :

  ['tas']

  ci_creation_date :

  2022-08-11T10:37:15Z

  ci_institute_id :

  GERICS

  ci_institution :

  Helmholtz-Zentrum hereon GmbH, Climate Service Center Germany

  ci_contact :

  N/A

  ci_name :

  TG

  ci_frequency :

  year

  ci_timerange_index :

  2001-2005

  ci_timerange_source :

  20010101-20051231

  ci_package_name :

  xclim_0.37.0

  ci_package_reference :

  xcalc_0.3.2