Helios

Solar analysis and forecasts powered by EarthNet

Hourly analysis

import xarray as xr

ds = xr.open_zarr("https://data.myzeus.ai/solar/analysis-hourly/latest.zarr")
ds

<xarray.Dataset> Size: 799GB
Dimensions:  (time: 8760, lat: 2745, lon: 2770)
Coordinates:
  * lat      (lat) float64 22kB -78.57 -78.52 -78.46 -78.4 ... 78.46 78.52 78.57
  * lon      (lon) float64 22kB -154.3 -154.2 -154.2 -154.1 ... 4.18 4.24 4.29
  * time     (time) datetime64[ns] 70kB 2025-01-01 ... 2025-12-31T23:00:00
Data variables:
    dhi      (time, lat, lon) float32 266GB dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    dni      (time, lat, lon) float32 266GB dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    ghi      (time, lat, lon) float32 266GB dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 8760
  • lat: 2745
  • lon: 2770
• Coordinates: (3)
  • lat
    (lat)
    float64
    -78.57 -78.52 ... 78.52 78.57

    axis :

    Y

    standard_name :

    latitude

    units :

    degrees_north

    array([-78.57, -78.52, -78.46, ...,  78.46,  78.52,  78.57])

  • lon
    (lon)
    float64
    -154.3 -154.2 -154.2 ... 4.24 4.29

    axis :

    X

    standard_name :

    longitude

    units :

    degrees_east

    array([-154.26, -154.2 , -154.15, ...,    4.18,    4.24,    4.29])

  • time
    (time)
    datetime64[ns]
    2025-01-01 ... 2025-12-31T23:00:00

    axis :

    T

    standard_name :

    time

    array(['2025-01-01T00:00:00.000000000', '2025-01-01T01:00:00.000000000',
           '2025-01-01T02:00:00.000000000', ..., '2025-12-31T21:00:00.000000000',
           '2025-12-31T22:00:00.000000000', '2025-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (3)
  • dhi
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>

    units :

    W m-2

    Array Chunk Bytes 248.13 GiB 3.81 MiB Shape (8760, 2745, 2770) (1, 1000, 1000) Dask graph 78840 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dni
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>

    units :

    W m-2

    Array Chunk Bytes 248.13 GiB 3.81 MiB Shape (8760, 2745, 2770) (1, 1000, 1000) Dask graph 78840 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ghi
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>

    units :

    W m-2

    Array Chunk Bytes 248.13 GiB 3.81 MiB Shape (8760, 2745, 2770) (1, 1000, 1000) Dask graph 78840 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-78.57, -78.52, -78.46,  -78.4, -78.35, -78.29, -78.23, -78.17, -78.12,
           -78.06,
           ...
            78.06,  78.12,  78.17,  78.23,  78.29,  78.35,   78.4,  78.46,  78.52,
            78.57],
          dtype='float64', name='lat', length=2745))

  • lon
    PandasIndex

    PandasIndex(Index([-154.26,  -154.2, -154.15, -154.09, -154.03, -153.97, -153.92, -153.86,
            -153.8, -153.75,
           ...
              3.78,    3.84,    3.89,    3.95,    4.01,    4.07,    4.12,    4.18,
              4.24,    4.29],
          dtype='float64', name='lon', length=2770))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2025-01-01 00:00:00', '2025-01-01 01:00:00',
                   '2025-01-01 02:00:00', '2025-01-01 03:00:00',
                   '2025-01-01 04:00:00', '2025-01-01 05:00:00',
                   '2025-01-01 06:00:00', '2025-01-01 07:00:00',
                   '2025-01-01 08:00:00', '2025-01-01 09:00:00',
                   ...
                   '2025-12-31 14:00:00', '2025-12-31 15:00:00',
                   '2025-12-31 16:00:00', '2025-12-31 17:00:00',
                   '2025-12-31 18:00:00', '2025-12-31 19:00:00',
                   '2025-12-31 20:00:00', '2025-12-31 21:00:00',
                   '2025-12-31 22:00:00', '2025-12-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=8760, freq=None))

• Attributes: (0)

ds.sel(time="2025-03-07T14").dhi.plot(cmap="magma")

<matplotlib.collections.QuadMesh at 0x15e2e5cd0>