NATL60_horizontal_grid

NEMO NATL60 Ocean Simulation Horizontal 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_horizontal_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
Coordinates: (0)

Data variables: (19)

e1f

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e1t

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e1u

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e1v

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e2f

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e2t

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e2u

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

e2v

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

ff

(t, y, x)

float32

dask.array<chunksize=(1, 432, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 432, 678) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

glamf

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

glamt

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

glamu

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

glamv

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

gphif

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

gphit

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

gphiu

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

gphiv

(t, y, x)

float64

dask.array<chunksize=(1, 216, 678), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  149.82 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 216, 678) 
     Count  129 Tasks  128 Chunks 
     Type  float64  numpy.ndarray

nav_lat

(t, y, x)

float32

dask.array<chunksize=(1, 432, 678), meta=np.ndarray>

long_name :: Latitude
units :: degrees_north
valid_max :: 67.48115539550781
valid_min :: 26.417089462280273





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 432, 678) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

nav_lon

(t, y, x)

float32

dask.array<chunksize=(1, 432, 678), meta=np.ndarray>

long_name :: Longitude
units :: degrees_east
valid_max :: 17.958284378051758
valid_min :: -86.67500305175781





  
      Array  Chunk 
  
  
     Bytes  74.91 MB   1.17 MB 
     Shape  (1, 3454, 5422)   (1, 432, 678) 
     Count  65 Tasks  64 Chunks 
     Type  float32  numpy.ndarray

Attributes: (4)
NCO :
4.4.2
comment :
NATL60 file, resized for the V4 Bathymetry
history :
Thu Apr 23 12:19:18 2015: ncrename -d time_counter,t NATL60_v4.1_cdf_mesh_hgr.nc
nco_openmp_thread_number :
1