NATL60_vertical_grid

NEMO NATL60 Ocean Simulation Vertical 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["NATL60_vertical_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://github.com/meom-configurations/NATL60-CJM165
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- t: 1
- x: 5422
- y: 3454
- z: 300
Coordinates: (0)

Data variables: (15)

e3t

(t, z, y, x)

float32

dask.array<chunksize=(1, 1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  22.47 GB   74.91 MB 
     Shape  (1, 300, 3454, 5422)   (1, 1, 3454, 5422) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

e3t_0

(t, z)

float32

dask.array<chunksize=(1, 1), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  1.20 kB   4 B 
     Shape  (1, 300)   (1, 1) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

e3u

(t, z, y, x)

float32

dask.array<chunksize=(1, 1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  22.47 GB   74.91 MB 
     Shape  (1, 300, 3454, 5422)   (1, 1, 3454, 5422) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

e3v

(t, z, y, x)

float32

dask.array<chunksize=(1, 1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  22.47 GB   74.91 MB 
     Shape  (1, 300, 3454, 5422)   (1, 1, 3454, 5422) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

e3w

(t, z, y, x)

float32

dask.array<chunksize=(1, 1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  22.47 GB   74.91 MB 
     Shape  (1, 300, 3454, 5422)   (1, 1, 3454, 5422) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

e3w_0

(t, z)

float32

dask.array<chunksize=(1, 1), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  1.20 kB   4 B 
     Shape  (1, 300)   (1, 1) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

gdept_0

(t, z)

float32

dask.array<chunksize=(1, 1), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  1.20 kB   4 B 
     Shape  (1, 300)   (1, 1) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

gdepw_0

(t, z)

float32

dask.array<chunksize=(1, 1), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  1.20 kB   4 B 
     Shape  (1, 300)   (1, 1) 
     Count  301 Tasks  300 Chunks 
     Type  float32  numpy.ndarray

hdept

(t, y, x)

float32

dask.array<chunksize=(1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   74.91 MB 
     Shape  (1, 3454, 5422)   (1, 3454, 5422) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hdepw

(t, y, x)

float32

dask.array<chunksize=(1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   74.91 MB 
     Shape  (1, 3454, 5422)   (1, 3454, 5422) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

mbathy

(t, y, x)

float32

dask.array<chunksize=(1, 3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   74.91 MB 
     Shape  (1, 3454, 5422)   (1, 3454, 5422) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

nav_lat

(y, x)

float32

dask.array<chunksize=(3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   74.91 MB 
     Shape  (3454, 5422)   (3454, 5422) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

nav_lev

(z)

float32

dask.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)

float32

dask.array<chunksize=(3454, 5422), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   74.91 MB 
     Shape  (3454, 5422)   (3454, 5422) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time_counter

(t)

float32

dask.array<chunksize=(1,), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  4 B   4 B 
     Shape  (1,)   (1,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

Attributes: (0)