eNATL60_grid
NEMO eNATL60 Ocean Simulation Grid
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/MEOM-NEMO.yaml")
ds = cat["eNATL60_grid"].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
gcloud
command 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
gcloud
to 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 | https://mycore.core-cloud.net/index.php/s/zQAcDHWhxiGt1RW#pdfviewer |
tags | ['ocean', 'model'] |
Dataset Contents
xarray.Dataset
- t: 1
- x: 8354
- y: 4729
- z: 300
- e1f(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e1t(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e1u(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e1v(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e2f(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e2t(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e2u(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e2v(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - e3t_0(t, z, y, x)float64dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 94.81 GB 316.05 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - e3t_1d(t, z)float64dask.array<chunksize=(1, 1), meta=np.ndarray>
Array Chunk Bytes 2.40 kB 8 B Shape (1, 300) (1, 1) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - e3u_0(t, z, y, x)float64dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 94.81 GB 316.05 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - e3v_0(t, z, y, x)float64dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 94.81 GB 316.05 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - e3w_0(t, z, y, x)float64dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 94.81 GB 316.05 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - e3w_1d(t, z)float64dask.array<chunksize=(1, 1), meta=np.ndarray>
Array Chunk Bytes 2.40 kB 8 B Shape (1, 300) (1, 1) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - ff(t, y, x)float64dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 316.05 MB 316.05 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - fmask(t, z, y, x)int8dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 11.85 GB 39.51 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type int8 numpy.ndarray - fmaskutil(t, y, x)int8dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 39.51 MB 39.51 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type int8 numpy.ndarray - gdept_0(t, z, y, x)float32dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 47.41 GB 158.02 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float32 numpy.ndarray - gdept_1d(t, z)float64dask.array<chunksize=(1, 1), meta=np.ndarray>
Array Chunk Bytes 2.40 kB 8 B Shape (1, 300) (1, 1) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - gdepu(t, z, y, x)float32dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 47.41 GB 158.02 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float32 numpy.ndarray - gdepv(t, z, y, x)float32dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 47.41 GB 158.02 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float32 numpy.ndarray - gdepw_0(t, z, y, x)float32dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 47.41 GB 158.02 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type float32 numpy.ndarray - gdepw_1d(t, z)float64dask.array<chunksize=(1, 1), meta=np.ndarray>
Array Chunk Bytes 2.40 kB 8 B Shape (1, 300) (1, 1) Count 301 Tasks 300 Chunks Type float64 numpy.ndarray - glamf(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - glamt(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - glamu(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - glamv(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - gphif(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - gphit(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - gphiu(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - gphiv(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - isfdraft(t, y, x)float32dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - mbathy(t, y, x)int16dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 79.01 MB 79.01 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type int16 numpy.ndarray - misf(t, y, x)int16dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 79.01 MB 79.01 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type int16 numpy.ndarray - nav_lat(y, x)float32dask.array<chunksize=(4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (4729, 8354) (4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - nav_lev(z)float32dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 1.20 kB 4 B Shape (300,) (1,) Count 301 Tasks 300 Chunks Type float32 numpy.ndarray - nav_lon(y, x)float32dask.array<chunksize=(4729, 8354), meta=np.ndarray>
Array Chunk Bytes 158.02 MB 158.02 MB Shape (4729, 8354) (4729, 8354) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - time_counter(t)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 8 B 8 B Shape (1,) (1,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - tmask(t, z, y, x)int8dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 11.85 GB 39.51 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type int8 numpy.ndarray - tmaskutil(t, y, x)int8dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 39.51 MB 39.51 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type int8 numpy.ndarray - umask(t, z, y, x)int8dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 11.85 GB 39.51 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type int8 numpy.ndarray - umaskutil(t, y, x)int8dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 39.51 MB 39.51 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type int8 numpy.ndarray - vmask(t, z, y, x)int8dask.array<chunksize=(1, 1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 11.85 GB 39.51 MB Shape (1, 300, 4729, 8354) (1, 1, 4729, 8354) Count 301 Tasks 300 Chunks Type int8 numpy.ndarray - vmaskutil(t, y, x)int8dask.array<chunksize=(1, 4729, 8354), meta=np.ndarray>
Array Chunk Bytes 39.51 MB 39.51 MB Shape (1, 4729, 8354) (1, 4729, 8354) Count 2 Tasks 1 Chunks Type int8 numpy.ndarray
- TimeStamp :
- 13/07/2018 22:32:53 +0200
- file_name :
- mesh_mask.nc