Interactivity

We can do more things in HTML-land than we can in PDF-land, like using Jupyter’s interactive features. We can inspect the Pangeo Forge (Coastwatch 2023) dataset in this way.

import xarray as xr

store = 'https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/noaa-coastwatch-geopolar-sst-feedstock/noaa-coastwatch-geopolar-sst.zarr'
ds = xr.open_dataset(store, engine='zarr', chunks={})
ds

<xarray.Dataset>
Dimensions:           (time: 7134, lat: 3600, lon: 7200)
Coordinates:
  * lat               (lat) float32 -89.97 -89.93 -89.88 ... 89.88 89.93 89.97
  * lon               (lon) float32 -180.0 -179.9 -179.9 ... 179.9 179.9 180.0
  * time              (time) datetime64[ns] 2002-09-01T12:00:00 ... 2022-03-2...
Data variables:
    analysed_sst      (time, lat, lon) float32 dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>
    analysis_error    (time, lat, lon) float32 dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>
    mask              (time, lat, lon) float32 dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>
    sea_ice_fraction  (time, lat, lon) float32 dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>
Attributes: (12/47)
    Conventions:                CF-1.4, Unidata Observation Dataset v1.0
    Metadata_Conventions:       Unidata Observation Dataset v1.0
    acknowledgment:             NOAA/NESDIS
    cdm_data_type:              grid
    comment:                    The Geo-Polar Blended Sea Surface Temperature...
    creator_email:              andy.harris@noaa.gov
    ...                         ...
    summary:                    An SST estimation scheme which combines multi...
    time_coverage_end:          20020902T000000Z
    time_coverage_start:        20020901T000000Z
    title:                      Analysed blended sea surface temperature over...
    uuid:                       7c4fc02a-1034-4021-be6f-c3ce858fd33a
    westernmost_longitude:      -180.0

xarray.Dataset

• Dimensions:
  • time: 7134
  • lat: 3600
  • lon: 7200
• Coordinates: (3)
  • lat
    (lat)
    float32
    -89.97 -89.93 ... 89.93 89.97

    axis :

    Y

    comment :

    equirectangular projection

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-89.975, -89.925, -89.875, ...,  89.875,  89.925,  89.975],
          dtype=float32)

  • lon
    (lon)
    float32
    -180.0 -179.9 ... 179.9 180.0

    axis :

    X

    comment :

    equirectangular projection

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-179.975, -179.925, -179.875, ...,  179.875,  179.925,  179.975],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2002-09-01T12:00:00 ... 2022-03-...

    axis :

    T

    comment :

    Nominal time of Level 4 analysis

    long_name :

    reference time of sst field

    standard_name :

    time

    array(['2002-09-01T12:00:00.000000000', '2002-09-02T12:00:00.000000000',
           '2002-09-03T12:00:00.000000000', ..., '2022-03-18T12:00:00.000000000',
           '2022-03-19T12:00:00.000000000', '2022-03-20T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (4)
  • analysed_sst
    (time, lat, lon)
    float32
    dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>

    comment :

    Analysed SST for each ocean grid point

    long_name :

    analysed sea surface temperature

    reference :

    Fieguth,P.W. et al. "Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm", J. Atmos. Ocean Tech, 15 (2): 535-546, 1998. Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001. Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, "Statistical Processing of Large Image Sequences", IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005. Maturi, E., A. Harris, J. Mittaz, J. Sapper, G. Wick, X. Zhu, P. Dash, P. Koner, "A New High-Resolution Sea Surface Temperature Blended Analysis", Bulleting of the American Meteorological Society, 98 (5), 1015-1026, 2017.

    source :

    STAR-ACSPO_GAC, STAR-ACSPO_H-8, STAR-Geo_SST, UKMO-OSTIA

    standard_name :

    sea_surface_foundation_temperature

    units :

    kelvin

    valid_max :

    4000

    valid_min :

    -200

    Array Chunk Bytes 688.86 GiB 98.88 MiB Shape (7134, 3600, 7200) (2, 1800, 7200) Dask graph 7134 chunks in 2 graph layers Data type float32 numpy.ndarray

  • analysis_error
    (time, lat, lon)
    float32
    dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>

    comment :

    Estimate of internal analysis accuracy

    long_name :

    estimated error standard deviation of analysed_sst

    units :

    kelvin

    valid_max :

    500

    valid_min :

    0

    Array Chunk Bytes 688.86 GiB 98.88 MiB Shape (7134, 3600, 7200) (2, 1800, 7200) Dask graph 7134 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mask
    (time, lat, lon)
    float32
    dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>

    comment :

    b0: 1=grid cell is water, b1: 1=grid cell is land, b2: 1=grid cell is ice

    flag_meanings :

    water land ice

    flag_values :

    [1, 2, 4]

    long_name :

    sea/land/ice bit mask

    source :

    OSTIA reanalysis land mask

    standard_name :

    sea_land_ice_bit_mask

    valid_max :

    4

    valid_min :

    1

    Array Chunk Bytes 688.86 GiB 98.88 MiB Shape (7134, 3600, 7200) (2, 1800, 7200) Dask graph 7134 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_ice_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(2, 1800, 7200), meta=np.ndarray>

    comment :

    Percentage of ice

    long_name :

    sea ice fraction

    source :

    OSTIA reanalysis ice mask

    standard_name :

    sea_ice_area_fraction

    units :

    1

    valid_max :

    100

    valid_min :

    0

    Array Chunk Bytes 688.86 GiB 98.88 MiB Shape (7134, 3600, 7200) (2, 1800, 7200) Dask graph 7134 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([ -89.9749984741211, -89.92500305175781,            -89.875,
                  -89.82499694824219,  -89.7750015258789,  -89.7249984741211,
                  -89.67500305175781,            -89.625, -89.57499694824219,
                   -89.5250015258789,
                  ...
                    89.5250015258789,  89.57499694824219,             89.625,
                   89.67500305175781,   89.7249984741211,   89.7750015258789,
                   89.82499694824219,             89.875,  89.92500305175781,
                    89.9749984741211],
                 dtype='float64', name='lat', length=3600))

  • lon
    PandasIndex

    PandasIndex(Float64Index([-179.97500610351562,  -179.9250030517578,            -179.875,
                   -179.8249969482422, -179.77499389648438, -179.72500610351562,
                   -179.6750030517578,            -179.625,  -179.5749969482422,
                  -179.52499389648438,
                  ...
                   179.52499389648438,   179.5749969482422,             179.625,
                    179.6750030517578,  179.72500610351562,  179.77499389648438,
                    179.8249969482422,             179.875,   179.9250030517578,
                   179.97500610351562],
                 dtype='float64', name='lon', length=7200))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-09-01 12:00:00', '2002-09-02 12:00:00',
                   '2002-09-03 12:00:00', '2002-09-04 12:00:00',
                   '2002-09-05 12:00:00', '2002-09-06 12:00:00',
                   '2002-09-07 12:00:00', '2002-09-08 12:00:00',
                   '2002-09-09 12:00:00', '2002-09-10 12:00:00',
                   ...
                   '2022-03-11 12:00:00', '2022-03-12 12:00:00',
                   '2022-03-13 12:00:00', '2022-03-14 12:00:00',
                   '2022-03-15 12:00:00', '2022-03-16 12:00:00',
                   '2022-03-17 12:00:00', '2022-03-18 12:00:00',
                   '2022-03-19 12:00:00', '2022-03-20 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=7134, freq=None))

• Attributes: (47)

  Conventions :

  CF-1.4, Unidata Observation Dataset v1.0

  Metadata_Conventions :

  Unidata Observation Dataset v1.0

  acknowledgment :

  NOAA/NESDIS

  cdm_data_type :

  grid

  comment :

  The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST

  creator_email :

  andy.harris@noaa.gov

  creator_name :

  Satellite Applications and Research

  creator_url :

  www.star.nesdis.noaa.gov

  date_created :

  20170622T163825Z

  easternmost_longitude :

  180.0

  file_quality_level :

  0.0

  gds_version_id :

  2.0

  geospatial_lat_resolution :

  0.05

  geospatial_lat_units :

  degrees north

  geospatial_lon_resolution :

  0.05

  geospatial_lon_units :

  degrees east

  history :

  Fri Oct 19 11:12:34 2018: ncatted -a add_offset,analysis_error,o,f,0. 20020901000000-STAR-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0.nc Fri Oct 19 11:09:25 2018: ncatted -a add_offset,analysed_sst,o,f,273.15 20020901000000-STAR-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0.nc NESDIS geo-SST L1 to L2 processor, NESDIS Advanced Clear-Sky Processor for Oceans (ACSPO), NESDIS Geo-Polar 1/20th degree Blended SST Analysis

  id :

  Geo_Polar_Blended_Night-STAR-L4-GLOB-v1.0

  institution :

  Office of Satellite Products and Operations

  keywords :

  Oceans > Ocean Temperature > Sea Surface Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  GHRSST protocol describes data use as free and open

  metadata_link :

  http://podaac.jpl.nasa.gov:8890/ws/metadata/dataset?format=iso&shortName=Geo_Polar_Blended_Night-STAR-L4-GLOB-v1.0

  naming_authority :

  org.ghrsst

  netcdf_version_id :

  4.1.3

  northernmost_latitude :

  90.0

  platform :

  NOAA-16, NOAA-17, NOAA-18, NOAA-19, MetOpA, Himawari-8, GOES10, GOES11, GOES12, GOES13, GOES15, MTSAT1R, MTSAT2

  processing_level :

  L4

  product_version :

  1.0

  project :

  Group for High Resolution Sea Surface Temperature

  publisher_email :

  ghrsst-po@nceo.ac.uk

  publisher_name :

  The GHRSST Project Office

  publisher_url :

  http://www.ghrsst.org

  references :

  Fieguth,P.W. et al. "Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm", J. Atmos. Ocean Tech, 15 (2): 535-546, 1998. Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001. Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, "Statistical Processing of Large Image Sequences", IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005. Maturi, E., A. Harris, J. Mittaz, J. Sapper, G. Wick, X. Zhu, P. Dash, P. Koner, "A New High-Resolution Sea Surface Temperature Blended Analysis", Bulleting of the American Meteorological Society, 98 (5), 1015-1026, 2017.

  sensor :

  AVHRR_GAC, AHI, GOES_Imager, JAMI

  source :

  STAR-ACSPO_GAC, STAR-ACSPO_H-8, STAR-Geo_SST, UKMO-OSTIA

  southernmost_latitude :

  -90.0

  spatial_resolution :

  0.05 degree

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convetion

  start_time :

  20020901T000000Z

  stop_time :

  20020902T000000Z

  summary :

  An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary IR and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05o spatial resolution.

  time_coverage_end :

  20020902T000000Z

  time_coverage_start :

  20020901T000000Z

  title :

  Analysed blended sea surface temperature over the global ocean

  uuid :

  7c4fc02a-1034-4021-be6f-c3ce858fd33a

  westernmost_longitude :

  -180.0

References

Coastwatch, NOAA. 2023. “NOAA Geo-Polar Blended Global Sea Surface Temperature Analysis (Level 4).” March 2023. https://nsidc.github.io/quarto-demo.