LLC4320_SSU

MITgcm LLC4320 Ocean Simulation Sea Surface Zonal Velocity

Load in Python

from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/llc4320.yaml") ds = cat["LLC4320_SSU"].to_dask()

Working with requester pays data

Several of the datasets within the cloud data catalog are contained in requester pays storage buckets. This means that a user requesting data must provide their own billing project (created and authenticated through Google Cloud Platform) to be billed for the charges associated with accessing a dataset. To set up an GCP billing project and use it for authentication in applications:

Metadata

url http://online.kitp.ucsb.edu/online/blayers18/menemenlis/
tags ['ocean', 'model']

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • face: 13
    • i_g: 4320
    • j: 4320
    • time: 9030
    • face
      (face)
      int64
      0 1 2 3 4 5 6 7 8 9 10 11 12
      standard_name :
      face_index
      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])
    • i_g
      (i_g)
      int64
      0 1 2 3 4 ... 4316 4317 4318 4319
      axis :
      X
      c_grid_axis_shift :
      -0.5
      long_name :
      x-dimension of the u grid
      standard_name :
      x_grid_index_at_u_location
      swap_dim :
      XG
      array([   0,    1,    2, ..., 4317, 4318, 4319])
    • j
      (j)
      int64
      0 1 2 3 4 ... 4316 4317 4318 4319
      axis :
      Y
      long_name :
      y-dimension of the t grid
      standard_name :
      y_grid_index
      swap_dim :
      YC
      array([   0,    1,    2, ..., 4317, 4318, 4319])
    • time
      (time)
      datetime64[ns]
      2011-09-13 ... 2012-09-23T05:00:00
      axis :
      T
      long_name :
      Time
      standard_name :
      time
      array(['2011-09-13T00:00:00.000000000', '2011-09-13T01:00:00.000000000',
             '2011-09-13T02:00:00.000000000', ..., '2012-09-23T03:00:00.000000000',
             '2012-09-23T04:00:00.000000000', '2012-09-23T05:00:00.000000000'],
            dtype='datetime64[ns]')
    • U
      (time, face, j, i_g)
      float32
      dask.array<chunksize=(1, 1, 4320, 4320), meta=np.ndarray>
      Array Chunk
      Bytes 8.76 TB 74.65 MB
      Shape (9030, 13, 4320, 4320) (1, 1, 4320, 4320)
      Count 117391 Tasks 117390 Chunks
      Type float32 numpy.ndarray
      9030 1 4320 4320 13
  • Conventions :
    CF-1.6
    history :
    Created by calling `open_mdsdataset(llc_method='smallchunks', nz=None, ny=None, nx=None, default_dtype=dtype('>f4'), ignore_unknown_vars=True, chunks=None, endian='>', swap_dims=None, grid_vars_to_coords=True, geometry='llc', calendar='gregorian', ref_date=None, delta_t=25.0, read_grid=True, prefix=None, iters=[10368], grid_dir='/pleiades/u/dmenemen/llc_4320/grid/', data_dir='/pleiades/u/dmenemen/llc_4320/MITgcm/run/')`
    source :
    MITgcm
    title :
    netCDF wrapper of MITgcm MDS binary data