Cordex 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.

NOTE: The domain module mostly focuses on working with rotated cordex domains and how they are defined in the cordex archive specifications. However, there are some regional models that use different mappings instead of rotated_pole or rotated_latitude_longitude which we focus on. Any expertise working with those different mappings is highly welcome!

Working with domain information

import cordex as cx

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

cx.domain_info("EUR-11")

Downloading file 'rotated-latitude-longitude.csv' from 'https://raw.githubusercontent.com/WCRP-CORDEX/domain-tables/main/rotated-latitude-longitude.csv' to '/Users/runner/.py-cordex'.

{'short_name': 'EUR-11',
 'region': 4,
 'domain_id': 'EUR-12',
 'domain': 'Europe',
 'CORDEX_domain': 'EUR-11',
 'long_name': 'Europe',
 'nlon': 424,
 'nlat': 412,
 'll_lon': -28.375,
 'ur_lon': 18.155,
 'll_lat': -23.375,
 'ur_lat': 21.835,
 'dlon': 0.11,
 'dlat': 0.11,
 'pollon': -162.0,
 'pollat': 39.25}

The domain information is stored in a number of csv tables. The module contains a tables dictionary that sorts the tables by resolution or project, e.g.

cx.domains.tables.keys()

Index(['region', 'domain_id', 'domain', 'CORDEX_domain', 'long_name', 'nlon',
       'nlat', 'll_lon', 'ur_lon', 'll_lat', 'ur_lat', 'dlon', 'dlat',
       'pollon', 'pollat'],
      dtype='object')

All available cordex domains are in those tables:

cx.domains.table

	region	domain_id	domain	CORDEX_domain	long_name	nlon	nlat	ll_lon	ur_lon	ll_lat	ur_lat	dlon	dlat	pollon	pollat
short_name
SAM-44	1	SAM-50	South America	SAM-44	South America	146	167	143.92000	207.72000	-38.28000	34.76000	0.4400	0.4400	-56.06	70.60
CAM-44	2	CAM-50	Central America	CAM-44	Central America	210	113	-52.80000	39.16000	-28.60000	20.68000	0.4400	0.4400	113.98	75.74
NAM-44	3	NAM-50	North America	NAM-44	North America	155	130	-33.88000	33.88000	-28.40000	28.36000	0.4400	0.4400	83.00	42.50
EUR-44	4	EUR-50	Europe	EUR-44	Europe	106	103	-28.21000	17.99000	-23.21000	21.67000	0.4400	0.4400	-162.00	39.25
AFR-44	5	AFR-50	Africa	AFR-44	Africa	194	201	-24.64000	60.28000	-45.76000	42.24000	0.4400	0.4400	180.00	90.00
WAS-44	6	WAS-50	South Asia	WAS-44	South Asia	193	130	-32.12000	52.36000	-21.56000	35.20000	0.4400	0.4400	-123.34	79.95
EAS-44	7	EAS-50	East Asia	EAS-44	East Asia	203	167	-40.92000	47.96000	-26.84000	46.20000	0.4400	0.4400	-64.78	77.61
CAS-44	8	CAS-50	Central Asia	CAS-44	Central Asia	153	100	-34.32000	32.56000	-20.68000	22.88000	0.4400	0.4400	-103.39	43.48
AUS-44	9	AUS-50	Australasia	AUS-44	Australasia	200	129	142.16000	229.72000	-22.88000	33.44000	0.4400	0.4400	141.38	60.31
ANT-44	10	ANT-50	Antarctica	ANT-44	Antarctica	125	97	152.72000	207.28000	-27.72000	14.52000	0.4400	0.4400	-166.92	6.08
ARC-44	11	ARC-50	Arctic	ARC-44	Arctic	116	133	-22.88000	27.72000	-24.20000	33.88000	0.4400	0.4400	0.00	6.55
MED-44	12	MED-50	Mediterranean	MED-44	Mediterranean	98	63	-23.22000	19.46000	-21.34000	5.94000	0.4400	0.4400	198.00	39.25
MNA-44	13	MNA-50	Middle East and North Africa	MNA-44	Middle East and North Africa	232	118	-26.40000	75.24000	-6.60000	44.88000	0.4400	0.4400	180.00	90.00
MNA-22	13	MNA-25	Middle East and North Africa	MNA-22	Middle East and North Africa	464	236	-26.51000	75.35000	-6.71000	44.99000	0.2200	0.2200	180.00	90.00
SAM-11	1	SAM-12	South America	SAM-11	South America	584	668	143.75500	207.88500	-38.44500	34.92500	0.1100	0.1100	-56.06	70.60
CAM-11	2	CAM-12	Central America	CAM-11	Central America	840	452	-52.96500	39.32500	-28.76500	20.84500	0.1100	0.1100	113.98	75.74
NAM-11	3	NAM-12	North America	NAM-11	North America	620	520	-34.04500	34.04500	-28.56500	28.52500	0.1100	0.1100	83.00	42.50
EUR-11	4	EUR-12	Europe	EUR-11	Europe	424	412	-28.37500	18.15500	-23.37500	21.83500	0.1100	0.1100	-162.00	39.25
AFR-11	5	AFR-12	Africa	AFR-11	Africa	776	804	-24.80500	60.44500	-45.92500	42.40500	0.1100	0.1100	180.00	90.00
WAS-11	6	WAS-12	South Asia	WAS-11	South Asia	772	520	-32.28500	52.52500	-21.72500	35.36500	0.1100	0.1100	-123.34	79.95
EAS-11	7	EAS-12	East Asia	EAS-11	East Asia	812	668	-41.08500	48.12500	-27.00500	46.36500	0.1100	0.1100	-64.78	77.61
CAS-11	8	CAS-12	Central Asia	CAS-11	Central Asia	612	400	-34.48500	32.72500	-20.84500	23.04500	0.1100	0.1100	-103.39	43.48
AUS-11	9	AUS-12	Australasia	AUS-11	Australasia	800	516	141.99500	229.88500	-23.04500	33.60500	0.1100	0.1100	141.38	60.31
ANT-11	10	ANT-12	Antarctica	ANT-11	Antarctica	500	388	152.55500	207.44500	-27.88500	14.68500	0.1100	0.1100	-166.92	6.08
ARC-11	11	ARC-12	Arctic	ARC-11	Arctic	464	532	-23.04500	27.88500	-24.36500	34.04500	0.1100	0.1100	0.00	6.55
MED-11	12	MED-12	Mediterranean	MED-11	Mediterranean	392	252	-23.38500	19.62500	-21.50500	6.10500	0.1100	0.1100	198.00	39.25
MNA-11	13	MNA-12	Middle East and North Africa	MNA-11	Middle East and North Africa	928	472	-26.56500	75.40500	-6.76500	45.04500	0.1100	0.1100	180.00	90.00
GAR-0275	4	GAR-3	Greater Alpine Region	GAR-0275	Greater Alpine Region	476	444	-13.51125	-0.44875	-10.93125	1.25125	0.0275	0.0275	-162.00	39.25
CEU-0275	4	CEU-3	Central Europe	CEU-0275	Central Europe	345	385	-9.30375	NaN	-5.07375	NaN	0.0275	0.0275	-162.00	39.25
SAM-22	1	SAM-25	South America	SAM-22	South America	292	334	143.81000	207.83000	-38.39000	34.87000	0.2200	0.2200	-56.06	70.60
CAM-22	2	CAM-25	Central America	CAM-22	Central America	420	226	-52.91000	39.27000	-28.71000	20.79000	0.2200	0.2200	113.98	75.74
NAM-22	3	NAM-25	North America	NAM-22	North America	310	260	-33.99000	33.99000	-28.51000	28.47000	0.2200	0.2200	83.00	42.50
EUR-22	4	EUR-25	Europe	EUR-22	Europe	212	206	-28.32000	18.10000	-23.32000	21.78000	0.2200	0.2200	-162.00	39.25
AFR-22	5	AFR-25	Africa	AFR-22	Africa	388	402	-24.75000	60.39000	-45.87000	42.35000	0.2200	0.2200	180.00	90.00
WAS-22	6	WAS-25	South Asia	WAS-22	South Asia	386	260	-32.23000	52.47000	-21.67000	35.31000	0.2200	0.2200	-123.34	79.95
EAS-22	7	EAS-25	East Asia	EAS-22	East Asia	396	251	-43.23000	43.67000	-22.10000	32.90000	0.2200	0.2200	-64.78	77.61
CAS-22	8	CAS-25	Central Asia	CAS-22	Central Asia	306	200	-34.43000	32.67000	-20.79000	22.99000	0.2200	0.2200	-103.39	43.48
AUS-22	9	AUS-25	Australasia	AUS-22	Australasia	400	258	142.05000	229.83000	-22.99000	33.55000	0.2200	0.2200	141.38	60.31
SEA-22	14	SEA-25	South East Asia	SEA-22	South East Asia	264	194	89.26000	147.12000	-15.18000	27.28000	0.2200	0.2200	180.00	90.00
SAM-44i	1	SAM-50i	South America	SAM-44i	South America	181	155	-106.25000	-16.25000	-58.25000	18.75000	0.5000	0.5000	NaN	NaN
CAM-44i	2	CAM-50i	Central America	CAM-44i	Central America	207	111	-124.75000	-21.75000	-19.75000	35.25000	0.5000	0.5000	NaN	NaN
NAM-44i	3	NAM-50i	North America	NAM-44i	North America	300	129	-171.75000	-22.25000	12.25000	76.25000	0.5000	0.5000	NaN	NaN
EUR-44i	4	EUR-50i	Europe	EUR-44i	Europe	221	103	-44.75000	65.25000	21.75000	72.75000	0.5000	0.5000	NaN	NaN
AFR-44i	5	AFR-50i	Africa	AFR-44i	Africa	173	179	-25.25000	60.75000	-46.25000	42.75000	0.5000	0.5000	NaN	NaN
WAS-44i	6	WAS-50i	South Asia	WAS-44i	South Asia	195	124	19.25000	116.25000	-15.75000	45.75000	0.5000	0.5000	NaN	NaN
EAS-44i	7	EAS-50i	East Asia	EAS-44i	East Asia	227	157	62.75000	175.75000	-18.75000	59.25000	0.5000	0.5000	NaN	NaN
CAS-44i	8	CAS-50i	Central Asia	CAS-44i	Central Asia	260	133	10.75000	140.25000	17.75000	69.75000	0.5000	0.5000	NaN	NaN
AUS-44i	9	AUS-50i	Australasia	AUS-44i	Australasia	238	133	88.75000	207.25000	-53.25000	12.75000	0.5000	0.5000	NaN	NaN
ANT-44i	10	ANT-50i	Antarctica	ANT-44i	Antarctica	720	70	-179.75000	179.75000	-89.75000	-55.25000	0.5000	0.5000	NaN	NaN
ARC-44i	11	ARC-50i	Arctic	ARC-44i	Arctic	720	83	-179.75000	179.75000	48.75000	89.75000	0.5000	0.5000	NaN	NaN
MED-44i	12	MED-50i	Mediterranean	MED-44i	Mediterranean	144	65	-20.75000	51.75000	25.25000	57.25000	0.5000	0.5000	NaN	NaN
MNA-44i	13	MNA-50i	Middle East and North Africa	MNA-44i	Middle East and North Africa	206	106	-26.75000	75.75000	-7.25000	45.25000	0.5000	0.5000	NaN	NaN
MNA-22i	13	MNA-25i	Middle East and North Africa high res.	MNA-22i	Middle East and North Africa high res.	410	209	-26.62500	75.62500	-6.87500	45.12500	0.2500	0.2500	NaN	NaN
EUR-11i	4	EUR-12i	Europe high res.	EUR-11i	Europe high res.	881	408	-44.81250	65.18750	21.81250	72.68750	0.1250	0.1250	NaN	NaN
SEA-22i	14	SEA-25i	South East Asia	SEA-22i	South East Asia	233	172	89.12500	147.12500	-15.37500	27.37500	0.2500	0.2500	NaN	NaN

EUR-11 example

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

eur11 = cx.cordex_domain("EUR-11", dummy="topo")
eur11

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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: 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

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. The latest versions of py-cordex also come with an xarray accessor that allows you, e.g., to get a quick overview of the domain, using, e.g.

# from matplotlib import pyplot as plt

# plt.figure(figsize=(8,8))
eur11.cx.map()

<GeoAxes: >

/Users/runner/micromamba-root/envs/cordex-tutorials/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

/Users/runner/micromamba-root/envs/cordex-tutorials/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)

/Users/runner/micromamba-root/envs/cordex-tutorials/lib/python3.10/site-packages/cartopy/mpl/style.py:76: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '
/Users/runner/micromamba-root/envs/cordex-tutorials/lib/python3.10/site-packages/cartopy/mpl/style.py:76: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '

/Users/runner/micromamba-root/envs/cordex-tutorials/lib/python3.10/site-packages/cartopy/mpl/style.py:76: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '

We can also use the dummy topography to plot an overview:

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

<matplotlib.collections.QuadMesh at 0x144da9cc0>

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

<matplotlib.collections.QuadMesh at 0x144e61c30>

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=None):
    """plot a domain using the right projection with cartopy"""
    import cartopy.crs as ccrs
    import cartopy.feature as cf
    import matplotlib.pyplot as plt

    plt.figure(figsize=(10, 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)
    if title is not None:
        ax.set_title(title)

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

(-162.0, 39.25)

plot(eur11.topo, pole)

/Users/runner/micromamba-root/envs/cordex-tutorials/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)

User defined domain

The domains are actually created from a csv table. The domains are created using the create_dataset function, in which the data from the table is fed, e.g.:

cx.create_dataset?

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

cx.domains.table.loc["EUR-11"]

region                4
domain_id        EUR-12
domain           Europe
CORDEX_domain    EUR-11
long_name        Europe
nlon                424
nlat                412
ll_lon          -28.375
ur_lon           18.155
ll_lat          -23.375
ur_lat           21.835
dlon               0.11
dlat               0.11
pollon           -162.0
pollat            39.25
Name: EUR-11, dtype: object

eur11_user = cx.create_dataset(
    nlon=424,
    nlat=412,
    dlon=0.11,
    dlat=0.11,
    ll_lon=-28.375,
    ll_lat=-23.375,
    pollon=-162.00,
    pollat=39.25,
    dummy="topo",
)

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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.

Plot all cordex-core domains

We need a slightly modified plotting routine for this:

def plots(dsets, vmin=None, vmax=None, borders=True, title=None):
    """plot a domain using the right projection with cartopy"""
    import cartopy.crs as ccrs
    import cartopy.feature as cf
    import matplotlib.patheffects as pe
    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=projection)
    # 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, 15),
        ylocs=range(-90, 90, 10),
    )
    # path_effects = [pe.Stroke(linewidth=50, foreground='g'), pe.Normal()]
    for ds in dsets:
        pole = (
            ds.rotated_latitude_longitude.grid_north_pole_longitude,
            ds.rotated_latitude_longitude.grid_north_pole_latitude,
        )
        transform = ccrs.RotatedPole(pole_latitude=pole[1], pole_longitude=pole[0])
        ds.topo.plot(
            ax=ax,
            cmap="terrain",
            transform=transform,
            vmin=vmin,
            vmax=vmax,
            x="rlon",
            y="rlat",
            add_colorbar=False,
        )
    ax.coastlines(resolution="50m", color="black", linewidth=1)
    if borders:
        ax.add_feature(cf.BORDERS)
    if title is not None:
        ax.set_title("")

Now, let’s plot all cordex core domains into one overview. We will identify them by their resolution:

table = cx.domains.table

plots(
    [
        cx.cordex_domain(name, dummy="topo")
        for name in table.index
        if table.loc[name].dlon == 0.22
    ],
    borders=False,
)

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

/Users/runner/micromamba-root/envs/cordex-tutorials/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)
/Users/runner/micromamba-root/envs/cordex-tutorials/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():

Using the grid information for interpolation

The gridded Cordex datasets are particular usefule for regridding either with CDO or other interpolation packages.

We will use some CMIP6 model data from the ESGF to show how we can do the regridding:

!wget https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc

--2024-04-09 16:29:42--  https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 

200 OK
Length: 

299137 (292K) [application/octet-stream]
Saving to: ‘orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc.1’


          orog_fx_M   0%[                    ]       0  --.-KB/s               
orog_fx_MPI-ESM1-2- 100%[===================>] 292.13K  --.-KB/s    in 0.006s  

2024-04-09 16:29:42 (48.5 MB/s) - ‘orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc.1’ saved [299137/299137]

import xarray as xr

# https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc
ds = xr.open_dataset("orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc")
# ds = xr.open_dataset(
#    "https://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-HR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-HR_historical_r1i1p1f1_gn_199501-199912.nc"
# )
# ds = xr.open_dataset(
#    "http://esgf-data3.ceda.ac.uk/thredds/dodsC/esg_cmip6/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon/tas/gn/v20190406/tas_Amon_UKESM1-0-LL_historical_r1i1p1f2_gn_185001-194912.nc"
# )
ds

<xarray.Dataset> Size: 309kB
Dimensions:   (lat: 192, bnds: 2, lon: 384)
Coordinates:
  * lat       (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28
  * lon       (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1
Dimensions without coordinates: bnds
Data variables:
    lat_bnds  (lat, bnds) float64 3kB ...
    lon_bnds  (lon, bnds) float64 6kB ...
    orog      (lat, lon) float32 295kB ...
Attributes: (12/47)
    Conventions:            CF-1.7 CMIP-6.2
    activity_id:            CMIP
    branch_method:          standard
    branch_time_in_child:   0.0
    branch_time_in_parent:  0.0
    contact:                cmip6-mpi-esm@dkrz.de
    ...                     ...
    title:                  MPI-ESM1-2-HR output prepared for CMIP6
    tracking_id:            hdl:21.14100/3341d289-1282-489f-9419-c263718da247
    variable_id:            orog
    variant_label:          r1i1p1f1
    license:                CMIP6 model data produced by MPI-M is licensed un...
    cmor_version:           3.5.0

xarray.Dataset

• Dimensions:
  • lat: 192
  • bnds: 2
  • lon: 384
• Coordinates: (2)
  • lat
    (lat)
    float64
    -89.28 -88.36 ... 88.36 89.28

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,
           -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,
           -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,
           -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,
           -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,
           -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,
           -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,
           -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,
           -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,
           -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,
           -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,
           -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,
           -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,
           -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,
           -10.75321 ,  -9.818148,  -8.883087,  -7.948025,  -7.012963,  -6.077901,
            -5.14284 ,  -4.207778,  -3.272716,  -2.337654,  -1.402593,  -0.467531,
             0.467531,   1.402593,   2.337654,   3.272716,   4.207778,   5.14284 ,
             6.077901,   7.012963,   7.948025,   8.883087,   9.818148,  10.75321 ,
            11.688272,  12.623333,  13.558395,  14.493457,  15.428518,  16.36358 ,
            17.298641,  18.233703,  19.168764,  20.103825,  21.038887,  21.973948,
            22.909009,  23.84407 ,  24.779132,  25.714193,  26.649254,  27.584315,
            28.519375,  29.454436,  30.389497,  31.324558,  32.259618,  33.194679,
            34.129739,  35.064799,  35.99986 ,  36.93492 ,  37.86998 ,  38.805039,
            39.740099,  40.675159,  41.610218,  42.545277,  43.480336,  44.415395,
            45.350454,  46.285512,  47.22057 ,  48.155628,  49.090686,  50.025743,
            50.960801,  51.895857,  52.830914,  53.76597 ,  54.701026,  55.636081,
            56.571136,  57.50619 ,  58.441244,  59.376297,  60.311349,  61.246401,
            62.181452,  63.116502,  64.051551,  64.986599,  65.921645,  66.856691,
            67.791734,  68.726776,  69.661816,  70.596854,  71.531889,  72.466921,
            73.401949,  74.336973,  75.271992,  76.207005,  77.142011,  78.077007,
            79.011992,  79.946962,  80.881913,  81.816839,  82.751728,  83.686567,
            84.621327,  85.55596 ,  86.490367,  87.424304,  88.357004,  89.284228])

  • lon
    (lon)
    float64
    0.0 0.9375 1.875 ... 358.1 359.1

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.    ,   0.9375,   1.875 , ..., 357.1875, 358.125 , 359.0625])

• Data variables: (3)
  • lat_bnds
    (lat, bnds)
    float64
    ...

    [384 values with dtype=float64]

  • lon_bnds
    (lon, bnds)
    float64
    ...

    [768 values with dtype=float64]

  • orog
    (lat, lon)
    float32
    ...

    standard_name :

    surface_altitude

    long_name :

    Surface Altitude

    comment :

    The surface called 'surface' means the lower boundary of the atmosphere. Altitude is the (geometric) height above the geoid, which is the reference geopotential surface. The geoid is similar to mean sea level.

    units :

    m

    original_name :

    orog

    cell_methods :

    area: mean

    cell_measures :

    area: areacella

    history :

    2019-08-25T06:42:14Z altered by CMOR: replaced missing value flag (-9e+33) and corresponding data with standard missing value (1e+20). 2019-08-25T06:42:14Z altered by CMOR: Inverted axis: lat.

    [73728 values with dtype=float32]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,
           -86.49036676628116, -85.55596048489265,  -84.6213271076488,
           -83.68656681656385, -82.75172847343066, -81.81683872860322,
            -80.8819133467975,
           ...
             80.8819133467975,  81.81683872860322,  82.75172847343066,
            83.68656681656385,   84.6213271076488,  85.55596048489265,
            86.49036676628116,  87.42430374606988,  88.35700351866494,
            89.28422753251364],
          dtype='float64', name='lat', length=192))

  • lon
    PandasIndex

    PandasIndex(Index([     0.0,   0.9375,    1.875,   2.8125,     3.75,   4.6875,    5.625,
             6.5625,      7.5,   8.4375,
           ...
            350.625, 351.5625,    352.5, 353.4375,  354.375, 355.3125,   356.25,
           357.1875,  358.125, 359.0625],
          dtype='float64', name='lon', length=384))

• Attributes: (47)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  0.0

  contact :

  cmip6-mpi-esm@dkrz.de

  creation_date :

  2019-08-25T06:42:14Z

  data_specs_version :

  01.00.30

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  fx

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.MPI-M.MPI-ESM1-2-HR.historical.none.r1i1p1f1

  grid :

  gn

  grid_label :

  gn

  history :

  2019-08-25T06:42:14Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  Max Planck Institute for Meteorology, Hamburg 20146, Germany

  institution_id :

  MPI-M

  mip_era :

  CMIP6

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  MPI-ESM1-2-HR

  parent_time_units :

  days since 1850-1-1 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  project_id :

  CMIP6

  realization_index :

  1

  realm :

  land

  references :

  MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400, Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217

  source :

  MPI-ESM1.2-HR (2017): aerosol: none, prescribed MACv2-SP atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa) atmosChem: none land: JSBACH3.20 landIce: none/prescribed ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m) ocnBgchem: HAMOCC6 seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)

  source_id :

  MPI-ESM1-2-HR

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  fx

  table_info :

  Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc

  title :

  MPI-ESM1-2-HR output prepared for CMIP6

  tracking_id :

  hdl:21.14100/3341d289-1282-489f-9419-c263718da247

  variable_id :

  orog

  variant_label :

  r1i1p1f1

  license :

  CMIP6 model data produced by MPI-M is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  cmor_version :

  3.5.0

Interpolation using CDO

We will use CDO’s python bindings to control the cdo operators. Please note, that the python bindings in the background actually execute the cdo commands on the command line. CDO does have a huge IO overhead since it will always write a file between each operator step and will always need data from a file on the filesystem as input. If you give an xarray dataset as input (see below), the python binding will actually trigger a to_netcdf call to write the input as a temporary file to disk. You should be aware of this if you use huge xarray datasets as input.

We will first write the EUR-11 grid into a file on the disk so that we can use it as input to CDO

from cdo import Cdo

eur11.to_netcdf("EUR-11_grid.nc")

Now we will remap the first timestep of the CMIP6 modeldata to the EUR-11 grid:

remap_cdo = Cdo().remapbil("EUR-11_grid.nc", input=ds, returnXArray="orog")
remap_cdo

<xarray.DataArray 'orog' (rlat: 412, rlon: 424)> Size: 699kB
[174688 values with dtype=float32]
Coordinates:
    lon      (rlat, rlon) float64 1MB ...
    lat      (rlat, rlon) float64 1MB ...
  * rlon     (rlon) float64 3kB -28.38 -28.27 -28.16 ... 17.93 18.05 18.16
  * rlat     (rlat) float64 3kB -23.38 -23.27 -23.16 ... 21.61 21.73 21.84
Attributes:
    standard_name:  surface_altitude
    long_name:      Surface Altitude
    units:          m
    grid_mapping:   rotated_latitude_longitude
    comment:        The surface called 'surface' means the lower boundary of ...
    original_name:  orog
    cell_methods:   area: mean
    cell_measures:  area: areacella
    history:        2019-08-25T06:42:14Z altered by CMOR: replaced missing va...

xarray.DataArray

'orog'

• rlat: 412
• rlon: 424

• ...

  [174688 values with dtype=float32]

• Coordinates: (4)
  • lon
    (rlat, rlon)
    float64
    ...

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    _CoordinateAxisType :

    Lon

    [174688 values with dtype=float64]

  • lat
    (rlat, rlon)
    float64
    ...

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    _CoordinateAxisType :

    Lat

    [174688 values with dtype=float64]

  • rlon
    (rlon)
    float64
    -28.38 -28.27 ... 18.05 18.16

    standard_name :

    projection_x_coordinate

    long_name :

    longitude in rotated pole grid

    units :

    degrees

    axis :

    X

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

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

    standard_name :

    projection_y_coordinate

    long_name :

    latitude in rotated pole grid

    units :

    degrees

    axis :

    Y

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

• 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: (9)

  standard_name :

  surface_altitude

  long_name :

  Surface Altitude

  units :

  m

  grid_mapping :

  rotated_latitude_longitude

  comment :

  The surface called 'surface' means the lower boundary of the atmosphere. Altitude is the (geometric) height above the geoid, which is the reference geopotential surface. The geoid is similar to mean sea level.

  original_name :

  orog

  cell_methods :

  area: mean

  cell_measures :

  area: areacella

  history :

  2019-08-25T06:42:14Z altered by CMOR: replaced missing value flag (-9e+33) and corresponding data with standard missing value (1e+20). 2019-08-25T06:42:14Z altered by CMOR: Inverted axis: lat.

remap_cdo.plot()

<matplotlib.collections.QuadMesh at 0x145562c20>

Alternative interpolation methods

A nice alternative is xesmf since it is based on xarray and will also very nicely work with dask.

import xesmf as xe

regridder = xe.Regridder(ds, eur11, "bilinear", periodic=True)
regridder

xESMF Regridder 
Regridding algorithm:       bilinear 
Weight filename:            bilinear_192x384_412x424_peri.nc 
Reuse pre-computed weights? False 
Input grid shape:           (192, 384) 
Output grid shape:          (412, 424) 
Periodic in longitude?      True

remap_xe = regridder(ds.orog)

remap_xe.plot()

<matplotlib.collections.QuadMesh at 0x146476aa0>

We can easily compare both approaches

(remap_cdo - remap_xe).plot(x="lon", y="lat")

<matplotlib.collections.QuadMesh at 0x1464f9840>

The nice thing about xesmf is that it works together with xarray and will keep all meta information. Another consequence is that xesmf works well with dask and it’s vectorization. That means, if we have a long time axis along we want to regrid, this can easily be parallelized using, e.g., dask.distributed and will also work nicely with large datasets that don’t fit into memory. The xesmf regridder can also store and reuse regridding weights for faster interpolation.