UXarray for Advanced HEALPix Analysis & Visualization

In this section, you’ll learn:

• Using the uxarray package to perform advanced analysis operators over HEALPix data such as non-conservative zonal means, etc.

Related Documentation

• UXarray homepage

• Working with HEALPix data - UXarray documentation

• UXarray overview - Unstructured Grids Visualization Cookbook

• Data visualization with UXarray - Unstructured Grids Visualization Cookbook

• Cross-sections - UXarray documentation

• Intake Cookbook

Prerequisites

Concepts	Importance	Notes
UXarray	Necessary
HEALPix overview	Necessary

Time to learn: 30 minutes

---

import cartopy.crs as ccrs
import intake
import uxarray as ux

Open data catalog

Tip

We assume that you have already gone over the previous section, UXarray for Basic HEALPix Statistics & Visualization. If not and if you need to learn about data catalogs in general and the data we will use throughout this notebook, we recommend to check that section first.

Let us open the online catalog from the WCRP’s Digital Earths Global Hackathon 2025 catalog repository using intake and read the output of the ICON run d3hp003, which is stored in the HEALPix format:

cat_url = "https://digital-earths-global-hackathon.github.io/catalog/catalog.yaml"
cat = intake.open_catalog(cat_url)
model_run = cat.online.icon_d3hp003

We can look into the highest possible resolution level allowed in this dataset at zoom level = 9 as Xarray.Dataset:

ds = model_run(zoom=9, time="P1D").to_dask()

/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:21: 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`.
  'dims': dict(self._ds.dims),

Create UXarray Datasets from HEALPix

We can use UXarray’s from_healpix API as follows to open a HEALPix grid from xarray.Dataset:

uxds = ux.UxDataset.from_healpix(ds)
uxds

<xarray.UxDataset> Size: 2TB
Dimensions:        (time: 425, n_face: 3145728, crs: 1, pressure: 30,
                    soil_level: 5, pressure_rva: 3)
Coordinates:
  * crs            (crs) float32 4B nan
  * pressure       (pressure) int64 240B 5 10 20 50 ... 92500 95000 97500 100000
  * pressure_rva   (pressure_rva) int64 24B 16 18 23
  * soil_level     (soil_level) int64 40B 0 0 0 2 6
  * time           (time) datetime64[ns] 3kB 2020-01-02 ... 2021-03-01
Dimensions without coordinates: n_face
Data variables: (12/58)
    clivi          (time, n_face) float32 5GB ...
    clt            (time, n_face) float32 5GB ...
    clwvi          (time, n_face) float32 5GB ...
    egpvi          (time, n_face) float32 5GB ...
    einvi          (time, n_face) float32 5GB ...
    ekhvi          (time, n_face) float32 5GB ...
    ...             ...
    ua             (time, pressure, n_face) float32 160GB ...
    uas            (time, n_face) float32 5GB ...
    va             (time, pressure, n_face) float32 160GB ...
    vas            (time, n_face) float32 5GB ...
    wa             (time, pressure, n_face) float32 160GB ...
    zg             (time, pressure, n_face) float32 160GB ...

<xarray.UxDataset> Size: 2TB
Dimensions:        (time: 425, n_face: 3145728, crs: 1, pressure: 30,
                    soil_level: 5, pressure_rva: 3)
Coordinates:
  * crs            (crs) float32 4B nan
  * pressure       (pressure) int64 240B 5 10 20 50 ... 92500 95000 97500 100000
  * pressure_rva   (pressure_rva) int64 24B 16 18 23
  * soil_level     (soil_level) int64 40B 0 0 0 2 6
  * time           (time) datetime64[ns] 3kB 2020-01-02 ... 2021-03-01
Dimensions without coordinates: n_face
Data variables: (12/58)
    clivi          (time, n_face) float32 5GB ...
    clt            (time, n_face) float32 5GB ...
    clwvi          (time, n_face) float32 5GB ...
    egpvi          (time, n_face) float32 5GB ...
    einvi          (time, n_face) float32 5GB ...
    ekhvi          (time, n_face) float32 5GB ...
    ...             ...
    ua             (time, pressure, n_face) float32 160GB ...
    uas            (time, n_face) float32 5GB ...
    va             (time, pressure, n_face) float32 160GB ...
    vas            (time, n_face) float32 5GB ...
    wa             (time, pressure, n_face) float32 160GB ...
    zg             (time, pressure, n_face) float32 160GB ...

uxarray.UxDataset

• Dimensions:
  • time: 425
  • n_face: 3145728
  • crs: 1
  • pressure: 30
  • soil_level: 5
  • pressure_rva: 3
• Coordinates: (5)
  • crs
    (crs)
    float32
    nan

    grid_mapping_name :

    healpix

    healpix_nside :

    512

    healpix_order :

    nest

    array([nan], dtype=float32)

  • pressure
    (pressure)
    int64
    5 10 20 50 ... 95000 97500 100000

    axis :

    Z

    long_name :

    generalized_height

    positive :

    up

    standard_name :

    height

    array([     5,     10,     20,     50,    100,    200,    500,   1000,   2000,
             3000,   5000,   7000,  10000,  15000,  20000,  25000,  30000,  40000,
            50000,  60000,  70000,  75000,  80000,  85000,  87500,  90000,  92500,
            95000,  97500, 100000])

  • pressure_rva
    (pressure_rva)
    int64
    16 18 23

    axis :

    Z

    long_name :

    generalized_height_rva

    positive :

    up

    standard_name :

    height

    array([16, 18, 23])

  • soil_level
    (soil_level)
    int64
    0 0 0 2 6

    axis :

    Z

    long_name :

    generalized_height

    positive :

    up

    standard_name :

    height

    array([0, 0, 0, 2, 6])

  • time
    (time)
    datetime64[ns]
    2020-01-02 ... 2021-03-01

    axis :

    T

    array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000',
           '2020-01-04T00:00:00.000000000', ..., '2021-02-27T00:00:00.000000000',
           '2021-02-28T00:00:00.000000000', '2021-03-01T00:00:00.000000000'],
          shape=(425,), dtype='datetime64[ns]')

• Data variables: (58)
  • clivi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    cloud ice path

    short_name :

    standard_name :

    atmosphere_mass_content_of_cloud_ice

    units :

    kg m-2

    [1336934400 values with dtype=float32]

  • clt
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    total cloud cover

    short_name :

    standard_name :

    clt

    units :

    m2 m-2

    [1336934400 values with dtype=float32]

  • clwvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    cloud condensed water path

    short_name :

    standard_name :

    atmosphere_mass_content_of_cloud_condensed_water

    units :

    kg m-2

    [1336934400 values with dtype=float32]

  • egpvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Atmosphere Geopotential Energy Content

    short_name :

    standard_name :

    atmosphere_geopotential_energy_content

    units :

    J m-2

    [1336934400 values with dtype=float32]

  • einvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Atmosphere Moist Internal Energy Content

    short_name :

    standard_name :

    atmosphere_moist_internal_energy_content

    units :

    J m-2

    [1336934400 values with dtype=float32]

  • ekhvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Atmosphere Horizontal Kinetic Energy Content

    short_name :

    standard_name :

    atmosphere_horizontal_kinetic_energy_content

    units :

    J m-2

    [1336934400 values with dtype=float32]

  • ekvvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Atmosphere Vertical Kinetic Energy Content

    short_name :

    standard_name :

    atmosphere_vertical_kinetic_energy_content

    units :

    J m-2

    [1336934400 values with dtype=float32]

  • hflsd
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    latent heat flux

    short_name :

    standard_name :

    lhflx

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • hfssd
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    sensible heat flux

    short_name :

    standard_name :

    shflx

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • hur
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::hur

    hiopy::time_method :

    mean

    long_name :

    relative humidity

    short_name :

    standard_name :

    relative_humidity

    units :

    [40108032000 values with dtype=float32]

  • hus
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::hus

    hiopy::time_method :

    mean

    long_name :

    Specific humidity

    short_name :

    standard_name :

    specific_humidity

    units :

    kg kg-1

    [40108032000 values with dtype=float32]

  • huss
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    specific humidity in 2m

    short_name :

    standard_name :

    qv2m

    units :

    kg kg-1

    [1336934400 values with dtype=float32]

  • mrso
    (time, soil_level, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Water content of soil layers

    short_name :

    standard_name :

    wtr_soil_sl

    units :

    m

    [6684672000 values with dtype=float32]

  • o3vi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    ozone path

    short_name :

    standard_name :

    atmosphere_mass_content_of_ozone

    units :

    kg m-2

    [1336934400 values with dtype=float32]

  • orog
    (n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    inst

    long_name :

    surface altitude

    short_name :

    standard_name :

    surface_altitude

    units :

    m

    [3145728 values with dtype=float32]

  • pr
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    precipitation flux

    short_name :

    standard_name :

    pr

    units :

    kg m-2 s-1

    [1336934400 values with dtype=float32]

  • prs
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    large-scale precipitation flux (snow)

    short_name :

    standard_name :

    prls

    units :

    kg m-2 s-1

    [1336934400 values with dtype=float32]

  • prw
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    water vapor path

    short_name :

    standard_name :

    atmosphere_mass_content_of_water_vapor

    units :

    kg m-2

    [1336934400 values with dtype=float32]

  • ps
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface pressure

    short_name :

    standard_name :

    surface_air_pressure

    units :

    Pa

    [1336934400 values with dtype=float32]

  • psl
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    mean sea level pressure

    short_name :

    standard_name :

    mean sea level pressure

    units :

    Pa

    [1336934400 values with dtype=float32]

  • qall
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::qall

    hiopy::time_method :

    mean

    long_name :

    mass fraction of all hydrometeors in air

    short_name :

    standard_name :

    mass_fraction_of_all_hydrometeors_in_air

    units :

    kg kg-1

    [40108032000 values with dtype=float32]

  • rlds
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface downwelling longwave radiation

    short_name :

    standard_name :

    surface_downwelling_longwave_flux_in_air

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rldscs
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface downwelling clear-sky longwave radiation

    short_name :

    standard_name :

    surface_downwelling_longwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rlus
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface upwelling longwave radiation

    short_name :

    standard_name :

    surface_upwelling_longwave_flux_in_air

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rlut
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    toa outgoing longwave radiation

    short_name :

    standard_name :

    toa_outgoing_longwave_flux

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rlutcs
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    toa outgoing clear-sky longwave radiation

    short_name :

    standard_name :

    toa_outgoing_longwave_flux_assuming_clear_sky

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsds
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface downwelling shortwave radiation

    short_name :

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsdscs
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface downwelling clear-sky shortwave radiation

    short_name :

    standard_name :

    surface_downwelling_shortwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsdt
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    toa incident shortwave radiation

    short_name :

    standard_name :

    toa_incoming_shortwave_flux

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsus
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface upwelling shortwave radiation

    short_name :

    standard_name :

    surface_upwelling_shortwave_flux_in_air

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsuscs
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface upwelling clear-sky shortwave radiation

    short_name :

    standard_name :

    surface_upwelling_shortwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsut
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    toa outgoing shortwave radiation

    short_name :

    standard_name :

    toa_outgoing_shortwave_flux

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rsutcs
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    toa outgoing clear-sky shortwave radiation

    short_name :

    standard_name :

    toa_outgoing_shortwave_flux_assuming_clear_sky

    units :

    W m-2

    [1336934400 values with dtype=float32]

  • rva
    (time, pressure_rva, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::vor

    hiopy::time_method :

    mean

    long_name :

    Vorticity

    short_name :

    standard_name :

    relative_vorticity_on_cells

    units :

    s-1

    [4010803200 values with dtype=float32]

  • sftgif
    (n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    inst

    long_name :

    cell area fraction occupied by land ice

    short_name :

    standard_name :

    land_ice_area_fraction

    units :

    m2/m2

    [3145728 values with dtype=float32]

  • sftlf
    (n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    inst

    long_name :

    cell area fraction occupied by land including lakes

    short_name :

    standard_name :

    land_area_fraction

    units :

    m2/m2

    [3145728 values with dtype=float32]

  • siconc
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    fraction of ocean covered by sea ice

    short_name :

    standard_name :

    sea_ice_cover

    units :

    [1336934400 values with dtype=float32]

  • sncvfa
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Snow area fraction

    short_name :

    standard_name :

    fract_snow

    units :

    -

    [1336934400 values with dtype=float32]

  • swe
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Snow amount

    short_name :

    standard_name :

    weq_snow

    units :

    m (water equivalent)

    [1336934400 values with dtype=float32]

  • ta
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::temp

    hiopy::time_method :

    mean

    long_name :

    Temperature

    short_name :

    standard_name :

    air_temperature

    units :

    K

    [40108032000 values with dtype=float32]

  • tas
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    temperature in 2m

    short_name :

    standard_name :

    tas

    units :

    K

    [1336934400 values with dtype=float32]

  • tauu
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    u-momentum flux at the surface

    short_name :

    standard_name :

    u_stress

    units :

    N m-2

    [1336934400 values with dtype=float32]

  • tauv
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    v-momentum flux at the surface

    short_name :

    standard_name :

    v_stress

    units :

    N m-2

    [1336934400 values with dtype=float32]

  • tend_egpdynvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Geopotential Energy Content due to Dynamics

    short_name :

    standard_name :

    tendency_of_atmosphere_geopotential_energy_content_due_to_dynamics

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_eincldvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Moist Internal Energy Content due to Cloud Microphysics

    short_name :

    standard_name :

    tendency_of_atmosphere_moist_internal_energy_content_due_to_cloud_microphysics

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_eindynvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Moist Internal Energy Content due to Dynamics

    short_name :

    standard_name :

    tendency_of_atmosphere_moist_internal_energy_content_due_to_dynamics

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_einradvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Moist Internal Energy Content due to Radiation

    short_name :

    standard_name :

    tendency_of_atmosphere_moist_internal_energy_content_due_to_radiation

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_eintmxvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Moist Internal Energy Content due to Turbulent Mixing

    short_name :

    standard_name :

    tendency_of_atmosphere_moist_internal_energy_content_due_to_turbulent_mixing

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_ekhdynvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Horizontal Kinetic Energy Content due to Dynamics

    short_name :

    standard_name :

    tendency_of_atmosphere_horizontal_kinetic_energy_content_due_to_dynamics

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_ekhtmxvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Horizontal Kinetic Energy Content due to Turbulent Mixing

    short_name :

    standard_name :

    tendency_of_atmosphere_horizontal_kinetic_energy_content_due_to_turbulent_mixing

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • tend_ekvdynvi
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    Tendency of Atmosphere Vertical Kinetic Energy Content due to Dynamics

    short_name :

    standard_name :

    tendency_of_atmosphere_vertical_kinetic_energy_content_due_to_dynamics

    units :

    J m-2 s-1

    [1336934400 values with dtype=float32]

  • ts
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    surface temperature

    short_name :

    standard_name :

    surface_temperature

    units :

    K

    [1336934400 values with dtype=float32]

  • ua
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::u

    hiopy::time_method :

    mean

    long_name :

    Zonal wind

    short_name :

    standard_name :

    eastward_wind

    units :

    m s-1

    [40108032000 values with dtype=float32]

  • uas
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    zonal wind in 10m

    short_name :

    standard_name :

    uas

    units :

    m s-1

    [1336934400 values with dtype=float32]

  • va
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::v

    hiopy::time_method :

    mean

    long_name :

    Meridional wind

    short_name :

    standard_name :

    northward_wind

    units :

    m s-1

    [40108032000 values with dtype=float32]

  • vas
    (time, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::time_method :

    mean

    long_name :

    meridional wind in 10m

    short_name :

    standard_name :

    vas

    units :

    m s-1

    [1336934400 values with dtype=float32]

  • wa
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::wa_phy

    hiopy::time_method :

    mean

    long_name :

    vertical velocity in m/s

    short_name :

    standard_name :

    upward_air_velocity

    units :

    m s-1

    [40108032000 values with dtype=float32]

  • zg
    (time, pressure, n_face)
    float32
    ...

    grid_mapping :

    crs

    hiopy::enable :

    True

    hiopy::nnn :

    4

    hiopy::src_name :

    pl::z_mc

    hiopy::time_method :

    mean

    long_name :

    geometric height at full level center

    short_name :

    standard_name :

    geometric_height_at_full_level_center

    units :

    m

    [40108032000 values with dtype=float32]

• Indexes: (5)
  • crs
    PandasIndex

    PandasIndex(Index([nan], dtype='float32', name='crs'))

  • pressure
    PandasIndex

    PandasIndex(Index([     5,     10,     20,     50,    100,    200,    500,   1000,   2000,
             3000,   5000,   7000,  10000,  15000,  20000,  25000,  30000,  40000,
            50000,  60000,  70000,  75000,  80000,  85000,  87500,  90000,  92500,
            95000,  97500, 100000],
          dtype='int64', name='pressure'))

  • pressure_rva
    PandasIndex

    PandasIndex(Index([16, 18, 23], dtype='int64', name='pressure_rva'))

  • soil_level
    PandasIndex

    PandasIndex(Index([0, 0, 0, 2, 6], dtype='int64', name='soil_level'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05',
                   '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09',
                   '2020-01-10', '2020-01-11',
                   ...
                   '2021-02-20', '2021-02-21', '2021-02-22', '2021-02-23',
                   '2021-02-24', '2021-02-25', '2021-02-26', '2021-02-27',
                   '2021-02-28', '2021-03-01'],
                  dtype='datetime64[ns]', name='time', length=425, freq=None))

• Attributes: (0)

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_face: 3145728
Grid Coordinates (Spherical):
  * face_lon: (3145728,)
  * face_lat: (3145728,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
Grid Descriptor Variables:

uxarray.UxDataset.uxgrid

• Dimensions:
  • n_face: 3145728
• Spherical Coordinates: (2)
  • face_lon
    (n_face)
    float64
    45.0 45.09 44.91 ... -45.09 -45.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([ 45.        ,  45.08789062,  44.91210938, ..., -44.91210938,
           -45.08789062, -45.        ], shape=(3145728,))

  • face_lat
    (n_face)
    float64
    0.0746 0.1492 ... -0.1492 -0.0746

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 0.0746039 ,  0.14920793,  0.14920793, ..., -0.14920793,
           -0.14920793, -0.0746039 ], shape=(3145728,))

• Cartesian Coordinates: (0)
• Connectivity: (0)
• Descriptors: (0)
• Attributes: (5)

  zoom :

  9

  n_side :

  512

  n_pix :

  3145728

  nest :

  True

  source_grid_spec :

  HEALPix

Data variable of interest

Then let us pick a variable, the surface temperature, from the dataset, which will give us an uxarray.UxDataArray:

uxda = uxds["ts"]
uxda

<xarray.UxDataArray 'ts' (time: 425, n_face: 3145728)> Size: 5GB
[1336934400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 3kB 2020-01-02 2020-01-03 ... 2021-03-01
Dimensions without coordinates: n_face
Attributes:
    grid_mapping:        crs
    hiopy::enable:       True
    hiopy::nnn:          4
    hiopy::time_method:  mean
    long_name:           surface temperature
    short_name:          
    standard_name:       surface_temperature
    units:               K

<xarray.UxDataArray 'ts' (time: 425, n_face: 3145728)> Size: 5GB
[1336934400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 3kB 2020-01-02 2020-01-03 ... 2021-03-01
Dimensions without coordinates: n_face
Attributes:
    grid_mapping:        crs
    hiopy::enable:       True
    hiopy::nnn:          4
    hiopy::time_method:  mean
    long_name:           surface temperature
    short_name:          
    standard_name:       surface_temperature
    units:               K

uxarray.UxDataArray

'ts'

• time: 425
• n_face: 3145728

• ...

  [1336934400 values with dtype=float32]

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2020-01-02 ... 2021-03-01

    axis :

    T

    array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000',
           '2020-01-04T00:00:00.000000000', ..., '2021-02-27T00:00:00.000000000',
           '2021-02-28T00:00:00.000000000', '2021-03-01T00:00:00.000000000'],
          shape=(425,), dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05',
                   '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09',
                   '2020-01-10', '2020-01-11',
                   ...
                   '2021-02-20', '2021-02-21', '2021-02-22', '2021-02-23',
                   '2021-02-24', '2021-02-25', '2021-02-26', '2021-02-27',
                   '2021-02-28', '2021-03-01'],
                  dtype='datetime64[ns]', name='time', length=425, freq=None))

• Attributes: (8)

  grid_mapping :

  crs

  hiopy::enable :

  True

  hiopy::nnn :

  4

  hiopy::time_method :

  mean

  long_name :

  surface temperature

  short_name :

  standard_name :

  surface_temperature

  units :

  K

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_face: 3145728
Grid Coordinates (Spherical):
  * face_lon: (3145728,)
  * face_lat: (3145728,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
Grid Descriptor Variables:

uxarray.UxDataArray.uxgrid

• Dimensions:
  • n_face: 3145728
• Spherical Coordinates: (2)
  • face_lon
    (n_face)
    float64
    45.0 45.09 44.91 ... -45.09 -45.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([ 45.        ,  45.08789062,  44.91210938, ..., -44.91210938,
           -45.08789062, -45.        ], shape=(3145728,))

  • face_lat
    (n_face)
    float64
    0.0746 0.1492 ... -0.1492 -0.0746

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 0.0746039 ,  0.14920793,  0.14920793, ..., -0.14920793,
           -0.14920793, -0.0746039 ], shape=(3145728,))

• Cartesian Coordinates: (0)
• Connectivity: (0)
• Descriptors: (0)
• Attributes: (5)

  zoom :

  9

  n_side :

  512

  n_pix :

  3145728

  nest :

  True

  source_grid_spec :

  HEALPix

Global mean and plot

Computing the global surface temperature mean (at the first timestep) and also having a quick plot of it would be a good idea to have as references to compare the upcoming analyses & visualizations to them:

print(
    "Global surface temperature average on ", uxda.time[0].values, ": ", uxda.isel(time=0).mean().values, " K"
)

Global surface temperature average on  2020-01-02T00:00:00.000000000 :  286.99234  K

%%time

projection = ccrs.Robinson()

uxda.isel(time=0).plot(
    projection=projection,
    cmap="inferno",
    features=["borders", "coastline"],
    title="Global surface temperature (Polygon raster)",
    width=700,
)

CPU times: user 16.9 s, sys: 935 ms, total: 17.8 s
Wall time: 17.1 s

WARNING:param.GeoOverlayPlot00477: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.

Rasterized point plots

When working with a higher-resolution dataset at a global scale, it’s not always practical to render each cell as a polygon. Instead, we can rasterize the center of each pixel.

%%time
projection = ccrs.Robinson()

# Controls the size of each pixel (smaller value leads to larger pixels)
pixel_ratio = 0.5

uxda.isel(time=0).plot.points(
    projection=projection,
    rasterize=True,
    dynamic=False,
    width=1000,
    height=500,
    pixel_ratio=pixel_ratio,
    cmap="inferno",
    title=f"Global surface temperature (Point raster), pixel_ratio={pixel_ratio}",
)

CPU times: user 910 ms, sys: 230 ms, total: 1.14 s
Wall time: 3.46 s

If we decrease the size of each pixel (by setting the pixel ratio to a higher value), we can start to see missing values, which is due to a lower density of points near the poles, leading to some pixels not containing any of our original points.

Because of this, it’s useful to try a few pixel_ratio values and see which one works best for your given resolution.

projection = ccrs.Robinson()

# Controls the size of each pixel (smaller value leads to larger pixels)
pixel_ratio = 2.0

uxda.isel(time=0).plot.points(
    projection=projection,
    rasterize=True,
    dynamic=False,
    width=1000,
    height=500,
    pixel_ratio=pixel_ratio,
    cmap="inferno",
    title=f"Global surface temperature (Point raster) with a bad pixel size selection, pixel_ratio={pixel_ratio}",
)

Cross-sections

We can look at constant latitude/longitude cross-sections of an uxarray.UxDataArray:

boulder_lat = 40.0190


# With fine resolutions like zoom level of 9, it is visually hard to observe the cross-sections,
# so we will use a zoom level of 4 for a better visualization
uxda_coarse = ux.UxDataset.from_healpix(model_run(zoom=4, time="P1D").to_dask())["ts"]
uxda_coarse.uxgrid.face_node_connectivity

uxda_lat = uxda_coarse.cross_section.constant_latitude(boulder_lat)
uxda_lat

/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:21: 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`.
  'dims': dict(self._ds.dims),

<xarray.UxDataArray 'ts' (time: 425, n_face: 128)> Size: 218kB
[54400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 3kB 2020-01-02 2020-01-03 ... 2021-03-01
Dimensions without coordinates: n_face
Attributes:
    grid_mapping:        crs
    hiopy::enable:       True
    hiopy::nnn:          4
    hiopy::time_method:  mean
    long_name:           surface temperature
    short_name:          
    standard_name:       surface_temperature
    units:               K

<xarray.UxDataArray 'ts' (time: 425, n_face: 128)> Size: 218kB
[54400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 3kB 2020-01-02 2020-01-03 ... 2021-03-01
Dimensions without coordinates: n_face
Attributes:
    grid_mapping:        crs
    hiopy::enable:       True
    hiopy::nnn:          4
    hiopy::time_method:  mean
    long_name:           surface temperature
    short_name:          
    standard_name:       surface_temperature
    units:               K

uxarray.UxDataArray

'ts'

• time: 425
• n_face: 128

• ...

  [54400 values with dtype=float32]

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2020-01-02 ... 2021-03-01

    axis :

    T

    array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000',
           '2020-01-04T00:00:00.000000000', ..., '2021-02-27T00:00:00.000000000',
           '2021-02-28T00:00:00.000000000', '2021-03-01T00:00:00.000000000'],
          shape=(425,), dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05',
                   '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09',
                   '2020-01-10', '2020-01-11',
                   ...
                   '2021-02-20', '2021-02-21', '2021-02-22', '2021-02-23',
                   '2021-02-24', '2021-02-25', '2021-02-26', '2021-02-27',
                   '2021-02-28', '2021-03-01'],
                  dtype='datetime64[ns]', name='time', length=425, freq=None))

• Attributes: (8)

  grid_mapping :

  crs

  hiopy::enable :

  True

  hiopy::nnn :

  4

  hiopy::time_method :

  mean

  long_name :

  surface temperature

  short_name :

  standard_name :

  surface_temperature

  units :

  K

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_node: 252
  * n_face: 128
  * n_max_face_nodes: 4
Grid Coordinates (Spherical):
  * node_lon: (252,)
  * node_lat: (252,)
  * face_lon: (128,)
  * face_lat: (128,)
Grid Coordinates (Cartesian):
  * node_x: (252,)
  * node_y: (252,)
  * node_z: (252,)
Grid Connectivity Variables:
  * face_node_connectivity: (128, 4)
Grid Descriptor Variables:
  * n_nodes_per_face: (128,)

uxarray.UxDataArray.uxgrid

• Dimensions:
  • n_face: 128
  • n_node: 252
  • n_max_face_nodes: 4
  • lon_lat: 2
  • min_max: 2
• Spherical Coordinates: (4)
  • node_lon
    (n_node)
    float64
    -14.06 -59.06 -45.0 ... -95.62 45.0

    standard_name :

    longitude

    long name :

    Longitude of the corner nodes of each face

    units :

    degrees_east

    array([ -14.0625,  -59.0625,  -45.    ,   -5.625 ,  174.375 ,   56.25  ,
            138.    ,  146.25  , -146.25  ,   96.    , -106.875 ,  -70.3125,
            -84.    ,  123.75  , -168.    ,  104.0625,  -56.25  , -180.    ,
            150.    ,  -11.25  ,   61.875 , -154.6875, -151.875 , -129.375 ,
           -138.    ,   39.375 ,   11.25  ,   19.6875,    5.625 ,  -45.    ,
            -36.5625,  -36.    ,    8.4375,    0.    ,  -47.8125, -135.    ,
             39.375 ,   56.25  ,  -33.75  ,  106.875 , -126.5625,  -22.5   ,
            135.    , -144.    ,   11.25  , -168.75  ,   18.    ,  -33.75  ,
            157.5   ,  118.125 ,  108.    , -168.75  ,  126.5625,  -39.375 ,
             60.    ,   90.    ,   87.1875,  114.    ,   90.    ,  -18.    ,
            -24.    ,  135.    ,  143.4375, -150.    , -115.3125, -123.75  ,
           -177.1875,   45.    ,  154.6875, -157.5   ,  137.8125, -151.875 ,
            -95.625 ,   84.375 ,  146.25  ,  118.125 ,  174.375 , -108.    ,
           -140.625 ,  -67.5   ,  151.875 ,  -67.5   , -118.125 ,   73.125 ,
           -102.    ,  -90.    ,   92.8125, -180.    ,  -78.    , -143.4375,
            102.    , -112.5   ,   16.875 ,  140.625 ,  132.1875,  115.3125,
            162.    ,   28.125 ,   -5.625 ,   73.125 ,  -39.375 ,   48.    ,
            -28.125 ,  -90.    , -174.375 ,   54.    ,  -11.25  ,  -42.    ,
           -140.625 ,  126.    ,  -48.    ,  -16.875 , -149.0625,   33.75  ,
             47.8125,  -78.75  ,  109.6875, -112.5   , -174.    ,  -98.4375,
    ...
            -96.    , -106.875 ,   12.    ,  -22.5   ,  -50.625 ,  168.75  ,
             53.4375,  -84.375 ,   64.6875,  101.25  , -163.125 ,  157.5   ,
           -174.375 ,   -6.    ,  -72.    ,  -42.1875, -120.9375,   90.    ,
             75.9375, -156.    ,   14.0625,   28.125 ,  -84.375 ,  156.    ,
              5.625 ,   22.5   ,   22.5   ,  168.75  ,  129.375 ,   36.5625,
             98.4375,   16.875 ,   67.5   ,   50.625 ,  -90.    ,   42.    ,
           -180.    , -101.25  ,   59.0625, -157.5   ,  177.1875,  -54.    ,
              6.    , -171.5625,  -56.25  ,  -53.4375,  140.625 ,   -8.4375,
           -120.    , -135.    ,   30.    ,  144.    ,   67.5   ,   84.    ,
             42.1875, -126.    ,   36.    ,  -61.875 , -129.375 , -163.125 ,
           -101.25  ,  120.9375,   66.    ,  163.125 ,   84.375 , -165.9375,
              0.    ,  -30.    ,  112.5   , -123.75  ,  -73.125 ,   78.    ,
            174.    , -114.    , -162.    ,  112.5   ,   30.9375,   25.3125,
           -104.0625,  -16.875 ,   61.875 ,  -78.75  ,  149.0625,   -2.8125,
             33.75  ,  -64.6875,  129.375 , -132.1875, -160.3125,  -75.9375,
            -73.125 ,  -60.    ,    0.    ,  -19.6875,   78.75  ,  132.    ,
            101.25  ,  120.    ,  -12.    ,  -28.125 ,  106.875 ,  -81.5625,
            151.875 ,  163.125 ,   50.625 ,  -50.625 , -146.25  ,   81.5625,
           -118.125 , -132.    ,   95.625 ,    2.8125,   78.75  ,  -87.1875,
            160.3125, -109.6875,  -25.3125,  165.9375,  -95.625 ,   45.    ])

  • node_lat
    (n_node)
    float64
    38.68 38.68 41.81 ... 41.81 35.69

    standard_name :

    latitude

    long name :

    Latitude of the corner nodes of each face

    units :

    degrees_north

    array([38.68218745, 38.68218745, 41.8103149 , 35.68533471, 41.8103149 ,
           41.8103149 , 44.99388015, 35.68533471, 41.8103149 , 44.99388015,
           35.68533471, 38.68218745, 44.99388015, 41.8103149 , 44.99388015,
           38.68218745, 41.8103149 , 35.68533471, 44.99388015, 35.68533471,
           35.68533471, 38.68218745, 41.8103149 , 41.8103149 , 44.99388015,
           35.68533471, 35.68533471, 38.68218745, 35.68533471, 35.68533471,
           38.68218745, 44.99388015, 38.68218745, 41.8103149 , 38.68218745,
           41.8103149 , 41.8103149 , 35.68533471, 41.8103149 , 41.8103149 ,
           38.68218745, 35.68533471, 35.68533471, 44.99388015, 41.8103149 ,
           35.68533471, 44.99388015, 35.68533471, 41.8103149 , 35.68533471,
           44.99388015, 41.8103149 , 38.68218745, 35.68533471, 44.99388015,
           44.99388015, 38.68218745, 44.99388015, 35.68533471, 44.99388015,
           44.99388015, 41.8103149 , 38.68218745, 44.99388015, 38.68218745,
           41.8103149 , 38.68218745, 41.8103149 , 38.68218745, 35.68533471,
           38.68218745, 35.68533471, 35.68533471, 41.8103149 , 41.8103149 ,
           41.8103149 , 35.68533471, 44.99388015, 41.8103149 , 35.68533471,
           41.8103149 , 41.8103149 , 41.8103149 , 35.68533471, 44.99388015,
           35.68533471, 38.68218745, 44.99388015, 44.99388015, 38.68218745,
           44.99388015, 35.68533471, 41.8103149 , 35.68533471, 38.68218745,
           38.68218745, 44.99388015, 35.68533471, 41.8103149 , 41.8103149 ,
    ...
           44.99388015, 41.8103149 , 41.8103149 , 35.68533471, 41.8103149 ,
           35.68533471, 38.68218745, 38.68218745, 35.68533471, 41.8103149 ,
           35.68533471, 41.8103149 , 44.99388015, 41.8103149 , 41.8103149 ,
           38.68218745, 41.8103149 , 38.68218745, 44.99388015, 44.99388015,
           38.68218745, 35.68533471, 38.68218745, 41.8103149 , 38.68218745,
           44.99388015, 35.68533471, 44.99388015, 44.99388015, 35.68533471,
           44.99388015, 38.68218745, 44.99388015, 44.99388015, 35.68533471,
           35.68533471, 41.8103149 , 35.68533471, 38.68218745, 44.99388015,
           41.8103149 , 35.68533471, 38.68218745, 35.68533471, 44.99388015,
           35.68533471, 35.68533471, 41.8103149 , 44.99388015, 44.99388015,
           44.99388015, 44.99388015, 41.8103149 , 38.68218745, 38.68218745,
           38.68218745, 41.8103149 , 41.8103149 , 35.68533471, 38.68218745,
           38.68218745, 35.68533471, 38.68218745, 41.8103149 , 38.68218745,
           38.68218745, 38.68218745, 35.68533471, 44.99388015, 44.99388015,
           38.68218745, 35.68533471, 44.99388015, 35.68533471, 44.99388015,
           44.99388015, 35.68533471, 35.68533471, 38.68218745, 35.68533471,
           35.68533471, 41.8103149 , 35.68533471, 35.68533471, 38.68218745,
           35.68533471, 44.99388015, 41.8103149 , 38.68218745, 41.8103149 ,
           38.68218745, 38.68218745, 38.68218745, 38.68218745, 38.68218745,
           41.8103149 , 35.68533471])

  • face_lon
    (n_face)
    float64
    45.0 67.5 78.75 ... -180.0 -90.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([  45.    ,   67.5   ,   78.75  ,   84.375 ,   87.1875,   81.5625,
             73.125 ,   75.9375,   70.3125,   56.25  ,   61.875 ,   64.6875,
             59.0625,   50.625 ,   53.4375,   47.8125,   22.5   ,   33.75  ,
             39.375 ,   42.1875,   36.5625,   28.125 ,   30.9375,   25.3125,
             11.25  ,   16.875 ,   19.6875,   14.0625,    5.625 ,    8.4375,
              2.8125,  135.    ,  157.5   ,  168.75  ,  174.375 ,  177.1875,
            171.5625,  163.125 ,  165.9375,  160.3125,  146.25  ,  151.875 ,
            154.6875,  149.0625,  140.625 ,  143.4375,  137.8125,  112.5   ,
            123.75  ,  129.375 ,  132.1875,  126.5625,  118.125 ,  120.9375,
            115.3125,  101.25  ,  106.875 ,  109.6875,  104.0625,   95.625 ,
             98.4375,   92.8125, -135.    , -112.5   , -101.25  ,  -95.625 ,
            -92.8125,  -98.4375, -106.875 , -104.0625, -109.6875, -123.75  ,
           -118.125 , -115.3125, -120.9375, -129.375 , -126.5625, -132.1875,
           -157.5   , -146.25  , -140.625 , -137.8125, -143.4375, -151.875 ,
           -149.0625, -154.6875, -168.75  , -163.125 , -160.3125, -165.9375,
           -174.375 , -171.5625, -177.1875,  -45.    ,  -22.5   ,  -11.25  ,
             -5.625 ,   -2.8125,   -8.4375,  -16.875 ,  -14.0625,  -19.6875,
            -33.75  ,  -28.125 ,  -25.3125,  -30.9375,  -39.375 ,  -36.5625,
            -42.1875,  -67.5   ,  -56.25  ,  -50.625 ,  -47.8125,  -53.4375,
            -61.875 ,  -59.0625,  -64.6875,  -78.75  ,  -73.125 ,  -70.3125,
            -75.9375,  -84.375 ,  -81.5625,  -87.1875,    0.    ,   90.    ,
           -180.    ,  -90.    ])

  • face_lat
    (n_face)
    float64
    38.68 38.68 38.68 ... 38.68 38.68

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([38.68218745, 38.68218745, 38.68218745, 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 38.68218745, 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 38.68218745, 38.68218745, 38.68218745,
           41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 ,
           38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745,
           41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 ,
           38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745,
           38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 ,
           38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745,
           38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 ,
           38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745,
           38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 ,
           41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745,
           38.68218745, 38.68218745, 38.68218745])

• Cartesian Coordinates: (3)
  • node_x
    (n_node)
    float64
    0.7572 0.4013 ... -0.07306 0.5743

    standard_name :

    x

    long name :

    Cartesian x location of the corner nodes of each face

    units :

    meters

    array([ 7.57230404e-01,  4.01321326e-01,  5.27046277e-01,  8.08321739e-01,
           -7.41766900e-01,  4.14097602e-01, -5.25538870e-01, -6.75346943e-01,
           -6.19740858e-01, -7.39206795e-02, -2.35778754e-01,  2.62984558e-01,
            7.39206795e-02, -4.14097602e-01, -6.91728674e-01, -1.89676342e-01,
            4.14097602e-01, -8.12232862e-01, -6.12437841e-01,  7.96626035e-01,
            3.82883921e-01, -7.05676416e-01, -6.57345299e-01, -4.72848836e-01,
           -5.25538870e-01,  6.27864493e-01,  7.96626035e-01,  7.34992600e-01,
            8.08321739e-01,  5.74335365e-01,  6.27003678e-01,  5.72122502e-01,
            7.72175666e-01,  7.45355992e-01,  5.24235541e-01, -5.27046277e-01,
            5.76167974e-01,  4.51252400e-01,  6.19740858e-01, -2.16365424e-01,
           -4.65017621e-01,  7.50405317e-01, -5.74335365e-01, -5.72122502e-01,
            7.31034186e-01, -7.96626035e-01,  6.72570339e-01,  6.75346943e-01,
           -6.88619146e-01, -3.82883921e-01, -2.18531350e-01, -7.31034186e-01,
           -4.65017621e-01,  6.27864493e-01,  3.53591152e-01,  4.33024273e-17,
            3.83034410e-02, -2.87636956e-01,  4.97349187e-17,  6.72570339e-01,
            6.46043182e-01, -5.27046277e-01, -6.27003678e-01, -6.12437841e-01,
           -3.33760088e-01, -4.14097602e-01, -7.79684453e-01,  5.27046277e-01,
           -7.05676416e-01, -7.50405317e-01, -5.78404787e-01, -7.16325433e-01,
           -7.96127424e-02,  7.30576629e-02, -6.19740858e-01, -3.51358383e-01,
           -8.08321739e-01, -2.18531350e-01, -5.76167974e-01,  3.10828060e-01,
    ...
           -7.79684453e-01,  4.15671329e-01,  7.03308286e-01, -7.72175666e-01,
            4.51252400e-01,  4.65017621e-01, -5.76167974e-01,  7.72175666e-01,
           -3.53591152e-01, -5.74335365e-01,  6.12437841e-01, -5.72122502e-01,
            3.10828060e-01,  7.39206795e-02,  5.78404787e-01, -4.15671329e-01,
            5.72122502e-01,  3.82883921e-01, -5.15275073e-01, -7.13261214e-01,
           -1.58458771e-01, -4.01321326e-01,  2.87636956e-01, -7.13261214e-01,
            7.96127424e-02, -7.57230404e-01,  8.12232862e-01,  6.12437841e-01,
           -3.10828060e-01, -4.51252400e-01,  2.16365424e-01,  1.47031469e-01,
           -7.03308286e-01, -2.87636956e-01, -6.72570339e-01, -2.85235390e-01,
            6.69564182e-01,  7.05676416e-01, -1.89676342e-01,  7.13261214e-01,
            3.51358383e-01,  1.58458771e-01, -6.69564182e-01,  7.79684453e-01,
            6.75346943e-01,  3.33760088e-01, -4.72848836e-01, -5.24235541e-01,
           -7.34992600e-01,  1.89676342e-01,  2.35778754e-01,  3.53591152e-01,
            7.07182304e-01,  7.34992600e-01,  1.58458771e-01, -4.73197324e-01,
           -1.58458771e-01, -3.53591152e-01,  6.91728674e-01,  7.16325433e-01,
           -2.35778754e-01,  1.14541440e-01, -7.16325433e-01, -7.77258388e-01,
            4.72848836e-01,  5.15275073e-01, -6.75346943e-01,  1.14541440e-01,
           -3.82883921e-01, -4.73197324e-01, -7.30576629e-02,  7.79684453e-01,
            1.45411741e-01,  3.83034410e-02, -7.34992600e-01, -2.62984558e-01,
            7.05676416e-01, -7.57230404e-01, -7.30576629e-02,  5.74335365e-01])

  • node_y
    (n_node)
    float64
    -0.1897 -0.6696 ... -0.7418 0.5743

    standard_name :

    y

    long name :

    Cartesian y location of the corner nodes of each face

    units :

    meters

    array([-1.89676342e-01, -6.69564182e-01, -5.27046277e-01, -7.96127424e-02,
            7.30576629e-02,  6.19740858e-01,  4.73197324e-01,  4.51252400e-01,
           -4.14097602e-01,  7.03308286e-01, -7.77258388e-01, -7.34992600e-01,
           -7.03308286e-01,  6.19740858e-01, -1.47031469e-01,  7.57230404e-01,
           -6.19740858e-01, -9.94698375e-17,  3.53591152e-01, -1.58458771e-01,
            7.16325433e-01, -3.33760088e-01, -3.51358383e-01, -5.76167974e-01,
           -4.73197324e-01,  5.15275073e-01,  1.58458771e-01,  2.62984558e-01,
            7.96127424e-02, -5.74335365e-01, -4.65017621e-01, -4.15671329e-01,
            1.14541440e-01,  0.00000000e+00, -5.78404787e-01, -5.27046277e-01,
            4.72848836e-01,  6.75346943e-01, -4.14097602e-01,  7.13261214e-01,
           -6.27003678e-01, -3.10828060e-01,  5.74335365e-01, -4.15671329e-01,
            1.45411741e-01, -1.58458771e-01,  2.18531350e-01, -4.51252400e-01,
            2.85235390e-01,  7.16325433e-01,  6.72570339e-01, -1.45411741e-01,
            6.27003678e-01, -5.15275073e-01,  6.12437841e-01,  7.07182304e-01,
            7.79684453e-01,  6.46043182e-01,  8.12232862e-01, -2.18531350e-01,
           -2.87636956e-01,  5.27046277e-01,  4.65017621e-01, -3.53591152e-01,
           -7.05676416e-01, -6.19740858e-01, -3.83034410e-02,  5.27046277e-01,
            3.33760088e-01, -3.10828060e-01,  5.24235541e-01, -3.82883921e-01,
           -8.08321739e-01,  7.41766900e-01,  4.14097602e-01,  6.57345299e-01,
            7.96127424e-02, -6.72570339e-01, -4.72848836e-01, -7.50405317e-01,
    ...
            3.83034410e-02, -5.72122502e-01,  7.39206795e-02, -1.14541440e-01,
           -6.75346943e-01, -6.27003678e-01,  4.72848836e-01, -1.14541440e-01,
           -6.12437841e-01, -5.74335365e-01,  3.53591152e-01,  4.15671329e-01,
            7.50405317e-01,  7.03308286e-01,  5.24235541e-01, -5.72122502e-01,
            4.15671329e-01, -7.16325433e-01, -6.27864493e-01, -2.16365424e-01,
           -7.96626035e-01,  6.69564182e-01,  6.46043182e-01,  2.16365424e-01,
            8.08321739e-01, -1.89676342e-01,  0.00000000e+00, -3.53591152e-01,
            7.50405317e-01, -6.75346943e-01, -7.13261214e-01,  6.91728674e-01,
            7.39206795e-02, -6.46043182e-01, -2.18531350e-01,  6.88619146e-01,
            4.01321326e-01,  3.33760088e-01, -7.57230404e-01, -2.16365424e-01,
            6.57345299e-01, -7.96626035e-01,  4.01321326e-01, -3.83034410e-02,
            4.51252400e-01, -7.05676416e-01,  5.76167974e-01, -5.78404787e-01,
           -2.62984558e-01, -7.57230404e-01, -7.77258388e-01, -6.12437841e-01,
            0.00000000e+00, -2.62984558e-01,  7.96626035e-01,  5.25538870e-01,
            7.96626035e-01,  6.12437841e-01, -1.47031469e-01, -3.82883921e-01,
            7.77258388e-01, -7.72175666e-01,  3.82883921e-01,  2.35778754e-01,
            5.76167974e-01, -6.27864493e-01, -4.51252400e-01,  7.72175666e-01,
           -7.16325433e-01, -5.25538870e-01,  7.41766900e-01,  3.83034410e-02,
            7.31034186e-01, -7.79684453e-01,  2.62984558e-01, -7.34992600e-01,
           -3.33760088e-01,  1.89676342e-01, -7.41766900e-01,  5.74335365e-01])

  • node_z
    (n_node)
    float64
    0.625 0.625 ... 0.6667 0.5833

    standard_name :

    z

    long name :

    Cartesian z location of the corner nodes of each face

    units :

    meters

    array([0.625     , 0.625     , 0.66666667, 0.58333333, 0.66666667,
           0.66666667, 0.70703125, 0.58333333, 0.66666667, 0.70703125,
           0.58333333, 0.625     , 0.70703125, 0.66666667, 0.70703125,
           0.625     , 0.66666667, 0.58333333, 0.70703125, 0.58333333,
           0.58333333, 0.625     , 0.66666667, 0.66666667, 0.70703125,
           0.58333333, 0.58333333, 0.625     , 0.58333333, 0.58333333,
           0.625     , 0.70703125, 0.625     , 0.66666667, 0.625     ,
           0.66666667, 0.66666667, 0.58333333, 0.66666667, 0.66666667,
           0.625     , 0.58333333, 0.58333333, 0.70703125, 0.66666667,
           0.58333333, 0.70703125, 0.58333333, 0.66666667, 0.58333333,
           0.70703125, 0.66666667, 0.625     , 0.58333333, 0.70703125,
           0.70703125, 0.625     , 0.70703125, 0.58333333, 0.70703125,
           0.70703125, 0.66666667, 0.625     , 0.70703125, 0.625     ,
           0.66666667, 0.625     , 0.66666667, 0.625     , 0.58333333,
           0.625     , 0.58333333, 0.58333333, 0.66666667, 0.66666667,
           0.66666667, 0.58333333, 0.70703125, 0.66666667, 0.58333333,
           0.66666667, 0.66666667, 0.66666667, 0.58333333, 0.70703125,
           0.58333333, 0.625     , 0.70703125, 0.70703125, 0.625     ,
           0.70703125, 0.58333333, 0.66666667, 0.58333333, 0.625     ,
           0.625     , 0.70703125, 0.58333333, 0.66666667, 0.66666667,
    ...
           0.70703125, 0.66666667, 0.66666667, 0.58333333, 0.66666667,
           0.58333333, 0.625     , 0.625     , 0.58333333, 0.66666667,
           0.58333333, 0.66666667, 0.70703125, 0.66666667, 0.66666667,
           0.625     , 0.66666667, 0.625     , 0.70703125, 0.70703125,
           0.625     , 0.58333333, 0.625     , 0.66666667, 0.625     ,
           0.70703125, 0.58333333, 0.70703125, 0.70703125, 0.58333333,
           0.70703125, 0.625     , 0.70703125, 0.70703125, 0.58333333,
           0.58333333, 0.66666667, 0.58333333, 0.625     , 0.70703125,
           0.66666667, 0.58333333, 0.625     , 0.58333333, 0.70703125,
           0.58333333, 0.58333333, 0.66666667, 0.70703125, 0.70703125,
           0.70703125, 0.70703125, 0.66666667, 0.625     , 0.625     ,
           0.625     , 0.66666667, 0.66666667, 0.58333333, 0.625     ,
           0.625     , 0.58333333, 0.625     , 0.66666667, 0.625     ,
           0.625     , 0.625     , 0.58333333, 0.70703125, 0.70703125,
           0.625     , 0.58333333, 0.70703125, 0.58333333, 0.70703125,
           0.70703125, 0.58333333, 0.58333333, 0.625     , 0.58333333,
           0.58333333, 0.66666667, 0.58333333, 0.58333333, 0.625     ,
           0.58333333, 0.70703125, 0.66666667, 0.625     , 0.66666667,
           0.625     , 0.625     , 0.625     , 0.625     , 0.625     ,
           0.66666667, 0.58333333])

• Connectivity: (1)
  • face_node_connectivity
    (n_face, n_max_face_nodes)
    int64
    114 67 186 251 ... 245 166 123 85

    cf_role :

    face_node_connectivity

    long name :

    Maps every face to its corner nodes.

    start_index :

    0

    _FillValue :

    -9223372036854775808

    array([[114,  67, 186, 251],
           [130, 164, 140, 184],
           [239, 244, 150, 226],
           [ 56,  73, 239, 196],
           [149,  55,  73,  56],
           [ 73, 185, 244, 239],
           [150,  99, 130,  83],
           [244, 203,  99, 150],
           [ 99, 131, 164, 130],
           [170,   5, 138,  37],
           [140, 212, 170,  20],
           [164, 194, 212, 140],
           [212,  54,   5, 170],
           [138, 236, 114, 165],
           [  5, 105, 236, 138],
           [236, 101,  67, 114],
           [209, 157,  27, 158],
           [161, 113, 208, 216],
           [186,  36, 161,  25],
           [ 67, 167,  36, 186],
    ...
           [100, 107,   2, 147],
           [217,  81,  11,  79],
           [177,  16,   1, 176],
           [ 34, 136, 177, 237],
           [  2, 110, 136,  34],
           [136, 173,  16, 177],
           [  1, 126, 217, 189],
           [ 16, 223, 126,   1],
           [126, 124,  81, 217],
           [221, 115, 233, 213],
           [ 11, 202, 221, 222],
           [ 81, 146, 202,  11],
           [202,  88, 115, 221],
           [233, 154, 245, 139],
           [115,  12, 154, 233],
           [154, 103, 166, 245],
           [243,  33, 215, 198],
           [ 86, 149,  56,  58],
           [ 66, 168, 172,  17],
           [245, 166, 123,  85]])

• Descriptors: (1)
  • n_nodes_per_face
    (n_face)
    int64
    4 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4

    cf_role :

    n_nodes_per_face

    long name :

    Number of nodes per face

    array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
           4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
           4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
           4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
           4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
           4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4])

• Attributes: (5)

  zoom :

  4

  n_side :

  16

  n_pix :

  3072

  nest :

  True

  source_grid_spec :

  HEALPix

import geoviews.feature as gf

uxda_lat.isel(time=0).plot(
    rasterize=False,
    projection=projection,
    global_extent=True,
    cmap="inferno",
    clim=(220, 310),
    features=["coastline"],
    title=f"Global surface temperature cross-section at {boulder_lat} degrees latitude",
    width=700,
) * gf.grid(projection=projection)

/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_graticules_30.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

WARNING:param.GeoOverlayPlot01107: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.

Let’s also look at the mean of the cross-section:

print(
    f"Mean at {boulder_lat} degrees lat (Boulder, CO, USA): {uxda_lat.mean().values} K"
)

Mean at 40.019 degrees lat (Boulder, CO, USA): 286.452880859375 K

Latitude interval

uxda_lat_interval = uxda_coarse.cross_section.constant_latitude_interval(
    [boulder_lat - 15, boulder_lat + 15]
)

uxda_lat_interval.isel(time=0).plot(
    rasterize=False,
    projection=projection,
    global_extent=True,
    cmap="inferno",
    clim=(220, 310),
    features=["coastline"],
    title=f"Global surface temperature cross-section at the latitude interval [{boulder_lat-5},{boulder_lat+5}] degrees",
    width=700,
) * gf.grid(projection=projection)

WARNING:param.GeoOverlayPlot01331: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.

print(
    f"Mean at the latitude interval of [{boulder_lat-5},{boulder_lat+5}] degrees (-/+15 degrees Boulder, CO, USA): {uxda_lat_interval.mean().values} K"
)

Mean at the latitude interval of [35.019,45.019] degrees (-/+15 degrees Boulder, CO, USA): 286.20574951171875 K

Non-conservative zonal mean

Calculating the zonal mean is easy by providing the latitude range between -90 and 90 degrees with a step size in degrees:

%%time
zonal_mean_ts = uxda.isel(time=0).zonal_mean(lat=(-90, 90, 5))

CPU times: user 1min 4s, sys: 459 ms, total: 1min 5s
Wall time: 27.2 s

(
    uxda.isel(time=0).plot(
        cmap="inferno",
        # periodic_elements="split",
        height=300,
        width=600,
        colorbar=False,
        ylim=(-90, 90),
    )
    + zonal_mean_ts.plot.line(
        x="ts_zonal_mean",
        y="latitudes",
        height=300,
        width=180,
        ylabel="",
        ylim=(-90, 90),
        xlim=(220, 310),
        # xticks=[220, 250, 280, 310],
        yticks=[-90, -45, 0, 45, 90],
        grid=True,
    )
).opts(title="Temperature and its Zonal means at every 5 degree latitude")

Remapping

Now, we will be looking into one of many possible use cases where remapping would be helpful.

The data set we have been using in this section so far belongs to the newer ICON simulation, icon_d3hp003, while there is an older simulation as well, icon_ngc4008, in the same catalog. They are both stored in the HEALPix format in this case, but for most of the model intercomparison workflows in general, they might not be even so. UXarray would still be helpful to remap of those model outputs to other and then make comparisons since it can support several most commonly used unstructrued grid formats.

In this particular case, we still have some use for UXarray’s remapping such that the newer simulation has the zoom = 9 as the maximum available resolution, while the older one has zoom = 10 available. Unfortunately at zoom = 10 though, there is no actual data simulated for ts, the surface temperature. If there was, we could remap the newer simulation’s output to that one, so we could have both of them at zoom = 10, and then we could look into the difference between them for instance. Let’s pretend the highest zoom-level in the newer data is zoom = 8 then, and remap that one into the older simulation’s grid.

Let’s start with opening the older simulation run first:

model_run_older = cat.online.icon_ngc4008

ds_older = model_run_older(zoom=8, time="P1D").to_dask()

uxds_older = ux.UxDataset.from_healpix(ds_older)
uxds_older

/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:21: 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`.
  'dims': dict(self._ds.dims),

<xarray.UxDataset> Size: 58TB
Dimensions:                              (time: 10958, depth_half: 73,
                                          n_face: 786432, level_full: 90,
                                          crs: 1, depth_full: 72,
                                          soil_depth_water_level: 5,
                                          level_half: 91,
                                          soil_depth_energy_level: 5)
Coordinates:
  * crs                                  (crs) float32 4B nan
  * depth_full                           (depth_full) float32 288B 1.0 ... 5....
  * depth_half                           (depth_half) float32 292B 0.0 ... 5....
  * level_full                           (level_full) int32 360B 1 2 3 ... 89 90
  * level_half                           (level_half) int32 364B 1 2 3 ... 90 91
  * soil_depth_energy_level              (soil_depth_energy_level) float32 20B ...
  * soil_depth_water_level               (soil_depth_water_level) float32 20B ...
  * time                                 (time) datetime64[ns] 88kB 2020-01-0...
Dimensions without coordinates: n_face
Data variables: (12/103)
    A_tracer_v_to                        (time, depth_half, n_face) float32 3TB ...
    FrshFlux_IceSalt                     (time, n_face) float32 34GB ...
    FrshFlux_TotalIce                    (time, n_face) float32 34GB ...
    Qbot                                 (time, n_face) float32 34GB ...
    Qtop                                 (time, n_face) float32 34GB ...
    Wind_Speed_10m                       (time, n_face) float32 34GB ...
    ...                                   ...
    vas                                  (time, n_face) float32 34GB ...
    w                                    (time, depth_half, n_face) float32 3TB ...
    wa_phy                               (time, level_half, n_face) float32 3TB ...
    zg                                   (level_full, n_face) float32 283MB ...
    zghalf                               (level_half, n_face) float32 286MB ...
    zos                                  (time, n_face) float32 34GB ...

<xarray.UxDataset> Size: 58TB
Dimensions:                              (time: 10958, depth_half: 73,
                                          n_face: 786432, level_full: 90,
                                          crs: 1, depth_full: 72,
                                          soil_depth_water_level: 5,
                                          level_half: 91,
                                          soil_depth_energy_level: 5)
Coordinates:
  * crs                                  (crs) float32 4B nan
  * depth_full                           (depth_full) float32 288B 1.0 ... 5....
  * depth_half                           (depth_half) float32 292B 0.0 ... 5....
  * level_full                           (level_full) int32 360B 1 2 3 ... 89 90
  * level_half                           (level_half) int32 364B 1 2 3 ... 90 91
  * soil_depth_energy_level              (soil_depth_energy_level) float32 20B ...
  * soil_depth_water_level               (soil_depth_water_level) float32 20B ...
  * time                                 (time) datetime64[ns] 88kB 2020-01-0...
Dimensions without coordinates: n_face
Data variables: (12/103)
    A_tracer_v_to                        (time, depth_half, n_face) float32 3TB ...
    FrshFlux_IceSalt                     (time, n_face) float32 34GB ...
    FrshFlux_TotalIce                    (time, n_face) float32 34GB ...
    Qbot                                 (time, n_face) float32 34GB ...
    Qtop                                 (time, n_face) float32 34GB ...
    Wind_Speed_10m                       (time, n_face) float32 34GB ...
    ...                                   ...
    vas                                  (time, n_face) float32 34GB ...
    w                                    (time, depth_half, n_face) float32 3TB ...
    wa_phy                               (time, level_half, n_face) float32 3TB ...
    zg                                   (level_full, n_face) float32 283MB ...
    zghalf                               (level_half, n_face) float32 286MB ...
    zos                                  (time, n_face) float32 34GB ...

uxarray.UxDataset

• Dimensions:
  • time: 10958
  • depth_half: 73
  • n_face: 786432
  • level_full: 90
  • crs: 1
  • depth_full: 72
  • soil_depth_water_level: 5
  • level_half: 91
  • soil_depth_energy_level: 5
• Coordinates: (8)
  • crs
    (crs)
    float32
    nan

    grid_mapping_name :

    healpix

    healpix_nside :

    256

    healpix_order :

    nest

    array([nan], dtype=float32)

  • depth_full
    (depth_full)
    float32
    1.0 3.1 ... 5.546e+03 5.816e+03

    axis :

    Z

    long_name :

    depth_below_sea

    positive :

    down

    standard_name :

    depth

    units :

    m

    array([1.00000e+00, 3.10000e+00, 5.45000e+00, 8.10000e+00, 1.10500e+01,
           1.43500e+01, 1.80500e+01, 2.22000e+01, 2.68500e+01, 3.20000e+01,
           3.76500e+01, 4.38000e+01, 5.05500e+01, 5.79500e+01, 6.60000e+01,
           7.48000e+01, 8.44500e+01, 9.50000e+01, 1.06500e+02, 1.19100e+02,
           1.32900e+02, 1.47950e+02, 1.64350e+02, 1.82250e+02, 2.01800e+02,
           2.23150e+02, 2.46450e+02, 2.71850e+02, 2.99550e+02, 3.29750e+02,
           3.62650e+02, 3.98450e+02, 4.37400e+02, 4.79250e+02, 5.23450e+02,
           5.69500e+02, 6.17100e+02, 6.66300e+02, 7.17150e+02, 7.69700e+02,
           8.24000e+02, 8.80100e+02, 9.38050e+02, 9.97900e+02, 1.05975e+03,
           1.12450e+03, 1.19410e+03, 1.27070e+03, 1.35630e+03, 1.45170e+03,
           1.55680e+03, 1.67195e+03, 1.79645e+03, 1.92950e+03, 2.07140e+03,
           2.22245e+03, 2.38290e+03, 2.55290e+03, 2.73250e+03, 2.92175e+03,
           3.12060e+03, 3.32885e+03, 3.54625e+03, 3.77245e+03, 4.00695e+03,
           4.24915e+03, 4.49830e+03, 4.75355e+03, 5.01400e+03, 5.27860e+03,
           5.54625e+03, 5.81575e+03], dtype=float32)

  • depth_half
    (depth_half)
    float32
    0.0 2.0 4.2 ... 5.681e+03 5.951e+03

    axis :

    Z

    long_name :

    depth_below_sea

    positive :

    down

    standard_name :

    depth

    units :

    m

    array([0.0000e+00, 2.0000e+00, 4.2000e+00, 6.7000e+00, 9.5000e+00, 1.2600e+01,
           1.6100e+01, 2.0000e+01, 2.4400e+01, 2.9300e+01, 3.4700e+01, 4.0600e+01,
           4.7000e+01, 5.4100e+01, 6.1800e+01, 7.0200e+01, 7.9400e+01, 8.9500e+01,
           1.0050e+02, 1.1250e+02, 1.2570e+02, 1.4010e+02, 1.5580e+02, 1.7290e+02,
           1.9160e+02, 2.1200e+02, 2.3430e+02, 2.5860e+02, 2.8510e+02, 3.1400e+02,
           3.4550e+02, 3.7980e+02, 4.1710e+02, 4.5770e+02, 5.0080e+02, 5.4610e+02,
           5.9290e+02, 6.4130e+02, 6.9130e+02, 7.4300e+02, 7.9640e+02, 8.5160e+02,
           9.0860e+02, 9.6750e+02, 1.0283e+03, 1.0912e+03, 1.1578e+03, 1.2304e+03,
           1.3110e+03, 1.4016e+03, 1.5018e+03, 1.6118e+03, 1.7321e+03, 1.8608e+03,
           1.9982e+03, 2.1446e+03, 2.3003e+03, 2.4655e+03, 2.6403e+03, 2.8247e+03,
           3.0188e+03, 3.2224e+03, 3.4353e+03, 3.6572e+03, 3.8877e+03, 4.1262e+03,
           4.3721e+03, 4.6245e+03, 4.8826e+03, 5.1454e+03, 5.4118e+03, 5.6807e+03,
           5.9508e+03], dtype=float32)

  • level_full
    (level_full)
    int32
    1 2 3 4 5 6 7 ... 85 86 87 88 89 90

    axis :

    Z

    long_name :

    generalized_height

    positive :

    down

    standard_name :

    height

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
           55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
           73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90],
          dtype=int32)

  • level_half
    (level_half)
    int32
    1 2 3 4 5 6 7 ... 86 87 88 89 90 91

    axis :

    Z

    long_name :

    generalized_height

    positive :

    down

    standard_name :

    height

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
           55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
           73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
           91], dtype=int32)

  • soil_depth_energy_level
    (soil_depth_energy_level)
    float32
    0.0325 0.192 0.7755 2.683 6.984

    axis :

    Z

    long_name :

    depth_below_land

    positive :

    down

    units :

    m

    array([0.0325, 0.192 , 0.7755, 2.683 , 6.984 ], dtype=float32)

  • soil_depth_water_level
    (soil_depth_water_level)
    float32
    0.0325 0.192 0.7755 2.683 6.984

    axis :

    Z

    long_name :

    depth_below_land

    positive :

    down

    units :

    m

    array([0.0325, 0.192 , 0.7755, 2.683 , 6.984 ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2020-01-02 ... 2050-01-01

    axis :

    T

    array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000',
           '2020-01-04T00:00:00.000000000', ..., '2049-12-30T00:00:00.000000000',
           '2049-12-31T00:00:00.000000000', '2050-01-01T00:00:00.000000000'],
          shape=(10958,), dtype='datetime64[ns]')

• Data variables: (103)
  • A_tracer_v_to
    (time, depth_half, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    sea water vertical diffusivity

    standard_name :

    ocean_vertical_diffusivity

    units :

    m2 s-1

    vgrid :

    depth_below_sea_half

    [629093695488 values with dtype=float32]

  • FrshFlux_IceSalt
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Salt volume flux due to sea ice change

    units :

    psu m s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • FrshFlux_TotalIce
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Freshwater Flux due to Sea Ice Change

    units :

    m s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • Qbot
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Conductive heat flux at ice-ocean interface

    units :

    W/m^2

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • Qtop
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Energy flux available for surface melting

    units :

    W/m^2

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • Wind_Speed_10m
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Wind Speed at 10m height

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_FrshFlux_Evaporation
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_FrshFlux_Precipitation
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_FrshFlux_Runoff
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_FrshFlux_SnowFall
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_HeatFlux_Latent
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    W/m2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_HeatFlux_LongWave
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    W/m2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_HeatFlux_Sensible
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    W/m2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_HeatFlux_ShortWave
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    W/m2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_HeatFlux_Total
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    W/m2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_stress_x
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    Pa

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_stress_xw
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    Pa

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_stress_y
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    Pa

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • atmos_fluxes_stress_yw
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    units :

    Pa

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • cli
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    specific cloud ice content

    units :

    kg kg-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • clivi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertically integrated cloud ice

    units :

    kg m-2

    vgrid :

    atmosphere

    [8617721856 values with dtype=float32]

  • cllvi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertically integrated cloud water

    units :

    kg m-2

    vgrid :

    atmosphere

    [8617721856 values with dtype=float32]

  • clw
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    specific cloud water content

    units :

    kg kg-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • conc
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    ice concentration in each ice class

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • delhi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Change in ice mean thickness due to thermodynamic effects

    units :

    m

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • dzghalf
    (level_full, n_face)
    float32
    ...

    long_name :

    vertical extent of model full layer

    standard_name :

    cell_thickness

    units :

    m

    [70778880 values with dtype=float32]

  • heatOceI
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Heat flux to ocean from the ice growth

    units :

    W/m^2

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • heatOceW
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    Heat flux to ocean from the atmosphere

    units :

    W/m^2

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • heat_content_seaice
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • heat_content_snow
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • heat_content_total
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hfls
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    latent heat flux

    standard_name :

    surface_downward_latent_heat_flux

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hfss
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    sensible heat flux

    standard_name :

    surface_downward_sensible_heat_flux

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    ice thickness

    units :

    m

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • hs
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    snow thickness

    units :

    m

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • hus
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    Specific humidity

    standard_name :

    specific_humidity

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • hydro_canopy_cond_limited_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_discharge_ocean_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    units :

    m3 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_drainage_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    units :

    kg m-2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_ice_soil_sl_box
    (time, soil_depth_water_level, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    Ice content in soil layers

    units :

    m

    vgrid :

    soil_depth_water

    [43088609280 values with dtype=float32]

  • hydro_runoff_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    surface runoff

    units :

    kg m-2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_snow_soil_dens_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    Density of snow on soil

    units :

    kg m-3

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_transpiration_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    Transpiration from surface

    units :

    kg m-2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_weq_snow_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    Water content of snow reservoir on surface

    units :

    m

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • hydro_wtr_soil_sl_box
    (time, soil_depth_water_level, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    Water content in soil layers

    units :

    m

    vgrid :

    soil_depth_water

    [43088609280 values with dtype=float32]

  • ice_u
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    zonal velocity

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • ice_v
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    meridional velocity

    units :

    m/s

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • mlotst
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_t

    units :

    m

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • newice
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    new ice growth in open water

    units :

    m

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • ocean_fraction_depth_full
    (depth_full, n_face)
    float32
    ...

    long_name :

    fraction of cell covered by ocean

    standard_name :

    ocean_area_fraction

    [56623104 values with dtype=float32]

  • ocean_fraction_depth_half
    (depth_half, n_face)
    float32
    ...

    long_name :

    fraction of cell covered by ocean

    standard_name :

    ocean_area_fraction

    [57409536 values with dtype=float32]

  • ocean_fraction_surface
    (n_face)
    float32
    ...

    long_name :

    fraction of cell covered by ocean

    standard_name :

    ocean_area_fraction

    [786432 values with dtype=float32]

  • pfull
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    standard_name :

    air_pressure

    units :

    Pa

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • phalf
    (time, level_half, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    standard_name :

    air_pressure

    units :

    Pa

    vgrid :

    reference_half

    [784212688896 values with dtype=float32]

  • pr
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    precipitation flux

    units :

    kg m-2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • pres_msl
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    mean sea level pressure

    units :

    Pa

    vgrid :

    meansea

    [8617721856 values with dtype=float32]

  • pres_sfc
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    surface pressure

    standard_name :

    surface_air_pressure

    units :

    Pa

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • prls
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • prw
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertically integrated water vapour

    units :

    kg m-2

    vgrid :

    atmosphere

    [8617721856 values with dtype=float32]

  • qg
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    specific graupel content

    units :

    kg kg-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • qgvi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertically integrated graupel

    units :

    kg m-2

    vgrid :

    atmosphere

    [8617721856 values with dtype=float32]

  • qr
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    specific rain content

    units :

    kg kg-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • qrvi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertically integrated rain

    units :

    kg m-2

    vgrid :

    atmosphere

    [8617721856 values with dtype=float32]

  • qs
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    specific snow content

    units :

    kg kg-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • qsvi
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertically integrated snow

    units :

    kg m-2

    vgrid :

    atmosphere

    [8617721856 values with dtype=float32]

  • qv2m
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    specific humidity in 2m

    units :

    kg kg-1

    vgrid :

    height_2m

    [8617721856 values with dtype=float32]

  • rlds
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    surface downwelling longwave radiation

    standard_name :

    surface_downwelling_longwave_flux_in_air

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • rlus
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    surface upwelling longwave radiation

    standard_name :

    surface_upwelling_longwave_flux_in_air

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • rlut
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    toa outgoing longwave radiation

    standard_name :

    toa_outgoing_longwave_flux

    units :

    W m-2

    vgrid :

    toa

    [8617721856 values with dtype=float32]

  • rsds
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    surface downwelling shortwave radiation

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • rsdt
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    toa incident shortwave radiation

    standard_name :

    toa_incoming_shortwave_flux

    units :

    W m-2

    vgrid :

    toa

    [8617721856 values with dtype=float32]

  • rsus
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    surface upwelling shortwave radiation

    standard_name :

    surface_upwelling_shortwave_flux_in_air

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • rsut
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    toa outgoing shortwave radiation

    standard_name :

    toa_outgoing_shortwave_flux

    units :

    W m-2

    vgrid :

    toa

    [8617721856 values with dtype=float32]

  • seb_forc_hflx_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    additional heat flux at surface

    units :

    W m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • seb_heat_cap_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    surface layer heat capacity

    units :

    J m-2 K-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • sfcwind
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    10m windspeed

    units :

    m s-1

    vgrid :

    height_10m

    [8617721856 values with dtype=float32]

  • sic
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    fraction of ocean covered by sea ice

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • sit
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    sea ice thickness

    units :

    m

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • snow_to_ice
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    amount of snow that is transformed to ice

    units :

    m

    vgrid :

    generic_ice

    [8617721856 values with dtype=float32]

  • so
    (time, depth_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    sea water salinity

    standard_name :

    sea_water_salinity

    units :

    psu

    vgrid :

    depth_below_sea

    [620475973632 values with dtype=float32]

  • sse_grnd_hflx_old_box
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    long_name :

    Ground heat flux (old)

    units :

    J m-2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • sse_t_soil_sl_box
    (time, soil_depth_energy_level, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    jsbach

    grid_mapping :

    crs

    standard_name :

    soil_temperature

    units :

    K

    vgrid :

    soil_depth_energy

    [43088609280 values with dtype=float32]

  • stretch_c
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    zstar surface stretch at cell center

    units :

    m

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • ta
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    Temperature

    standard_name :

    air_temperature

    units :

    K

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • tas
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    temperature in 2m

    standard_name :

    air_temperature

    units :

    K

    vgrid :

    height_2m

    [8617721856 values with dtype=float32]

  • tauu
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    u-momentum flux at the surface

    units :

    N m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • tauv
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    v-momentum flux at the surface

    units :

    N m-2

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • tke
    (time, depth_half, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    turbulent kinetic energy

    units :

    m2 s-2

    vgrid :

    depth_below_sea_half

    [629093695488 values with dtype=float32]

  • to
    (time, depth_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    sea water potential temperature

    standard_name :

    sea_water_potential_temperature

    units :

    degC

    vgrid :

    depth_below_sea

    [620475973632 values with dtype=float32]

  • ts
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    surface temperature

    standard_name :

    surface_temperature

    units :

    K

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • u
    (time, depth_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    u zonal velocity component

    units :

    m/s

    vgrid :

    depth_below_sea

    [620475973632 values with dtype=float32]

  • u_vint
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    vertically integrated barotropic zonal velocity

    units :

    m2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • ua
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    Zonal wind

    standard_name :

    eastward_wind

    units :

    m s-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • uas
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    zonal wind in 10m

    units :

    m s-1

    vgrid :

    height_10m

    [8617721856 values with dtype=float32]

  • v
    (time, depth_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    v meridional velocity component

    units :

    m/s

    vgrid :

    depth_below_sea

    [620475973632 values with dtype=float32]

  • v_vint
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    vertically integrated meridional zonal velocity

    units :

    m2 s-1

    vgrid :

    surface

    [8617721856 values with dtype=float32]

  • va
    (time, level_full, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    Meridional wind

    standard_name :

    northward_wind

    units :

    m s-1

    vgrid :

    reference

    [775594967040 values with dtype=float32]

  • vas
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    meridional wind in 10m

    units :

    m s-1

    vgrid :

    height_10m

    [8617721856 values with dtype=float32]

  • w
    (time, depth_half, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    vertical velocity at cells

    units :

    m/s

    vgrid :

    depth_below_sea_half

    [629093695488 values with dtype=float32]

  • wa_phy
    (time, level_half, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    atmo

    grid_mapping :

    crs

    long_name :

    vertical velocity

    units :

    m s-1

    vgrid :

    reference_half

    [784212688896 values with dtype=float32]

  • zg
    (level_full, n_face)
    float32
    ...

    long_name :

    geometric height at full level center

    standard_name :

    height

    units :

    m

    [70778880 values with dtype=float32]

  • zghalf
    (level_half, n_face)
    float32
    ...

    long_name :

    geometric height at half level center

    standard_name :

    height

    units :

    m

    [71565312 values with dtype=float32]

  • zos
    (time, n_face)
    float32
    ...

    cell_methods :

    time: mean cell: mean

    component :

    ocean

    grid_mapping :

    crs

    long_name :

    zstar sfc elevation at cell center

    units :

    m

    vgrid :

    surface

    [8617721856 values with dtype=float32]

• Indexes: (8)
  • crs
    PandasIndex

    PandasIndex(Index([nan], dtype='float32', name='crs'))

  • depth_full
    PandasIndex

    PandasIndex(Index([               1.0, 3.0999999046325684,  5.449999809265137,
            8.100000381469727, 11.050000190734863, 14.350000381469727,
           18.049999237060547, 22.200000762939453, 26.850000381469727,
                         32.0, 37.650001525878906,  43.79999923706055,
            50.54999923706055,  57.95000076293945,               66.0,
            74.80000305175781,  84.44999694824219,               95.0,
                        106.5,  119.0999984741211, 132.89999389648438,
            147.9499969482422, 164.35000610351562,             182.25,
            201.8000030517578, 223.14999389648438,  246.4499969482422,
            271.8500061035156, 299.54998779296875,             329.75,
            362.6499938964844, 398.45001220703125,  437.3999938964844,
                       479.25,  523.4500122070312,              569.5,
            617.0999755859375,  666.2999877929688,  717.1500244140625,
            769.7000122070312,              824.0,  880.0999755859375,
            938.0499877929688,  997.9000244140625,            1059.75,
                       1124.5, 1194.0999755859375,  1270.699951171875,
            1356.300048828125,  1451.699951171875,  1556.800048828125,
            1671.949951171875,  1796.449951171875,             1929.5,
             2071.39990234375,  2222.449951171875,   2382.89990234375,
             2552.89990234375,             2732.5,            2921.75,
             3120.60009765625,   3328.85009765625,            3546.25,
            3772.449951171875,  4006.949951171875,   4249.14990234375,
              4498.2998046875,    4753.5498046875,             5014.0,
             5278.60009765625,            5546.25,            5815.75],
          dtype='float32', name='depth_full'))

  • depth_half
    PandasIndex

    PandasIndex(Index([               0.0,                2.0,  4.199999809265137,
            6.699999809265137,                9.5, 12.600000381469727,
           16.100000381469727,               20.0, 24.399999618530273,
           29.299999237060547,  34.70000076293945, 40.599998474121094,
                         47.0, 54.099998474121094,  61.79999923706055,
            70.19999694824219,   79.4000015258789,               89.5,
                        100.5,              112.5, 125.69999694824219,
           140.10000610351562,  155.8000030517578, 172.89999389648438,
           191.60000610351562,              212.0,  234.3000030517578,
            258.6000061035156,  285.1000061035156,              314.0,
                        345.5, 379.79998779296875,  417.1000061035156,
           457.70001220703125, 500.79998779296875,  546.0999755859375,
            592.9000244140625,  641.2999877929688,  691.2999877929688,
                        743.0,  796.4000244140625,  851.5999755859375,
            908.5999755859375,              967.5,  1028.300048828125,
            1091.199951171875,  1157.800048828125, 1230.4000244140625,
                       1311.0, 1401.5999755859375,  1501.800048828125,
            1611.800048828125, 1732.0999755859375,  1860.800048828125,
            1998.199951171875,   2144.60009765625,  2300.300048828125,
                       2465.5,  2640.300048828125,  2824.699951171875,
            3018.800048828125,   3222.39990234375,  3435.300048828125,
            3657.199951171875,  3887.699951171875,    4126.2001953125,
             4372.10009765625,             4624.5,   4882.60009765625,
             5145.39990234375,    5411.7998046875,    5680.7001953125,
              5950.7998046875],
          dtype='float32', name='depth_half'))

  • level_full
    PandasIndex

    PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
           55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
           73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90],
          dtype='int32', name='level_full'))

  • level_half
    PandasIndex

    PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
           55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
           73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
           91],
          dtype='int32', name='level_half'))

  • soil_depth_energy_level
    PandasIndex

    PandasIndex(Index([0.032499998807907104,  0.19200000166893005,   0.7754999995231628,
              2.683000087738037,    6.984000205993652],
          dtype='float32', name='soil_depth_energy_level'))

  • soil_depth_water_level
    PandasIndex

    PandasIndex(Index([0.032499998807907104,  0.19200000166893005,   0.7754999995231628,
              2.683000087738037,    6.984000205993652],
          dtype='float32', name='soil_depth_water_level'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05',
                   '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09',
                   '2020-01-10', '2020-01-11',
                   ...
                   '2049-12-23', '2049-12-24', '2049-12-25', '2049-12-26',
                   '2049-12-27', '2049-12-28', '2049-12-29', '2049-12-30',
                   '2049-12-31', '2050-01-01'],
                  dtype='datetime64[ns]', name='time', length=10958, freq=None))

• Attributes: (0)

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_face: 786432
Grid Coordinates (Spherical):
  * face_lon: (786432,)
  * face_lat: (786432,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
Grid Descriptor Variables:

uxarray.UxDataset.uxgrid

• Dimensions:
  • n_face: 786432
• Spherical Coordinates: (2)
  • face_lon
    (n_face)
    float64
    45.0 45.18 44.82 ... -45.18 -45.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([ 45.        ,  45.17578125,  44.82421875, ..., -44.82421875,
           -45.17578125, -45.        ], shape=(786432,))

  • face_lat
    (n_face)
    float64
    0.1492 0.2984 ... -0.2984 -0.1492

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 0.14920793,  0.29841687,  0.29841687, ..., -0.29841687,
           -0.29841687, -0.14920793], shape=(786432,))

• Cartesian Coordinates: (0)
• Connectivity: (0)
• Descriptors: (0)
• Attributes: (5)

  zoom :

  8

  n_side :

  256

  n_pix :

  786432

  nest :

  True

  source_grid_spec :

  HEALPix

Plot the older simulation for reference

Let’s have a quick look at how the global surface temperature looks like in the older simulation’s output:

uxds_older["ts"].isel(time=0).plot(
    projection=projection,
    cmap="inferno",
    features=["borders", "coastline"],
    title="Global surface temperature (zoom = 8) - Older Simulation",
    width=700,
)

WARNING:param.GeoOverlayPlot01861: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.

Visually there does not seem to be a huge difference between this and the newer simulation’s output we had plotted in the very beginning.

Remap the old simulation output to the newer one

Let’s start remapping! For that, we will use the uxgrid of the newer simulation output as the destination grid and use an inverse distance weighted implementation:

%%time
uxda_older_remapped = uxds_older["ts"].isel(time=0).remap.inverse_distance_weighted(
    uxds.uxgrid, k=3, remap_to="face centers", coord_type="cartesian"
)

/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/uxarray/grid/coordinates.py:254: UserWarning: This cannot be guaranteed to work correctly on concave polygons
  warnings.warn("This cannot be guaranteed to work correctly on concave polygons")

CPU times: user 10.8 s, sys: 217 ms, total: 11 s
Wall time: 14.8 s

Plot the difference between the old and newer simulations

Now that we have the older and newer model outputs on the same grid, let’s look at the surface temperature differences between the two:

(uxda.isel(time=0) - uxda_older_remapped).plot(
    projection=projection,
    cmap="RdBu_r",
    features=["borders", "coastline"],
    title="Global surface temperature difference between older and newer simulations (zoom = 9)",
    clim=(-25,25),
    width=700,
)

WARNING:param.GeoOverlayPlot02186: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.