LLC4320_SSS
MITgcm LLC4320 Ocean Simulation Sea Surface Salinity
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_SSS"].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:- Create a project on GCP; if this is the first time using GCP, a prompt will appear to choose a Google account to link to all GCP-related activities.
- Create a Cloud Billing account associated with the project and enable billing for the project through this account.
- Using Google Cloud IAM, add the Service Usage Consumer role to your account, which enables it to make billed requests on the behalf of the project.
- Through command line, install the Google Cloud SDK; this can be done using conda:
conda install -c conda-forge google-cloud-sdk - Initialize the
gcloudcommand line interface, logging into the account used to create the aforementioned project and selecting it as the default project; this will allow the project to be used for requester pays access through the command line:gcloud auth login gcloud init - Finally, use
gcloudto establish application default credentials; this will allow the project to be used for requester pays access through applications:gcloud auth application-default login
Metadata
| url | http://online.kitp.ucsb.edu/online/blayers18/menemenlis/ |
| tags | ['ocean', 'model'] |
Dataset Contents
xarray.Dataset
- face: 13
- i: 4320
- i_g: 4320
- j: 4320
- j_g: 4320
- k: 90
- k_l: 90
- k_p1: 91
- k_u: 90
- time: 9030
- face(face)int640 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(i)int640 1 2 3 4 ... 4316 4317 4318 4319
- axis :
- X
- long_name :
- x-dimension of the t grid
- standard_name :
- x_grid_index
- swap_dim :
- XC
array([ 0, 1, 2, ..., 4317, 4318, 4319])
- i_g(i_g)int640 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)int640 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])
- j_g(j_g)int640 1 2 3 4 ... 4316 4317 4318 4319
- axis :
- Y
- c_grid_axis_shift :
- -0.5
- long_name :
- y-dimension of the v grid
- standard_name :
- y_grid_index_at_v_location
- swap_dim :
- YG
array([ 0, 1, 2, ..., 4317, 4318, 4319])
- k(k)int640 1 2 3 4 5 6 ... 84 85 86 87 88 89
- axis :
- Z
- long_name :
- z-dimension of the t grid
- standard_name :
- z_grid_index
- swap_dim :
- Z
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]) - k_l(k_l)int640 1 2 3 4 5 6 ... 84 85 86 87 88 89
- axis :
- Z
- c_grid_axis_shift :
- -0.5
- long_name :
- z-dimension of the w grid
- standard_name :
- z_grid_index_at_upper_w_location
- swap_dim :
- Zl
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]) - k_p1(k_p1)int640 1 2 3 4 5 6 ... 85 86 87 88 89 90
- axis :
- Z
- c_grid_axis_shift :
- [-0.5, 0.5]
- long_name :
- z-dimension of the w grid
- standard_name :
- z_grid_index_at_w_location
- swap_dim :
- Zp1
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]) - k_u(k_u)int640 1 2 3 4 5 6 ... 84 85 86 87 88 89
- axis :
- Z
- c_grid_axis_shift :
- 0.5
- long_name :
- z-dimension of the w grid
- standard_name :
- z_grid_index_at_lower_w_location
- swap_dim :
- Zu
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]) - 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]')
- SSS(time, face, j, i)float32dask.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 - XC(face, j, i)float32dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>
- coordinate :
- YC XC
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
Array Chunk Bytes 970.44 MB 74.65 MB Shape (13, 4320, 4320) (1, 4320, 4320) Count 14 Tasks 13 Chunks Type float32 numpy.ndarray - YC(face, j, i)float32dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>
- coordinate :
- YC XC
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
Array Chunk Bytes 970.44 MB 74.65 MB Shape (13, 4320, 4320) (1, 4320, 4320) Count 14 Tasks 13 Chunks Type float32 numpy.ndarray - iter(time)int64dask.array<chunksize=(9030,), meta=np.ndarray>
Array Chunk Bytes 72.24 kB 72.24 kB Shape (9030,) (9030,) Count 2 Tasks 1 Chunks Type int64 numpy.ndarray
- 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