Working with REMO domains

The domain module should give some tools to work with preconfigured or user defined domains. Domains are defined as xarray datasets that will contain dimensions and coodinates according to CF-conventions. The pyremo domain module actually uses the py-cordex domains module in the background just with another set of tables.

NOTE: Please be aware that a remo domain is usually a little larger than the “official” cordex domain according to the archive specification since the regional model usually accounts for a “buffer” zone where the lateral boundary conditions are nudged.

Working with domain information

import pyremo as pr

The domain module contains some useful functions to work with cordex meta data, e.g., you can get some domain grid information using

pr.domain_info("EUR-11")

Downloading file 'domains.csv' from 'https://raw.githubusercontent.com/remo-rcm/tables/main/domains/domains.csv' to '/home/docs/.pyremo'.

{'domain_id': 'EUR-11',
 'region': 4,
 'domain': 'Europe high-res.',
 'nlon': 433,
 'nlat': 433,
 'll_lon': -28.925,
 'll_lat': -23.925,
 'dlon': 0.11,
 'dlat': 0.11,
 'pollon': -162.0,
 'pollat': 39.25,
 'ur_lon': None,
 'ur_lat': None}

All available cordex domains are in this table:

pr.domains.table

	region	domain	nlon	nlat	ll_lon	ll_lat	dlon	dlat	pollon	pollat	ur_lon	ur_lat
domain_id
AFR-11	5	Africa	801	865	-26.23500	-49.33500	0.1100	0.1100	180.00	90.00	NaN	NaN
AFR-22	5	Africa	401	433	-26.29000	-49.39000	0.2200	0.2200	180.00	90.00	NaN	NaN
AFR-44	5	Africa	217	217	-29.92000	-49.28000	0.4400	0.4400	180.00	90.00	NaN	NaN
AUS-11	9	Australasia	811	541	321.49000	-60.20000	0.1100	0.1100	141.38	60.31	NaN	NaN
AUS-22	9	Australasia	433	271	138.31000	-24.53000	0.2200	0.2200	141.38	60.31	NaN	NaN
AUS-44	9	Australasia	217	145	321.82000	-59.87000	0.4400	0.4400	141.38	60.31	NaN	NaN
CAM-11	2	Central America	865	481	294.09000	-75.63000	0.1100	0.1100	113.98	75.74	NaN	NaN
CAM-22	2	Central America	433	241	-54.45000	-30.47000	0.2200	0.2200	113.98	75.74	NaN	NaN
CAM-44	2	Central America	241	129	-59.84000	-32.12000	0.4400	0.4400	113.98	75.74	NaN	NaN
CAS-11	8	Central Asia	641	433	-36.02500	-22.60500	0.1100	0.1100	-103.39	43.48	NaN	NaN
CAS-22	8	Central Asia	325	217	-36.63000	-22.77000	0.2200	0.2200	-103.39	43.48	NaN	NaN
CAS-44	8	Central Asia	181	121	-40.48000	-25.52000	0.4400	0.4400	-103.39	43.48	NaN	NaN
EAS-11	7	East Asia	811	513	116.41000	-60.89000	0.1100	0.1100	-63.70	61.00	NaN	NaN
EAS-22	7	East Asia	433	271	-47.41000	-24.30000	0.2200	0.2200	296.30	61.00	NaN	NaN
EAS-44	7	East Asia	201	163	476.74000	-60.56000	0.4400	0.4400	296.30	61.00	NaN	NaN
EUR-11	4	Europe high-res.	433	433	-28.92500	-23.92500	0.1100	0.1100	-162.00	39.25	NaN	NaN
EUR-22	4	Europe	241	217	-31.62000	-24.64000	0.2200	0.2200	-162.00	39.25	NaN	NaN
EUR-44	4	Europe	129	121	-31.73000	-26.73000	0.4400	0.4400	-162.00	39.25	NaN	NaN
NAM-11	3	North America	641	541	263.11000	-42.39000	0.1100	0.1100	83.00	42.50	NaN	NaN
NAM-22	3	North America	321	271	-35.31000	-29.83000	0.2200	0.2200	83.00	42.50	NaN	NaN
NAM-44	3	North America	181	151	-39.60000	-33.24000	0.4400	0.4400	83.00	42.50	NaN	NaN
SAM-11	1	South America	601	721	124.05000	-70.49000	0.1100	0.1100	-56.06	70.60	NaN	NaN
SAM-22	1	South America	301	361	142.71000	-41.47000	0.2200	0.2200	-56.06	70.60	NaN	NaN
SAM-44	1	South America	161	181	140.40000	-41.36000	0.4400	0.4400	-56.06	70.60	NaN	NaN
SEA-22	14	South East Asia	289	217	86.40000	-17.82000	0.2200	0.2200	180.00	90.00	NaN	NaN
WAS-22	6	South Asia	401	271	-33.99000	-22.99000	0.2200	0.2200	-123.34	79.95	NaN	NaN
EUR+NA-11	4	Europe + North Atlantic	649	433	18.11000	-39.14000	0.1100	0.1100	-162.00	39.25	NaN	NaN
HUN-11	4	Hungary	301	145	360.11000	-89.89000	0.1100	0.1100	180.00	90.00	NaN	NaN
SASSCAL-22	5	Southern Africa	433	433	360.22000	-89.78000	0.2200	0.2200	180.00	90.00	NaN	NaN
GAR-11	4	Greater Alpine Region	145	129	-14.95500	-12.15500	0.1100	0.1100	-162.00	39.25	NaN	NaN
EUN-0275	4	EUCP Northern Europe	649	811	-9.99125	-0.20625	0.0275	0.0275	-162.00	39.25	NaN	NaN
EUC-0275	4	EUCP Central Europe	361	401	-9.52375	-5.29375	0.0275	0.0275	-162.00	39.25	NaN	NaN
SGAR-0275	4	Greater Alpine Region	289	271	-9.38625	-9.66625	0.0275	0.0275	-162.00	39.25	NaN	NaN
GAR-0275	4	Greater Alpine Region	501	481	-13.92375	-11.67375	0.0275	0.0275	-162.00	39.25	-0.17375	1.52625
MEU-11	4	Mid-Europe	121	121	-10.89000	-6.89000	0.1100	0.1100	-162.00	39.25	NaN	NaN
WAF-11	5	Westafrica	361	271	-20.68000	2.64000	0.1100	0.1100	180.00	90.00	NaN	NaN
MSEA-11	14	South East Asia Mainland	145	181	92.34000	7.12000	0.1100	0.1100	180.00	80.00	NaN	NaN
MOMED-11	12	Mediterranean	487	321	-27.92500	-25.92500	0.1100	0.1100	198.00	39.25	NaN	NaN
HYDRASIA-11	8	Central Asia	257	271	-20.02500	-20.60500	0.1100	0.1100	-103.39	43.48	NaN	NaN
GUI-0275	5	Guinea	811	321	-17.03625	4.08375	0.0275	0.0275	180.00	90.00	5.23875	12.88375
ALPX-3	4	Extended Alpine Region	721	577	-18.62625	-11.20625	0.0275	0.0275	-162.00	39.25	NaN	NaN
NSEA-3	4	North Sea	649	541	-15.98625	-0.97625	0.0275	0.0275	-162.00	39.25	NaN	NaN

EUR-11 example

The heart of the module are some functions that create a dataset from the grid information, e.g.

eur11 = pr.remo_domain("EUR-11", dummy="topo")
eur11

/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:671: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  unused_keys = set(attribute.keys()) - set(inverted)
/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:672: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  for key, value in attribute.items():

<xarray.Dataset> Size: 4MB
Dimensions:                     (rlon: 433, rlat: 433)
Coordinates:
  * rlon                        (rlon) float64 3kB -28.93 -28.82 ... 18.48 18.59
  * rlat                        (rlat) float64 3kB -23.93 -23.82 ... 23.48 23.59
    lon                         (rlat, rlon) float64 1MB -10.32 -10.23 ... 68.65
    lat                         (rlat, rlon) float64 1MB 21.28 21.32 ... 67.8
Data variables:
    rotated_latitude_longitude  int32 4B 0
    topo                        (rlat, rlon) float32 750kB 283.0 283.0 ... 44.67
Attributes:
    CORDEX_domain:  EUR-11

xarray.Dataset

• Dimensions:
  • rlon: 433
  • rlat: 433
• Coordinates: (4)
  • rlon
    (rlon)
    float64
    -28.93 -28.82 -28.7 ... 18.48 18.59

    axis :

    X

    standard_name :

    grid_longitude

    long_name :

    longitude in rotated pole grid

    units :

    degrees

    array([-28.925, -28.815, -28.705, ...,  18.375,  18.485,  18.595])

  • rlat
    (rlat)
    float64
    -23.93 -23.82 -23.7 ... 23.48 23.59

    axis :

    Y

    standard_name :

    grid_latitude

    long_name :

    latitude in rotated pole grid

    units :

    degrees

    array([-23.925, -23.815, -23.705, ...,  23.375,  23.485,  23.595])

  • lon
    (rlat, rlon)
    float64
    -10.32 -10.23 ... 68.45 68.65

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array([[-10.32442512, -10.22557479, -10.12663651, ...,  36.46573407,
             36.57217046,  36.67853771],
           [-10.37190917, -10.27291987, -10.17384224, ...,  36.49820688,
             36.60481678,  36.71135721],
           [-10.41945863, -10.32033034, -10.22111334, ...,  36.53073482,
             36.63751844,  36.74423223],
           ...,
           [-47.63250823, -47.49968336, -47.36633272, ...,  67.86970912,
             68.07308532,  68.27546488],
           [-47.80633127, -47.67374748, -47.54063732, ...,  68.0578613 ,
             68.26127181,  68.46367911],
           [-47.98092089, -47.84858288, -47.71571792, ...,  68.24737075,
             68.45080848,  68.6532364 ]])

  • lat
    (rlat, rlon)
    float64
    21.28 21.32 21.36 ... 67.86 67.8

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    array([[21.28237624, 21.32272786, 21.36294993, ..., 24.53040305,
            24.50334731, 24.47614161],
           [21.38309194, 21.42350563, 21.46378965, ..., 24.63636539,
            24.60926374, 24.58201193],
           [21.48379427, 21.52427009, 21.56461611, ..., 24.74232074,
            24.71517313, 24.68787514],
           ...,
           [60.83106194, 60.90861605, 60.98608811, ..., 67.76169805,
            67.69647123, 67.63105083],
           [60.90133622, 60.97899695, 61.05657631, ..., 67.84564802,
            67.78024163, 67.71464238],
           [60.97138542, 61.04915251, 61.12683894, ..., 67.92938064,
            67.86379409, 67.79801543]])

• Data variables: (2)
  • rotated_latitude_longitude
    ()
    int32
    0

    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(0, dtype=int32)

  • topo
    (rlat, rlon)
    float32
    283.0 283.0 283.0 ... 136.0 44.67

    coordinates :

    lat lon

    grid_mapping :

    rotated_latitude_longitude

    units :

    m

    array([[  283.       ,   283.       ,   283.       , ..., -1181.       ,
             -996.6667   , -1100.       ],
           [  283.       ,   283.       ,   283.       , ..., -1181.       ,
             -996.6667   ,  -996.6667   ],
           [  283.       ,   284.       ,   284.       , ...,  -996.6667   ,
             -996.6667   ,  -996.6667   ],
           ...,
           [   -1.3333334,   -15.       ,   -15.       , ...,   116.       ,
              136.       ,   136.       ],
           [   -1.3333334,    -1.3333334,   690.       , ...,   136.       ,
              136.       ,   136.       ],
           [   -1.3333334,   690.       ,   690.       , ...,   136.       ,
              136.       ,    44.666668 ]], dtype=float32)

• Indexes: (2)
  • rlon
    PandasIndex

    PandasIndex(Index([-28.925, -28.815, -28.705, -28.595, -28.485, -28.375, -28.265, -28.155,
           -28.045, -27.935,
           ...
            17.605,  17.715,  17.825,  17.935,  18.045,  18.155,  18.265,  18.375,
            18.485,  18.595],
          dtype='float64', name='rlon', length=433))

  • rlat
    PandasIndex

    PandasIndex(Index([-23.925, -23.815, -23.705, -23.595, -23.485, -23.375, -23.265, -23.155,
           -23.045, -22.935,
           ...
            22.605,  22.715,  22.825,  22.935,  23.045,  23.155,  23.265,  23.375,
            23.485,  23.595],
          dtype='float64', name='rlat', length=433))

• Attributes: (1)

  CORDEX_domain :

  EUR-11

The dummy='topo' argument means, we want a dummy variable in the dataset to see how the domain looks like. For the dummy topography, we use the cdo topo operator in the background. So maybe you have to install python-cdo, e.g., conda install -c conda-forge python-cdo. Working with xarray datasets means, that we can use all the nice functions of xarray including plotting, e.g.,

eur11.topo.plot(cmap="terrain")

Matplotlib is building the font cache; this may take a moment.

<matplotlib.collections.QuadMesh at 0x7fa9e89c6fb0>

eur11.topo.plot(x="lon", y="lat", cmap="terrain")

<matplotlib.collections.QuadMesh at 0x7fa9e8885780>

Let’s define a slightly more sophisticated plotting function that uses cartopy for the right projection with a rotated pole:

def plot(da, pole, vmin=None, vmax=None, borders=True, title=""):
    """plot a domain using the right projection with cartopy"""
    %matplotlib inline
    import cartopy.crs as ccrs
    import cartopy.feature as cf
    import matplotlib.pyplot as plt

    plt.figure(figsize=(20, 10))
    projection = ccrs.PlateCarree()
    transform = ccrs.RotatedPole(pole_latitude=pole[1], pole_longitude=pole[0])
    # ax = plt.axes(projection=projection)
    ax = plt.axes(projection=transform)
    # ax.set_extent([ds_sub.rlon.min(), ds_sub.rlon.max(), ds_sub.rlat.min(), ds_sub.rlat.max()], crs=transform)
    ax.gridlines(
        draw_labels=True,
        linewidth=0.5,
        color="gray",
        xlocs=range(-180, 180, 10),
        ylocs=range(-90, 90, 5),
    )
    da.plot(
        ax=ax,
        cmap="terrain",
        transform=transform,
        vmin=vmin,
        vmax=vmax,
        x="rlon",
        y="rlat",
    )
    ax.coastlines(resolution="50m", color="black", linewidth=1)
    if borders:
        ax.add_feature(cf.BORDERS)
    ax.set_title("")

pole_eur = (
    eur11.rotated_latitude_longitude.grid_north_pole_longitude,
    eur11.rotated_latitude_longitude.grid_north_pole_latitude,
)
pole_eur

(-162.0, 39.25)

plot(eur11.topo, pole_eur)

/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_boundary_lines_land.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

User defined domain

The domains are created using the create_dataset function from the `py-cordex <https://py-cordex.readthedocs.io>`__ package, e.g.:

from cordex import create_dataset

Let’s create the EUR-11 domain manually from the numbers in the table:

eur11_user = create_dataset(
    nlon=433,
    nlat=433,
    dlon=0.11,
    dlat=0.11,
    ll_lon=-28.925,
    ll_lat=-23.925,
    pollon=-162.00,
    pollat=39.25,
    dummy="topo",
)

/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:671: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  unused_keys = set(attribute.keys()) - set(inverted)
/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:672: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  for key, value in attribute.items():

We can check that this gives the same result as our preconfigured domain.

eur11_user.equals(eur11)

True

You can now use the create_dataset function to create any domain as an xarray dataset.

afr11 = pr.remo_domain("AFR-11", dummy="topo")
afr11

/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:671: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  unused_keys = set(attribute.keys()) - set(inverted)
/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:672: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  for key, value in attribute.items():

<xarray.Dataset> Size: 14MB
Dimensions:                     (rlon: 801, rlat: 865)
Coordinates:
  * rlon                        (rlon) float64 6kB -26.23 -26.12 ... 61.66 61.77
  * rlat                        (rlat) float64 7kB -49.34 -49.23 ... 45.6 45.71
    lon                         (rlat, rlon) float64 6MB -26.23 -26.13 ... 61.77
    lat                         (rlat, rlon) float64 6MB -49.34 -49.34 ... 45.71
Data variables:
    rotated_latitude_longitude  int32 4B 0
    topo                        (rlat, rlon) float32 3MB -4.646e+03 ... 56.0
Attributes:
    CORDEX_domain:  AFR-11

xarray.Dataset

• Dimensions:
  • rlon: 801
  • rlat: 865
• Coordinates: (4)
  • rlon
    (rlon)
    float64
    -26.23 -26.12 ... 61.66 61.77

    axis :

    X

    standard_name :

    grid_longitude

    long_name :

    longitude in rotated pole grid

    units :

    degrees

    array([-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765])

  • rlat
    (rlat)
    float64
    -49.34 -49.23 -49.12 ... 45.6 45.71

    axis :

    Y

    standard_name :

    grid_latitude

    long_name :

    latitude in rotated pole grid

    units :

    degrees

    array([-49.335, -49.225, -49.115, ...,  45.485,  45.595,  45.705])

  • lon
    (rlat, rlon)
    float64
    -26.23 -26.13 ... 61.66 61.77

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array([[-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765],
           [-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765],
           [-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765],
           ...,
           [-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765],
           [-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765],
           [-26.235, -26.125, -26.015, ...,  61.545,  61.655,  61.765]])

  • lat
    (rlat, rlon)
    float64
    -49.34 -49.34 ... 45.71 45.71

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    array([[-49.335, -49.335, -49.335, ..., -49.335, -49.335, -49.335],
           [-49.225, -49.225, -49.225, ..., -49.225, -49.225, -49.225],
           [-49.115, -49.115, -49.115, ..., -49.115, -49.115, -49.115],
           ...,
           [ 45.485,  45.485,  45.485, ...,  45.485,  45.485,  45.485],
           [ 45.595,  45.595,  45.595, ...,  45.595,  45.595,  45.595],
           [ 45.705,  45.705,  45.705, ...,  45.705,  45.705,  45.705]])

• Data variables: (2)
  • rotated_latitude_longitude
    ()
    int32
    0

    grid_mapping_name :

    rotated_latitude_longitude

    grid_north_pole_latitude :

    90.0

    grid_north_pole_longitude :

    180.0

    north_pole_grid_longitude :

    0.0

    array(0, dtype=int32)

  • topo
    (rlat, rlon)
    float32
    -4.646e+03 -4.646e+03 ... 56.0 56.0

    coordinates :

    lat lon

    grid_mapping :

    rotated_latitude_longitude

    units :

    m

    array([[-4645.6665, -4645.6665, -4645.6665, ..., -3140.    , -3140.    ,
            -3140.    ],
           [-4645.6665, -4645.6665, -4645.6665, ..., -3140.    , -3140.    ,
            -3140.    ],
           [-4645.6665, -4645.6665, -4645.6665, ..., -3140.    , -3140.    ,
            -3140.    ],
           ...,
           [-2889.3333, -2889.3333, -2889.3333, ...,    60.    ,    60.    ,
               60.    ],
           [-2838.    , -2838.    , -2838.    , ...,    56.    ,    56.    ,
               56.    ],
           [-2838.    , -2838.    , -2838.    , ...,    56.    ,    56.    ,
               56.    ]], dtype=float32)

• Indexes: (2)
  • rlon
    PandasIndex

    PandasIndex(Index([          -26.235,           -26.125,           -26.015,
                     -25.905,           -25.795,           -25.685,
                     -25.575,           -25.465,           -25.355,
                     -25.245,
           ...
           60.77500000000001, 60.88500000000001,            60.995,
                      61.105,            61.215,            61.325,
                      61.435,            61.545,            61.655,
                      61.765],
          dtype='float64', name='rlon', length=801))

  • rlat
    PandasIndex

    PandasIndex(Index([          -49.335,           -49.225,           -49.115,
                     -49.005,           -48.895,           -48.785,
                     -48.675,           -48.565,           -48.455,
                     -48.345,
           ...
                      44.715,            44.825,            44.935,
           45.04499999999999, 45.15499999999999, 45.26499999999999,
           45.37499999999999, 45.48500000000001, 45.59500000000001,
           45.70500000000001],
          dtype='float64', name='rlat', length=865))

• Attributes: (1)

  CORDEX_domain :

  AFR-11

pole_afr = (
    afr11.rotated_latitude_longitude.grid_north_pole_longitude,
    afr11.rotated_latitude_longitude.grid_north_pole_latitude,
)
pole_afr

(180.0, 90.0)

plot(afr11.topo, pole_afr)

Cropping the REMO domain

Sometimes it might be neccessary to crop the REMO data to the official CORDEX grid size, e.g., for cmorization. This can now easily be done like this:

import numpy as np
from cordex import cordex_domain

eur11_cordex = cordex_domain("EUR-11", dummy="topo")

Downloading file 'rotated-latitude-longitude.csv' from 'https://raw.githubusercontent.com/WCRP-CORDEX/domain-tables/main/rotated-latitude-longitude.csv' to '/home/docs/.py-cordex'.
/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:671: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  unused_keys = set(attribute.keys()) - set(inverted)
/home/docs/checkouts/readthedocs.org/user_builds/pyremo/conda/latest/lib/python3.10/site-packages/cf_xarray/accessor.py:672: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  for key, value in attribute.items():

# crop = eur11.sel(rlon=slice(eur11_cordex.rlon.min(), eur11_cordex.rlon.max()), rlat=slice(eur11_cordex.rlat.min(), eur11_cordex.rlat.max()))
crop = eur11.sel(rlon=eur11_cordex.rlon, rlat=eur11_cordex.rlat, method="nearest")

plot(crop.topo, pole_eur)

crop

<xarray.Dataset> Size: 4MB
Dimensions:                     (rlon: 424, rlat: 412)
Coordinates:
  * rlon                        (rlon) float64 3kB -28.38 -28.27 ... 18.05 18.16
  * rlat                        (rlat) float64 3kB -23.38 -23.27 ... 21.73 21.84
    lon                         (rlat, rlon) float64 1MB -10.06 -9.964 ... 64.96
    lat                         (rlat, rlon) float64 1MB 21.99 22.03 ... 66.69
Data variables:
    rotated_latitude_longitude  int32 4B 0
    topo                        (rlat, rlon) float32 699kB 284.0 246.0 ... 509.0
Attributes:
    CORDEX_domain:  EUR-11

xarray.Dataset

• Dimensions:
  • rlon: 424
  • rlat: 412
• Coordinates: (4)
  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.05 18.16

    axis :

    X

    standard_name :

    grid_longitude

    long_name :

    longitude in rotated pole grid

    units :

    degrees

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

  • rlat
    (rlat)
    float64
    -23.38 -23.27 ... 21.73 21.84

    axis :

    Y

    standard_name :

    grid_latitude

    long_name :

    latitude in rotated pole grid

    units :

    degrees

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

  • lon
    (rlat, rlon)
    float64
    -10.06 -9.964 ... 64.76 64.96

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array([[-10.06387966,  -9.96388628,  -9.86380363, ...,  36.19872654,
             36.30631294,  36.41382968],
           [-10.11099589, -10.01086154,  -9.91063756, ...,  36.23077703,
             36.33853934,  36.44623165],
           [-10.15817955, -10.05790422,  -9.95753886, ...,  36.26288362,
             36.37082205,  36.47869015],
           ...,
           [-44.26744553, -44.1287729 , -43.98957131, ...,  64.21746364,
             64.42305922,  64.62774455],
           [-44.43028713, -44.29178077, -44.15274426, ...,  64.38488381,
             64.59063409,  64.79546746],
           [-44.59386389, -44.45552794, -44.31666067, ...,  64.55349992,
             64.75940009,  64.96437667]])

  • lat
    (rlat, rlon)
    float64
    21.99 22.03 22.07 ... 66.75 66.69

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    array([[21.98782876, 22.02783807, 22.06771602, ..., 25.1677739 ,
            25.14109405, 25.11426239],
           [22.08878725, 22.1288583 , 22.16879786, ..., 25.27388366,
            25.24715843, 25.22028117],
           [22.18973223, 22.22986507, 22.2698663 , ..., 25.37998647,
            25.3532158 , 25.32629289],
           ...,
           [60.05553554, 60.13088567, 60.20613923, ..., 66.63852915,
            66.57692365, 66.51510791],
           [60.12974984, 60.20521265, 60.28057952, ..., 66.72632651,
            66.66454929, 66.60256233],
           [60.20376337, 60.27933874, 60.35481882, ..., 66.81394504,
            66.75199541, 66.68983654]])

• Data variables: (2)
  • rotated_latitude_longitude
    ()
    int32
    0

    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(0, dtype=int32)

  • topo
    (rlat, rlon)
    float32
    284.0 246.0 246.0 ... 509.0 509.0

    coordinates :

    lat lon

    grid_mapping :

    rotated_latitude_longitude

    units :

    m

    array([[ 2.8400000e+02,  2.4600000e+02,  2.4600000e+02, ...,
            -1.6953334e+03, -1.6953334e+03, -1.6953334e+03],
           [ 2.8066666e+02,  2.8066666e+02,  2.4600000e+02, ...,
            -1.6953334e+03, -1.6953334e+03, -1.6953334e+03],
           [ 2.8066666e+02,  2.8066666e+02,  2.4600000e+02, ...,
            -1.6953334e+03, -1.6953334e+03, -1.6953334e+03],
           ...,
           [-6.6666669e-01, -6.6666669e-01,  4.6666665e+00, ...,
             5.3600000e+02,  5.3600000e+02,  5.0900000e+02],
           [-6.6666669e-01, -6.6666669e-01, -6.6666669e-01, ...,
             5.3600000e+02,  5.0900000e+02,  5.0900000e+02],
           [ 2.7966666e+02, -6.6666669e-01, -6.6666669e-01, ...,
             5.0900000e+02,  5.0900000e+02,  5.0900000e+02]], dtype=float32)

• Indexes: (2)
  • rlon
    PandasIndex

    PandasIndex(Index([-28.375, -28.265, -28.155, -28.045, -27.935, -27.825, -27.715, -27.605,
           -27.495, -27.385,
           ...
            17.165,  17.275,  17.385,  17.495,  17.605,  17.715,  17.825,  17.935,
            18.045,  18.155],
          dtype='float64', name='rlon', length=424))

  • rlat
    PandasIndex

    PandasIndex(Index([-23.375, -23.265, -23.155, -23.045, -22.935, -22.825, -22.715, -22.605,
           -22.495, -22.385,
           ...
            20.845,  20.955,  21.065,  21.175,  21.285,  21.395,  21.505,  21.615,
            21.725,  21.835],
          dtype='float64', name='rlat', length=412))

• Attributes: (1)

  CORDEX_domain :

  EUR-11