How to read data from STAC

This notebook shows how to read the data in from a STAC asset using xarray and a little hidden helper library called xpystac.

tl;dr

For any PySTAC object that can be represented as an ndimensional dataset you can read the data using the following command:

xr.open_dataset(object)

Dependencies

There are lots of optional dependencies depending on where and how the data you are interested in are stored. Here are some of the libraries that you will probably need:

• dask - to delay data loading until access

• fsspec - to access data from remote storage

• pystac - STAC object structures

• xarray, rioxarray - data structures

• xpystac, stackstac - helper for loading pystac into xarray objects

!pip install adlfs dask 'fsspec[http]' planetary_computer --quiet
!pip install stackstac xarray xpystac zarr --quiet

Despite all these install instructions, the import block is very straightforward

import xarray as xr

import pystac

Examples

Here are a few examples of the different types of objects that you can open in xarray.

COGs

Read all the data from the COGs referenced by the assets on an item.

landsat_item = pystac.Item.from_file(
    "https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2/items/LC09_L2SP_088084_20230408_02_T2"
)
xr.open_dataset(landsat_item)

<xarray.Dataset>
Dimensions:                      (time: 1, y: 7802, x: 7762, band: 19)
Coordinates: (12/32)
  * time                         (time) datetime64[ns] 2023-04-08T23:37:51.63...
    id                           (time) <U31 ...
  * x                            (x) float64 3.774e+05 3.774e+05 ... 6.102e+05
  * y                            (y) float64 -3.713e+06 ... -3.947e+06
    proj:shape                   object ...
    sci:doi                      <U16 ...
    ...                           ...
    raster:bands                 (band) object ...
    classification:bitfields     (band) object ...
    common_name                  (band) object ...
    center_wavelength            (band) object ...
    full_width_half_max          (band) object ...
    epsg                         int64 ...
Dimensions without coordinates: band
Data variables: (12/19)
    qa                           (time, y, x) float64 ...
    red                          (time, y, x) float64 ...
    blue                         (time, y, x) float64 ...
    drad                         (time, y, x) float64 ...
    emis                         (time, y, x) float64 ...
    emsd                         (time, y, x) float64 ...
    ...                           ...
    swir16                       (time, y, x) float64 ...
    swir22                       (time, y, x) float64 ...
    coastal                      (time, y, x) float64 ...
    qa_pixel                     (time, y, x) float64 ...
    qa_radsat                    (time, y, x) float64 ...
    qa_aerosol                   (time, y, x) float64 ...
Attributes:
    spec:        RasterSpec(epsg=32656, bounds=(377370.0, -3947130.0, 610230....
    crs:         epsg:32656
    transform:   | 30.00, 0.00, 377370.00|\n| 0.00,-30.00,-3713070.00|\n| 0.0...
    resolution:  30.0

xarray.Dataset

• Dimensions:
  • time: 1
  • y: 7802
  • x: 7762
  • band: 19
• Coordinates: (32)
  • time
    (time)
    datetime64[ns]
    2023-04-08T23:37:51.630731

    array(['2023-04-08T23:37:51.630731000'], dtype='datetime64[ns]')

  • id
    (time)
    <U31
    ...

    [1 values with dtype=<U31]

  • x
    (x)
    float64
    3.774e+05 3.774e+05 ... 6.102e+05

    array([377370., 377400., 377430., ..., 610140., 610170., 610200.])

  • y
    (y)
    float64
    -3.713e+06 ... -3.947e+06

    array([-3713070., -3713100., -3713130., ..., -3947040., -3947070., -3947100.])

  • proj:shape
    ()
    object
    ...

    [1 values with dtype=object]

  • sci:doi
    ()
    <U16
    ...

    [1 values with dtype=<U16]

  • landsat:collection_number
    ()
    <U2
    ...

    [1 values with dtype=<U2]

  • landsat:wrs_path
    ()
    <U3
    ...

    [1 values with dtype=<U3]

  • landsat:wrs_type
    ()
    <U1
    ...

    [1 values with dtype=<U1]

  • landsat:wrs_row
    ()
    <U3
    ...

    [1 values with dtype=<U3]

  • landsat:cloud_cover_land
    ()
    float64
    ...

    [1 values with dtype=float64]

  • landsat:scene_id
    ()
    <U21
    ...

    [1 values with dtype=<U21]

  • proj:epsg
    ()
    int64
    ...

    [1 values with dtype=int64]

  • view:sun_azimuth
    ()
    float64
    ...

    [1 values with dtype=float64]

  • created
    ()
    <U27
    ...

    [1 values with dtype=<U27]

  • gsd
    (band)
    object
    ...

    [19 values with dtype=object]

  • description
    (band)
    <U101
    ...

    [19 values with dtype=<U101]

  • landsat:correction
    ()
    <U4
    ...

    [1 values with dtype=<U4]

  • instruments
    ()
    object
    ...

    [1 values with dtype=object]

  • eo:cloud_cover
    ()
    float64
    ...

    [1 values with dtype=float64]

  • view:off_nadir
    ()
    int64
    ...

    [1 values with dtype=int64]

  • landsat:collection_category
    ()
    <U2
    ...

    [1 values with dtype=<U2]

  • platform
    ()
    <U9
    ...

    [1 values with dtype=<U9]

  • view:sun_elevation
    ()
    float64
    ...

    [1 values with dtype=float64]

  • proj:transform
    ()
    object
    ...

    [1 values with dtype=object]

  • title
    (band)
    <U68
    ...

    [19 values with dtype=<U68]

  • raster:bands
    (band)
    object
    ...

    [19 values with dtype=object]

  • classification:bitfields
    (band)
    object
    ...

    [19 values with dtype=object]

  • common_name
    (band)
    object
    ...

    [19 values with dtype=object]

  • center_wavelength
    (band)
    object
    ...

    [19 values with dtype=object]

  • full_width_half_max
    (band)
    object
    ...

    [19 values with dtype=object]

  • epsg
    ()
    int64
    ...

    [1 values with dtype=int64]

• Data variables: (19)
  • qa
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • red
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • blue
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • drad
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • emis
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • emsd
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • trad
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • urad
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • atran
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • cdist
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • green
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • nir08
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • lwir11
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • swir16
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • swir22
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • coastal
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • qa_pixel
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • qa_radsat
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

  • qa_aerosol
    (time, y, x)
    float64
    ...

    [60559124 values with dtype=float64]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-04-08 23:37:51.630731'], dtype='datetime64[ns]', name='time', freq=None))

  • x
    PandasIndex

    PandasIndex(Float64Index([377370.0, 377400.0, 377430.0, 377460.0, 377490.0, 377520.0,
                  377550.0, 377580.0, 377610.0, 377640.0,
                  ...
                  609930.0, 609960.0, 609990.0, 610020.0, 610050.0, 610080.0,
                  610110.0, 610140.0, 610170.0, 610200.0],
                 dtype='float64', name='x', length=7762))

  • y
    PandasIndex

    PandasIndex(Float64Index([-3713070.0, -3713100.0, -3713130.0, -3713160.0, -3713190.0,
                  -3713220.0, -3713250.0, -3713280.0, -3713310.0, -3713340.0,
                  ...
                  -3946830.0, -3946860.0, -3946890.0, -3946920.0, -3946950.0,
                  -3946980.0, -3947010.0, -3947040.0, -3947070.0, -3947100.0],
                 dtype='float64', name='y', length=7802))

• Attributes: (4)

  spec :

  RasterSpec(epsg=32656, bounds=(377370.0, -3947130.0, 610230.0, -3713070.0), resolutions_xy=(30.0, 30.0))

  crs :

  epsg:32656

  transform :

  | 30.00, 0.00, 377370.00| | 0.00,-30.00,-3713070.00| | 0.00, 0.00, 1.00|

  resolution :

  30.0

Zarr

Read from an asset that references data stored in zarr

daymet_collection = pystac.Collection.from_file(
    "https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-daily-hi"
)
daymet_asset = daymet_collection.assets["zarr-abfs"]

xr.open_dataset(daymet_asset)

<xarray.Dataset>
Dimensions:                  (time: 14965, y: 584, x: 284, nv: 2)
Coordinates:
    lat                      (y, x) float32 ...
    lon                      (y, x) float32 ...
  * time                     (time) datetime64[ns] 1980-01-01T12:00:00 ... 20...
  * x                        (x) float32 -5.802e+06 -5.801e+06 ... -5.519e+06
  * y                        (y) float32 -3.9e+04 -4e+04 ... -6.21e+05 -6.22e+05
Dimensions without coordinates: nv
Data variables:
    dayl                     (time, y, x) float32 ...
    lambert_conformal_conic  int16 ...
    prcp                     (time, y, x) float32 ...
    srad                     (time, y, x) float32 ...
    swe                      (time, y, x) float32 ...
    time_bnds                (time, nv) datetime64[ns] ...
    tmax                     (time, y, x) float32 ...
    tmin                     (time, y, x) float32 ...
    vp                       (time, y, x) float32 ...
    yearday                  (time) int16 ...
Attributes:
    Conventions:       CF-1.6
    Version_data:      Daymet Data Version 4.0
    Version_software:  Daymet Software Version 4.0
    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...
    references:        Please see http://daymet.ornl.gov/ for current informa...
    source:            Daymet Software Version 4.0
    start_year:        1980

xarray.Dataset

• Dimensions:
  • time: 14965
  • y: 584
  • x: 284
  • nv: 2
• Coordinates: (5)
  • lat
    (y, x)
    float32
    ...

    long_name :

    latitude coordinate

    standard_name :

    latitude

    units :

    degrees_north

    [165856 values with dtype=float32]

  • lon
    (y, x)
    float32
    ...

    long_name :

    longitude coordinate

    standard_name :

    longitude

    units :

    degrees_east

    [165856 values with dtype=float32]

  • time
    (time)
    datetime64[ns]
    1980-01-01T12:00:00 ... 2020-12-...

    bounds :

    time_bnds

    long_name :

    24-hour day based on local time

    standard_name :

    time

    array(['1980-01-01T12:00:00.000000000', '1980-01-02T12:00:00.000000000',
           '1980-01-03T12:00:00.000000000', ..., '2020-12-28T12:00:00.000000000',
           '2020-12-29T12:00:00.000000000', '2020-12-30T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • x
    (x)
    float32
    -5.802e+06 ... -5.519e+06

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    m

    array([-5802250., -5801250., -5800250., ..., -5521250., -5520250., -5519250.],
          dtype=float32)

  • y
    (y)
    float32
    -3.9e+04 -4e+04 ... -6.22e+05

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    m

    array([ -39000.,  -40000.,  -41000., ..., -620000., -621000., -622000.],
          dtype=float32)

• Data variables: (10)
  • dayl
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean

    grid_mapping :

    lambert_conformal_conic

    long_name :

    daylength

    units :

    s

    [2482035040 values with dtype=float32]

  • lambert_conformal_conic
    ()
    int16
    ...

    false_easting :

    0.0

    false_northing :

    0.0

    grid_mapping_name :

    lambert_conformal_conic

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    42.5

    longitude_of_central_meridian :

    -100.0

    semi_major_axis :

    6378137.0

    standard_parallel :

    [25.0, 60.0]

    [1 values with dtype=int16]

  • prcp
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean time: sum

    grid_mapping :

    lambert_conformal_conic

    long_name :

    daily total precipitation

    units :

    mm/day

    [2482035040 values with dtype=float32]

  • srad
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean time: mean

    grid_mapping :

    lambert_conformal_conic

    long_name :

    daylight average incident shortwave radiation

    units :

    W/m2

    [2482035040 values with dtype=float32]

  • swe
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean time: mean

    grid_mapping :

    lambert_conformal_conic

    long_name :

    snow water equivalent

    units :

    kg/m2

    [2482035040 values with dtype=float32]

  • time_bnds
    (time, nv)
    datetime64[ns]
    ...

    [29930 values with dtype=datetime64[ns]]

  • tmax
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean time: maximum

    grid_mapping :

    lambert_conformal_conic

    long_name :

    daily maximum temperature

    units :

    degrees C

    [2482035040 values with dtype=float32]

  • tmin
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean time: minimum

    grid_mapping :

    lambert_conformal_conic

    long_name :

    daily minimum temperature

    units :

    degrees C

    [2482035040 values with dtype=float32]

  • vp
    (time, y, x)
    float32
    ...

    cell_methods :

    area: mean time: mean

    grid_mapping :

    lambert_conformal_conic

    long_name :

    daily average vapor pressure

    units :

    Pa

    [2482035040 values with dtype=float32]

  • yearday
    (time)
    int16
    ...

    long_name :

    day of year (DOY) starting with day 1 on Januaray 1st

    [14965 values with dtype=int16]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1980-01-01 12:00:00', '1980-01-02 12:00:00',
                   '1980-01-03 12:00:00', '1980-01-04 12:00:00',
                   '1980-01-05 12:00:00', '1980-01-06 12:00:00',
                   '1980-01-07 12:00:00', '1980-01-08 12:00:00',
                   '1980-01-09 12:00:00', '1980-01-10 12:00:00',
                   ...
                   '2020-12-21 12:00:00', '2020-12-22 12:00:00',
                   '2020-12-23 12:00:00', '2020-12-24 12:00:00',
                   '2020-12-25 12:00:00', '2020-12-26 12:00:00',
                   '2020-12-27 12:00:00', '2020-12-28 12:00:00',
                   '2020-12-29 12:00:00', '2020-12-30 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=14965, freq=None))

  • x
    PandasIndex

    PandasIndex(Float64Index([-5802250.0, -5801250.0, -5800250.0, -5799250.0, -5798250.0,
                  -5797250.0, -5796250.0, -5795250.0, -5794250.0, -5793250.0,
                  ...
                  -5528250.0, -5527250.0, -5526250.0, -5525250.0, -5524250.0,
                  -5523250.0, -5522250.0, -5521250.0, -5520250.0, -5519250.0],
                 dtype='float64', name='x', length=284))

  • y
    PandasIndex

    PandasIndex(Float64Index([ -39000.0,  -40000.0,  -41000.0,  -42000.0,  -43000.0,  -44000.0,
                   -45000.0,  -46000.0,  -47000.0,  -48000.0,
                  ...
                  -613000.0, -614000.0, -615000.0, -616000.0, -617000.0, -618000.0,
                  -619000.0, -620000.0, -621000.0, -622000.0],
                 dtype='float64', name='y', length=584))

• Attributes: (7)

  Conventions :

  CF-1.6

  Version_data :

  Daymet Data Version 4.0

  Version_software :

  Daymet Software Version 4.0

  citation :

  Please see http://daymet.ornl.gov/ for current Daymet data citation information

  references :

  Please see http://daymet.ornl.gov/ for current information on Daymet references

  source :

  Daymet Software Version 4.0

  start_year :

  1980

Reference file

If the collection has a reference file we can use that

cmip6_collection = pystac.Collection.from_file(
    "https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasa-nex-gddp-cmip6"
)
cmip6_asset = cmip6_collection.assets["ACCESS-CM2.historical"]

xr.open_dataset(cmip6_asset)

<xarray.Dataset>
Dimensions:  (time: 23741, lat: 600, lon: 1440)
Coordinates:
  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88
  * lon      (lon) float64 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9
  * time     (time) datetime64[us] 1950-01-01T12:00:00 ... 2014-12-31T12:00:00
Data variables:
    hurs     (time, lat, lon) float32 ...
    huss     (time, lat, lon) float32 ...
    pr       (time, lat, lon) float32 ...
    rlds     (time, lat, lon) float32 ...
    rsds     (time, lat, lon) float32 ...
    sfcWind  (time, lat, lon) float32 ...
    tas      (time, lat, lon) float32 ...
    tasmax   (time, lat, lon) float32 ...
    tasmin   (time, lat, lon) float32 ...
Attributes: (12/22)
    Conventions:           CF-1.7
    activity:              NEX-GDDP-CMIP6
    cmip6_institution_id:  CSIRO-ARCCSS
    cmip6_license:         CC-BY-SA 4.0
    cmip6_source_id:       ACCESS-CM2
    contact:               Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget...
    ...                    ...
    scenario:              historical
    source:                BCSD
    title:                 ACCESS-CM2, r1i1p1f1, historical, global downscale...
    tracking_id:           16d27564-470f-41ea-8077-f4cc3efa5bfe
    variant_label:         r1i1p1f1
    version:               1.0

xarray.Dataset

• Dimensions:
  • time: 23741
  • lat: 600
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float64
    -59.88 -59.62 ... 89.62 89.88

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-59.875, -59.625, -59.375, ...,  89.375,  89.625,  89.875])

  • lon
    (lon)
    float64
    0.125 0.375 0.625 ... 359.6 359.9

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,
           3.59875e+02])

  • time
    (time)
    datetime64[us]
    1950-01-01T12:00:00 ... 2014-12-...

    axis :

    T

    long_name :

    time

    standard_name :

    time

    _FillValue :

    1970-01-01T00:00:00.000000

    array(['1950-01-01T12:00:00.000000', '1950-01-02T12:00:00.000000',
           '1950-01-03T12:00:00.000000', ..., '2014-12-29T12:00:00.000000',
           '2014-12-30T12:00:00.000000', '2014-12-31T12:00:00.000000'],
          dtype='datetime64[us]')

• Data variables: (9)
  • hurs
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    The relative humidity with respect to liquid water for T> 0 C, and with respect to ice for T<0 C.

    long_name :

    Near-Surface Relative Humidity

    standard_name :

    relative_humidity

    units :

    %

    [20512224000 values with dtype=float32]

  • huss
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    Near-surface (usually, 2 meter) specific humidity.

    long_name :

    Near-Surface Specific Humidity

    standard_name :

    specific_humidity

    units :

    1

    [20512224000 values with dtype=float32]

  • pr
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    includes both liquid and solid phases

    long_name :

    Precipitation

    standard_name :

    precipitation_flux

    units :

    kg m-2 s-1

    [20512224000 values with dtype=float32]

  • rlds
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    The surface called 'surface' means the lower boundary of the atmosphere. 'longwave' means longwave radiation. Downwelling radiation is radiation from above. It does not mean 'net downward'. When thought of as being incident on a surface, a radiative flux is sometimes called 'irradiance'. In addition, it is identical with the quantity measured by a cosine-collector light-meter and sometimes called 'vector irradiance'. In accordance with common usage in geophysical disciplines, 'flux' implies per unit area, called 'flux density' in physics.

    long_name :

    Surface Downwelling Longwave Radiation

    standard_name :

    surface_downwelling_longwave_flux_in_air

    units :

    W m-2

    [20512224000 values with dtype=float32]

  • rsds
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    Surface solar irradiance for UV calculations.

    long_name :

    Surface Downwelling Shortwave Radiation

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    W m-2

    [20512224000 values with dtype=float32]

  • sfcWind
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    near-surface (usually, 10 meters) wind speed.

    long_name :

    Daily-Mean Near-Surface Wind Speed

    standard_name :

    wind_speed

    units :

    m s-1

    [20512224000 values with dtype=float32]

  • tas
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: mean time: maximum

    comment :

    near-surface (usually, 2 meter) air temperature; derived from downscaled tasmax & tasmin

    long_name :

    Daily Near-Surface Air Temperature

    standard_name :

    air_temperature

    units :

    K

    [20512224000 values with dtype=float32]

  • tasmax
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: mean time: maximum

    comment :

    maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')

    long_name :

    Daily Maximum Near-Surface Air Temperature

    standard_name :

    air_temperature

    units :

    K

    [20512224000 values with dtype=float32]

  • tasmin
    (time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: mean time: minimum

    comment :

    minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')

    long_name :

    Daily Minimum Near-Surface Air Temperature

    standard_name :

    air_temperature

    units :

    K

    [20512224000 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([-59.875, -59.625, -59.375, -59.125, -58.875, -58.625, -58.375,
                  -58.125, -57.875, -57.625,
                  ...
                   87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,
                   89.375,  89.625,  89.875],
                 dtype='float64', name='lat', length=600))

  • lon
    PandasIndex

    PandasIndex(Float64Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,
                    1.875,   2.125,   2.375,
                  ...
                  357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125,
                  359.375, 359.625, 359.875],
                 dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1950-01-01 12:00:00', '1950-01-02 12:00:00',
                   '1950-01-03 12:00:00', '1950-01-04 12:00:00',
                   '1950-01-05 12:00:00', '1950-01-06 12:00:00',
                   '1950-01-07 12:00:00', '1950-01-08 12:00:00',
                   '1950-01-09 12:00:00', '1950-01-10 12:00:00',
                   ...
                   '2014-12-22 12:00:00', '2014-12-23 12:00:00',
                   '2014-12-24 12:00:00', '2014-12-25 12:00:00',
                   '2014-12-26 12:00:00', '2014-12-27 12:00:00',
                   '2014-12-28 12:00:00', '2014-12-29 12:00:00',
                   '2014-12-30 12:00:00', '2014-12-31 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=23741, freq=None))

• Attributes: (22)

  Conventions :

  CF-1.7

  activity :

  NEX-GDDP-CMIP6

  cmip6_institution_id :

  CSIRO-ARCCSS

  cmip6_license :

  CC-BY-SA 4.0

  cmip6_source_id :

  ACCESS-CM2

  contact :

  Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget Thrasher: bridget@climateanalyticsgroup.org

  creation_date :

  2021-10-04T13:59:21.654137+00:00

  disclaimer :

  This data is considered provisional and subject to change. This data is provided as is without any warranty of any kind, either express or implied, arising by law or otherwise, including but not limited to warranties of completeness, non-infringement, accuracy, merchantability, or fitness for a particular purpose. The user assumes all risk associated with the use of, or inability to use, this data.

  external_variables :

  areacella

  frequency :

  day

  history :

  2021-10-04T13:59:21.654137+00:00: install global attributes

  institution :

  NASA Earth Exchange, NASA Ames Research Center, Moffett Field, CA 94035

  product :

  output

  realm :

  atmos

  references :

  BCSD method: Thrasher et al., 2012, Hydrol. Earth Syst. Sci.,16, 3309-3314. Ref period obs: latest version of the Princeton Global Meteorological Forcings (http://hydrology.princeton.edu/data.php), based on Sheffield et al., 2006, J. Climate, 19 (13), 3088-3111.

  resolution_id :

  0.25 degree

  scenario :

  historical

  source :

  BCSD

  title :

  ACCESS-CM2, r1i1p1f1, historical, global downscaled CMIP6 climate projection data

  tracking_id :

  16d27564-470f-41ea-8077-f4cc3efa5bfe

  variant_label :

  r1i1p1f1

  version :

  1.0