Reading NetCDF data from OPEnDAP services

In this example we read NetCDF data from an OPEnDAP service using the opendap source.

import earthkit.data as ekd

url="https://psl.noaa.gov/thredds/dodsC/Datasets/noaa.oisst.v2/sst.mnmean.nc"
ds = ekd.from_source("opendap", url)

What we get is a NetCDF fieldlist.

ds.ls()

	variable	level	valid_datetime	units
0	sst	None	1981-12-01T00:00:00	degC
1	sst	None	1982-01-01T00:00:00	degC
2	sst	None	1982-02-01T00:00:00	degC
3	sst	None	1982-03-01T00:00:00	degC
4	sst	None	1982-04-01T00:00:00	degC
...	...	...	...	...
489	sst	None	2022-09-01T00:00:00	degC
490	sst	None	2022-10-01T00:00:00	degC
491	sst	None	2022-11-01T00:00:00	degC
492	sst	None	2022-12-01T00:00:00	degC
493	sst	None	2023-01-01T00:00:00	degC

494 rows × 4 columns

v = ds[2].to_numpy()
v.shape

(180, 360)

ds.to_xarray()

<xarray.Dataset> Size: 128MB
Dimensions:    (lat: 180, lon: 360, time: 494, nbnds: 2)
Coordinates:
  * lat        (lat) float32 720B 89.5 88.5 87.5 86.5 ... -87.5 -88.5 -89.5
  * lon        (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * time       (time) datetime64[ns] 4kB 1981-12-01 1982-01-01 ... 2023-01-01
Dimensions without coordinates: nbnds
Data variables:
    time_bnds  (time, nbnds) datetime64[ns] 8kB dask.array<chunksize=(494, 2), meta=np.ndarray>
    sst        (time, lat, lon) float32 128MB dask.array<chunksize=(494, 180, 360), meta=np.ndarray>
Attributes:
    title:                           NOAA Optimum Interpolation (OI) SST V2
    Conventions:                     CF-1.0
    history:                         Wed Apr  6 13:47:45 2005: ncks -d time,0...
    comments:                        Data described in  Reynolds, R.W., N.A. ...
    platform:                        Model
    source:                          NCEP Climate Modeling Branch
    institution:                     National Centers for Environmental Predi...
    References:                      https://www.psl.noaa.gov/data/gridded/da...
    dataset_title:                   NOAA Optimum Interpolation (OI) SST V2
    source_url:                      http://www.emc.ncep.noaa.gov/research/cm...
    DODS_EXTRA.Unlimited_Dimension:  time

xarray.Dataset

• Dimensions:
  • lat: 180
  • lon: 360
  • time: 494
  • nbnds: 2
• Coordinates: (3)
  • lat
    (lat)
    float32
    89.5 88.5 87.5 ... -88.5 -89.5

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 89.5 -89.5]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 89.5,  88.5,  87.5,  86.5,  85.5,  84.5,  83.5,  82.5,  81.5,  80.5,
            79.5,  78.5,  77.5,  76.5,  75.5,  74.5,  73.5,  72.5,  71.5,  70.5,
            69.5,  68.5,  67.5,  66.5,  65.5,  64.5,  63.5,  62.5,  61.5,  60.5,
            59.5,  58.5,  57.5,  56.5,  55.5,  54.5,  53.5,  52.5,  51.5,  50.5,
            49.5,  48.5,  47.5,  46.5,  45.5,  44.5,  43.5,  42.5,  41.5,  40.5,
            39.5,  38.5,  37.5,  36.5,  35.5,  34.5,  33.5,  32.5,  31.5,  30.5,
            29.5,  28.5,  27.5,  26.5,  25.5,  24.5,  23.5,  22.5,  21.5,  20.5,
            19.5,  18.5,  17.5,  16.5,  15.5,  14.5,  13.5,  12.5,  11.5,  10.5,
             9.5,   8.5,   7.5,   6.5,   5.5,   4.5,   3.5,   2.5,   1.5,   0.5,
            -0.5,  -1.5,  -2.5,  -3.5,  -4.5,  -5.5,  -6.5,  -7.5,  -8.5,  -9.5,
           -10.5, -11.5, -12.5, -13.5, -14.5, -15.5, -16.5, -17.5, -18.5, -19.5,
           -20.5, -21.5, -22.5, -23.5, -24.5, -25.5, -26.5, -27.5, -28.5, -29.5,
           -30.5, -31.5, -32.5, -33.5, -34.5, -35.5, -36.5, -37.5, -38.5, -39.5,
           -40.5, -41.5, -42.5, -43.5, -44.5, -45.5, -46.5, -47.5, -48.5, -49.5,
           -50.5, -51.5, -52.5, -53.5, -54.5, -55.5, -56.5, -57.5, -58.5, -59.5,
           -60.5, -61.5, -62.5, -63.5, -64.5, -65.5, -66.5, -67.5, -68.5, -69.5,
           -70.5, -71.5, -72.5, -73.5, -74.5, -75.5, -76.5, -77.5, -78.5, -79.5,
           -80.5, -81.5, -82.5, -83.5, -84.5, -85.5, -86.5, -87.5, -88.5, -89.5],
          dtype=float32)

  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0.5 359.5]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1981-12-01 ... 2023-01-01

    long_name :

    Time

    actual_range :

    [66443. 81449.]

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    bounds :

    time_bnds

    array(['1981-12-01T00:00:00.000000000', '1982-01-01T00:00:00.000000000',
           '1982-02-01T00:00:00.000000000', ..., '2022-11-01T00:00:00.000000000',
           '2022-12-01T00:00:00.000000000', '2023-01-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (2)
  • time_bnds
    (time, nbnds)
    datetime64[ns]
    dask.array<chunksize=(494, 2), meta=np.ndarray>

    long_name :

    Time Boundaries

    Array Chunk Bytes 7.72 kiB 7.72 kiB Shape (494, 2) (494, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • sst
    (time, lat, lon)
    float32
    dask.array<chunksize=(494, 180, 360), meta=np.ndarray>

    long_name :

    Monthly Mean of Sea Surface Temperature

    unpacked_valid_range :

    [-5. 40.]

    actual_range :

    [-1.7999996 35.56862 ]

    units :

    degC

    precision :

    2

    var_desc :

    Sea Surface Temperature

    dataset :

    NOAA Optimum Interpolation (OI) SST V2

    level_desc :

    Surface

    statistic :

    Mean

    parent_stat :

    Weekly Mean

    standard_name :

    sea_surface_temperature

    cell_methods :

    time: mean (monthly from weekly values interpolated to daily)

    valid_range :

    [-500 4000]

    Array Chunk Bytes 122.11 MiB 122.11 MiB Shape (494, 180, 360) (494, 180, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.5,  88.5,  87.5,  86.5,  85.5,  84.5,  83.5,  82.5,  81.5,  80.5,
           ...
           -80.5, -81.5, -82.5, -83.5, -84.5, -85.5, -86.5, -87.5, -88.5, -89.5],
          dtype='float32', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
           ...
           350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
          dtype='float32', name='lon', length=360))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1981-12-01', '1982-01-01', '1982-02-01', '1982-03-01',
                   '1982-04-01', '1982-05-01', '1982-06-01', '1982-07-01',
                   '1982-08-01', '1982-09-01',
                   ...
                   '2022-04-01', '2022-05-01', '2022-06-01', '2022-07-01',
                   '2022-08-01', '2022-09-01', '2022-10-01', '2022-11-01',
                   '2022-12-01', '2023-01-01'],
                  dtype='datetime64[ns]', name='time', length=494, freq=None))

• Attributes: (11)

  title :

  NOAA Optimum Interpolation (OI) SST V2

  Conventions :

  CF-1.0

  history :

  Wed Apr 6 13:47:45 2005: ncks -d time,0,278 SAVEs/sst.mnmean.nc sst.mnmean.nc Created 10/2002 by RHS

  comments :

  Data described in Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang, 2002: An Improved In Situ and Satellite SST Analysis for Climate, J. Climate

  platform :

  Model

  source :

  NCEP Climate Modeling Branch

  institution :

  National Centers for Environmental Prediction

  References :

  https://www.psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html

  dataset_title :

  NOAA Optimum Interpolation (OI) SST V2

  source_url :

  http://www.emc.ncep.noaa.gov/research/cmb/sst_analysis/

  DODS_EXTRA.Unlimited_Dimension :

  time