Introduction to Pipelines

# Here we'll use WeatherBench 2 ERA5 dataset as data source
from pyearthtools.data.download.weatherbench import WB2ERA5

# Now we import the pipeline model which is new in this example
import pyearthtools.pipeline

# and the operations module used as steps of the pipeline
import pyearthtools.pipeline.operations as ops

import pyearthtools.data.transforms.coordinates as coords

Data source

For this example, we are using the ERA5 datasets from WeatherBench 2, at a low resolution. Data is small and direclty fetched from public Google Cloud bucket.

We can first have a look at the full dataset to see which variables and levels we want to assemble in the pipeline.

WB2ERA5(resolution="64x32").dataset

<xarray.Dataset> Size: 175GB
Dimensions:                                           (time: 93544,
                                                       longitude: 64,
                                                       latitude: 32, level: 13)
Coordinates:
  * latitude                                          (latitude) float64 256B ...
  * level                                             (level) int64 104B 50 ....
  * longitude                                         (longitude) float64 512B ...
  * time                                              (time) datetime64[ns] 748kB ...
Data variables: (12/62)
    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>
    ...                                                ...
    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>
    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>
    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 93544
  • longitude: 64
  • latitude: 32
  • level: 13
• Coordinates: (4)
  • latitude
    (latitude)
    float64
    -87.19 -81.56 ... 81.56 87.19

    array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,
           -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,
            -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,
            30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,
            70.3125,  75.9375,  81.5625,  87.1875])

  • level
    (level)
    int64
    50 100 150 200 ... 700 850 925 1000

    array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,
           1000])

  • longitude
    (longitude)
    float64
    0.0 5.625 11.25 ... 348.8 354.4

    array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,
            45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,
            90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,
           135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,
           180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,
           225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,
           270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,
           315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])

  • time
    (time)
    datetime64[ns]
    1959-01-01 ... 2023-01-10T18:00:00

    array(['1959-01-01T00:00:00.000000000', '1959-01-01T06:00:00.000000000',
           '1959-01-01T12:00:00.000000000', ..., '2023-01-10T06:00:00.000000000',
           '2023-01-10T12:00:00.000000000', '2023-01-10T18:00:00.000000000'],
          shape=(93544,), dtype='datetime64[ns]')

• Data variables: (62)
  • 10m_u_component_of_wind
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    units :

    m s**-1

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    units :

    m s**-1

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_dewpoint_temperature
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    2 metre dewpoint temperature

    short_name :

    d2m

    units :

    K

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    units :

    K

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • above_ground
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ageostrophic_wind_speed
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • angle_of_sub_gridscale_orography
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Angle of sub-gridscale orography

    short_name :

    anor

    units :

    radians

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • anisotropy_of_sub_gridscale_orography
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Anisotropy of sub-gridscale orography

    short_name :

    isor

    units :

    ~

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • boundary_layer_height
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Boundary layer height

    short_name :

    blh

    units :

    m

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • divergence
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • eddy_kinetic_energy
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential_at_surface
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geostrophic_wind_speed
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • high_vegetation_cover
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    High vegetation cover

    short_name :

    cvh

    units :

    (0 - 1)

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • integrated_vapor_transport
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lake_cover
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Lake cover

    short_name :

    cl

    units :

    (0 - 1)

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • land_sea_mask
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Land-sea mask

    short_name :

    lsm

    standard_name :

    land_binary_mask

    units :

    (0 - 1)

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lapse_rate
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • leaf_area_index_high_vegetation
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Leaf area index, high vegetation

    short_name :

    lai_hv

    units :

    m**2 m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • leaf_area_index_low_vegetation
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Leaf area index, low vegetation

    short_name :

    lai_lv

    units :

    m**2 m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • low_vegetation_cover
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Low vegetation cover

    short_name :

    cvl

    units :

    (0 - 1)

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_surface_latent_heat_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean surface latent heat flux

    short_name :

    mslhf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_surface_net_long_wave_radiation_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean surface net long-wave radiation flux

    short_name :

    msnlwrf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_surface_net_short_wave_radiation_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean surface net short-wave radiation flux

    short_name :

    msnswrf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_surface_sensible_heat_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean surface sensible heat flux

    short_name :

    msshf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_top_downward_short_wave_radiation_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean top downward short-wave radiation flux

    short_name :

    mtdwswrf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_top_net_long_wave_radiation_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean top net long-wave radiation flux

    short_name :

    mtnlwrf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_top_net_short_wave_radiation_flux
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean top net short-wave radiation flux

    short_name :

    mtnswrf

    units :

    W m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_vertically_integrated_moisture_divergence
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Mean vertically integrated moisture divergence

    short_name :

    mvimd

    units :

    kg m**-2 s**-1

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • potential_vorticity
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    Potential vorticity

    short_name :

    pv

    units :

    K m**2 kg**-1 s**-1

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • relative_humidity
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_ice_cover
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Sea ice area fraction

    short_name :

    siconc

    standard_name :

    sea_ice_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_surface_temperature
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Sea surface temperature

    short_name :

    sst

    units :

    K

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • slope_of_sub_gridscale_orography
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Slope of sub-gridscale orography

    short_name :

    slor

    units :

    ~

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • snow_depth
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Snow depth

    short_name :

    sd

    standard_name :

    lwe_thickness_of_surface_snow_amount

    units :

    m of water equivalent

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_type
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Soil type

    short_name :

    slt

    units :

    ~

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • standard_deviation_of_filtered_subgrid_orography
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Standard deviation of filtered subgrid orography

    short_name :

    sdfor

    units :

    m

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • standard_deviation_of_orography
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Standard deviation of orography

    short_name :

    sdor

    units :

    m

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_pressure
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Surface pressure

    short_name :

    sp

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_cloud_cover
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Total cloud cover

    short_name :

    tcc

    standard_name :

    cloud_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_column_vapor
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_column_water
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Total column water

    short_name :

    tcw

    units :

    kg m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_column_water_vapour
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Total column vertically-integrated water vapour

    short_name :

    tcwv

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    kg m**-2

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_12hr
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • type_of_high_vegetation
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Type of high vegetation

    short_name :

    tvh

    units :

    ~

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • type_of_low_vegetation
    (longitude, latitude)
    float32
    dask.array<chunksize=(64, 32), meta=np.ndarray>

    long_name :

    Type of low vegetation

    short_name :

    tvl

    units :

    ~

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (64, 32) (64, 32) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vertical_velocity
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    long_name :

    Vertical velocity

    short_name :

    w

    standard_name :

    lagrangian_tendency_of_air_pressure

    units :

    Pa s**-1

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • volumetric_soil_water_layer_1
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Volumetric soil water layer 1

    short_name :

    swvl1

    units :

    m**3 m**-3

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • volumetric_soil_water_layer_2
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Volumetric soil water layer 2

    short_name :

    swvl2

    units :

    m**3 m**-3

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • volumetric_soil_water_layer_3
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Volumetric soil water layer 3

    short_name :

    swvl3

    units :

    m**3 m**-3

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • volumetric_soil_water_layer_4
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(100, 64, 32), meta=np.ndarray>

    long_name :

    Volumetric soil water layer 4

    short_name :

    swvl4

    units :

    m**3 m**-3

    Array Chunk Bytes 730.81 MiB 800.00 kiB Shape (93544, 64, 32) (100, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vorticity
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 9.28 GiB 10.16 MiB Shape (93544, 13, 64, 32) (100, 13, 64, 32) Dask graph 936 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,
            -70.31249999999999,  -64.68750000000001,            -59.0625,
                      -53.4375,            -47.8125,            -42.1875,
                      -36.5625, -30.937499999999996, -25.312500000000004,
           -19.687499999999996, -14.062499999999991,  -8.437499999999996,
            -2.812500000000003,   2.812500000000003,   8.437500000000009,
            14.062500000000004,  19.687499999999996,  25.312500000000004,
             30.93750000000001,  36.562499999999986,             42.1875,
                       47.8125,             53.4375,  59.062500000000014,
             64.68750000000001,             70.3125,             75.9375,
             81.56249999999997,   87.18750000000003],
          dtype='float64', name='latitude'))

  • level
    PandasIndex

    PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int64', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([               0.0,              5.625,              11.25,
                       16.875,               22.5,             28.125,
                        33.75,             39.375,               45.0,
                       50.625,              56.25,  61.87499999999999,
                         67.5,             73.125,              78.75,
                       84.375,               90.0,             95.625,
                       101.25,            106.875,              112.5,
                      118.125, 123.74999999999999,            129.375,
                        135.0,            140.625,             146.25,
                      151.875,              157.5,            163.125,
                       168.75,            174.375,              180.0,
                      185.625,             191.25,            196.875,
                        202.5,            208.125,             213.75,
                      219.375,              225.0, 230.62499999999997,
                       236.25,            241.875, 247.49999999999997,
                      253.125,             258.75,            264.375,
                        270.0,            275.625,             281.25,
                      286.875,              292.5,            298.125,
                       303.75,            309.375,              315.0,
                      320.625,             326.25,            331.875,
                        337.5,            343.125,             348.75,
                      354.375],
          dtype='float64', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1959-01-01 00:00:00', '1959-01-01 06:00:00',
                   '1959-01-01 12:00:00', '1959-01-01 18:00:00',
                   '1959-01-02 00:00:00', '1959-01-02 06:00:00',
                   '1959-01-02 12:00:00', '1959-01-02 18:00:00',
                   '1959-01-03 00:00:00', '1959-01-03 06:00:00',
                   ...
                   '2023-01-08 12:00:00', '2023-01-08 18:00:00',
                   '2023-01-09 00:00:00', '2023-01-09 06:00:00',
                   '2023-01-09 12:00:00', '2023-01-09 18:00:00',
                   '2023-01-10 00:00:00', '2023-01-10 06:00:00',
                   '2023-01-10 12:00:00', '2023-01-10 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=93544, freq=None))

• Attributes: (0)

Create a data preparation pipeline.

We can use pipelines to create a reproducable and explainable process to prepare data for specific tasks. Pipelines could also be shared as templates to complete similar work.

The pipeline below:

1. Selects the ‘u’ and ‘v’ wind variables at 500 and 850 hPa, as well as 2 meter temperature.

2. Selects the ‘geopotential’ and ‘vorticity’ atmospheric variables at 850 hPa.

3. Merges these into a single dataset.

4. Sorts the variables into a specified order.

5. Applies a coordinate transformation to ensure latitude is formated as 0-360 degrees (not -180-180 degrees) using the StandardLongitude class.

6. Reverses the data by ‘level’ coordinate using the ReIndex class.

data_preparation = pyearthtools.pipeline.Pipeline(
    (
        WB2ERA5(resolution="64x32", variables=["u", "v", "t2m"], level=[500, 850]),
        WB2ERA5(resolution="64x32", variables=["geopotential", "vorticity"], level=850),
    ),
    ops.xarray.Merge(),
    ops.xarray.Sort(
        [
            "u_component_of_wind",
            "v_component_of_wind",
            "2m_temperature",
            "geopotential",
            "vorticity",
        ]
    ),
    ops.Transforms(
        apply=coords.StandardLongitude(type="0-360") + coords.ReIndex(level="reversed")
    ),
    # These methods will be explained when we create a pipeline for machine learning.
    # ops.xarray.reshape.CoordinateFlatten('level'),
    # ops.xarray.conversion.ToNumpy(),
    # ops.numpy.reshape.Squeeze(1),
)

# Inspect the data_preparation pipeline which also visualises the pipeline as a graph.
data_preparation

Pipeline
    Description                    `pyearthtools.pipeline` Data Pipeline


    Initialisation
             exceptions_to_ignore           None
             iterator                       None
             sampler                        None
    Steps
             weatherbench.WB2ERA5           {'WB2ERA5': {'chunks': "'auto'", 'download_dir': 'None', 'level': '[500, 850]', 'url': "'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'", 'variables': "['u', 'v', 't2m']"}}
             weatherbench.WB2ERA5[1]        {'WB2ERA5': {'chunks': "'auto'", 'download_dir': 'None', 'level': '850', 'url': "'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'", 'variables': "['geopotential', 'vorticity']"}}
             join.Merge                     {'Merge': {'merge_kwargs': 'None'}}
             sort.Sort                      {'Sort': {'order': "['u_component_of_wind', 'v_component_of_wind', '2m_temperature', 'geopotential', 'vorticity']", 'strict': 'False'}}
             transforms.Transforms          {'Transforms': {'apply': {'TransformCollection': {'StandardLongitude': {'longitude_name': "'longitude'", 'type': "'0-360'"}, 'ReIndex': {'coordinates': 'None', 'level': "'reversed'"}}}, 'transforms': 'None', 'undo': 'None'}}

Pipeline

Description:

`pyearthtools.pipeline` Data Pipeline

• Initialisation:
  • exceptions_to_ignore
    None
    None
  • iterator
    None
    None
  • sampler
    None
    None
• Steps:
  • weatherbench.WB2ERA5
    {'WB2ERA5': {'chunks': 'auto', 'download_dir': None, 'level': [500, 850], 'url': 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr', 'variables': ['u', 'v', 't2m']}}
    WB2ERA5 : {'chunks': 'auto', 'download_dir': None, 'level': [500, 850], 'url': 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr', 'variables': ['u', 'v', 't2m']}
  • weatherbench.WB2ERA5[1]
    {'WB2ERA5': {'chunks': 'auto', 'download_dir': None, 'level': 850, 'url': 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr', 'variables': ['geopotential', 'vorticity']}}
    WB2ERA5 : {'chunks': 'auto', 'download_dir': None, 'level': 850, 'url': 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr', 'variables': ['geopotential', 'vorticity']}
  • join.Merge
    {'Merge': {'merge_kwargs': None}}
    Merge : {'merge_kwargs': None}
  • sort.Sort
    {'Sort': {'order': ['u_component_of_wind', 'v_component_of_wind', '2m_temperature', 'geopotential', 'vorticity'], 'strict': False}}
    Sort : {'order': ['u_component_of_wind', 'v_component_of_wind', '2m_temperature', 'geopotential', 'vorticity'], 'strict': False}
  • transforms.Transforms
    {'Transforms': {'apply': {'TransformCollection': {'StandardLongitude': {'longitude_name': 'longitude', 'type': '0-360'}, 'ReIndex': {'coordinates': None, 'level': 'reversed'}}}, 'transforms': None, 'undo': None}}
    Transforms : {'apply': {'TransformCollection': {'StandardLongitude': {'longitude_name': 'longitude', 'type': '0-360'}, 'ReIndex': {'coordinates': None, 'level': 'reversed'}}}, 'transforms': None, 'undo': None}

Graph

# Use the pipeline to create a sample for a specific date.
sample = data_preparation["20120102T00"]
sample

<xarray.Dataset> Size: 58kB
Dimensions:              (latitude: 32, level: 2, longitude: 64, time: 1)
Coordinates:
  * latitude             (latitude) float64 256B -87.19 -81.56 ... 81.56 87.19
  * level                (level) int64 16B 850 500
  * longitude            (longitude) float64 512B 0.0 5.625 ... 348.8 354.4
  * time                 (time) datetime64[ns] 8B 2012-01-02
Data variables:
    u_component_of_wind  (time, level, longitude, latitude) float32 16kB dask.array<chunksize=(1, 2, 64, 32), meta=np.ndarray>
    v_component_of_wind  (time, level, longitude, latitude) float32 16kB dask.array<chunksize=(1, 2, 64, 32), meta=np.ndarray>
    2m_temperature       (time, longitude, latitude) float32 8kB dask.array<chunksize=(1, 64, 32), meta=np.ndarray>
    geopotential         (time, longitude, latitude) float32 8kB dask.array<chunksize=(1, 64, 32), meta=np.ndarray>
    vorticity            (time, longitude, latitude) float32 8kB dask.array<chunksize=(1, 64, 32), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • latitude: 32
  • level: 2
  • longitude: 64
  • time: 1
• Coordinates: (4)
  • latitude
    (latitude)
    float64
    -87.19 -81.56 ... 81.56 87.19

    array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,
           -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,
            -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,
            30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,
            70.3125,  75.9375,  81.5625,  87.1875])

  • level
    (level)
    int64
    850 500

    array([850, 500])

  • longitude
    (longitude)
    float64
    0.0 5.625 11.25 ... 348.8 354.4

    array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,
            45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,
            90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,
           135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,
           180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,
           225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,
           270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,
           315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])

  • time
    (time)
    datetime64[ns]
    2012-01-02

    array(['2012-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (5)
  • u_component_of_wind
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(1, 2, 64, 32), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 16.00 kiB 16.00 kiB Shape (1, 2, 64, 32) (1, 2, 64, 32) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, level, longitude, latitude)
    float32
    dask.array<chunksize=(1, 2, 64, 32), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 16.00 kiB 16.00 kiB Shape (1, 2, 64, 32) (1, 2, 64, 32) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(1, 64, 32), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    units :

    K

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (1, 64, 32) (1, 64, 32) Dask graph 1 chunks in 6 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(1, 64, 32), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (1, 64, 32) (1, 64, 32) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

  • vorticity
    (time, longitude, latitude)
    float32
    dask.array<chunksize=(1, 64, 32), meta=np.ndarray>

    Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (1, 64, 32) (1, 64, 32) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,
            -70.31249999999999,  -64.68750000000001,            -59.0625,
                      -53.4375,            -47.8125,            -42.1875,
                      -36.5625, -30.937499999999996, -25.312500000000004,
           -19.687499999999996, -14.062499999999991,  -8.437499999999996,
            -2.812500000000003,   2.812500000000003,   8.437500000000009,
            14.062500000000004,  19.687499999999996,  25.312500000000004,
             30.93750000000001,  36.562499999999986,             42.1875,
                       47.8125,             53.4375,  59.062500000000014,
             64.68750000000001,             70.3125,             75.9375,
             81.56249999999997,   87.18750000000003],
          dtype='float64', name='latitude'))

  • level
    PandasIndex

    PandasIndex(Index([850, 500], dtype='int64', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([               0.0,              5.625,              11.25,
                       16.875,               22.5,             28.125,
                        33.75,             39.375,               45.0,
                       50.625,              56.25,  61.87499999999999,
                         67.5,             73.125,              78.75,
                       84.375,               90.0,             95.625,
                       101.25,            106.875,              112.5,
                      118.125, 123.74999999999999,            129.375,
                        135.0,            140.625,             146.25,
                      151.875,              157.5,            163.125,
                       168.75,            174.375,              180.0,
                      185.625,             191.25,            196.875,
                        202.5,            208.125,             213.75,
                      219.375,              225.0, 230.62499999999997,
                       236.25,            241.875, 247.49999999999997,
                      253.125,             258.75,            264.375,
                        270.0,            275.625,             281.25,
                      286.875,              292.5,            298.125,
                       303.75,            309.375,              315.0,
                      320.625,             326.25,            331.875,
                        337.5,            343.125,             348.75,
                      354.375],
          dtype='float64', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2012-01-02'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (0)

# Inspect vorticity DataArray variable.
sample.vorticity

<xarray.DataArray 'vorticity' (time: 1, longitude: 64, latitude: 32)> Size: 8kB
dask.array<getitem, shape=(1, 64, 32), dtype=float32, chunksize=(1, 64, 32), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float64 256B -87.19 -81.56 -75.94 ... 81.56 87.19
  * longitude  (longitude) float64 512B 0.0 5.625 11.25 ... 343.1 348.8 354.4
  * time       (time) datetime64[ns] 8B 2012-01-02

xarray.DataArray

'vorticity'

• time: 1
• longitude: 64
• latitude: 32

• dask.array<chunksize=(1, 64, 32), meta=np.ndarray>

  Array Chunk Bytes 8.00 kiB 8.00 kiB Shape (1, 64, 32) (1, 64, 32) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • latitude
    (latitude)
    float64
    -87.19 -81.56 ... 81.56 87.19

    array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,
           -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,
            -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,
            30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,
            70.3125,  75.9375,  81.5625,  87.1875])

  • longitude
    (longitude)
    float64
    0.0 5.625 11.25 ... 348.8 354.4

    array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,
            45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,
            90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,
           135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,
           180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,
           225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,
           270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,
           315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])

  • time
    (time)
    datetime64[ns]
    2012-01-02

    array(['2012-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,
            -70.31249999999999,  -64.68750000000001,            -59.0625,
                      -53.4375,            -47.8125,            -42.1875,
                      -36.5625, -30.937499999999996, -25.312500000000004,
           -19.687499999999996, -14.062499999999991,  -8.437499999999996,
            -2.812500000000003,   2.812500000000003,   8.437500000000009,
            14.062500000000004,  19.687499999999996,  25.312500000000004,
             30.93750000000001,  36.562499999999986,             42.1875,
                       47.8125,             53.4375,  59.062500000000014,
             64.68750000000001,             70.3125,             75.9375,
             81.56249999999997,   87.18750000000003],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([               0.0,              5.625,              11.25,
                       16.875,               22.5,             28.125,
                        33.75,             39.375,               45.0,
                       50.625,              56.25,  61.87499999999999,
                         67.5,             73.125,              78.75,
                       84.375,               90.0,             95.625,
                       101.25,            106.875,              112.5,
                      118.125, 123.74999999999999,            129.375,
                        135.0,            140.625,             146.25,
                      151.875,              157.5,            163.125,
                       168.75,            174.375,              180.0,
                      185.625,             191.25,            196.875,
                        202.5,            208.125,             213.75,
                      219.375,              225.0, 230.62499999999997,
                       236.25,            241.875, 247.49999999999997,
                      253.125,             258.75,            264.375,
                        270.0,            275.625,             281.25,
                      286.875,              292.5,            298.125,
                       303.75,            309.375,              315.0,
                      320.625,             326.25,            331.875,
                        337.5,            343.125,             348.75,
                      354.375],
          dtype='float64', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2012-01-02'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (0)