eNATL60_BLBT02_SSH

Hourly outputs of eNATL60-BLBT02 (with tides) Sea Surface Height

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_BLBT02_SSH"].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

Dimensions:
- axis_nbounds: 2
- time_counter: 11688
- x: 8354
- y: 4729

Coordinates: (2)

time_centered

(time_counter)

datetime64[ns]

dask.array<chunksize=(240,), meta=np.ndarray>

bounds :: time_centered_bounds
long_name :: Time axis
standard_name :: time
time_origin :: 1900-01-01 00:00:00





  
      Array  Chunk 
  
  
     Bytes  93.50 kB   1.92 kB 
     Shape  (11688,)   (240,) 
     Count  50 Tasks  49 Chunks 
     Type  datetime64[ns]  numpy.ndarray

time_counter

(time_counter)

datetime64[ns]

2010-01-19T00:30:00 ... 2009-09-17T23:30:00

axis :: T
bounds :: time_counter_bounds
long_name :: Time axis
standard_name :: time
time_origin :: 1900-01-01 00:00:00

array(['2010-01-19T00:30:00.000000000', '2010-01-19T01:30:00.000000000',
       '2010-01-19T02:30:00.000000000', ..., '2009-09-17T21:30:00.000000000',
       '2009-09-17T22:30:00.000000000', '2009-09-17T23:30:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (5)

nav_lat

(y, x)

float32

dask.array<chunksize=(240, 480), meta=np.ndarray>

long_name :: Latitude
standard_name :: latitude
units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  158.02 MB   460.80 kB 
     Shape  (4729, 8354)   (240, 480) 
     Count  361 Tasks  360 Chunks 
     Type  float32  numpy.ndarray

nav_lon

(y, x)

float32

dask.array<chunksize=(240, 480), meta=np.ndarray>

long_name :: Longitude
standard_name :: longitude
units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  158.02 MB   460.80 kB 
     Shape  (4729, 8354)   (240, 480) 
     Count  361 Tasks  360 Chunks 
     Type  float32  numpy.ndarray

sossheig

(time_counter, y, x)

float32

dask.array<chunksize=(240, 240, 480), meta=np.ndarray>

interval_write :: 1 h
long_name :: sea surface height
online_operation :: average
standard_name :: sea_surface_height_above_geoid
units :: m





  
      Array  Chunk 
  
  
     Bytes  1.85 TB   110.59 MB 
     Shape  (11688, 4729, 8354)   (240, 240, 480) 
     Count  17641 Tasks  17640 Chunks 
     Type  float32  numpy.ndarray

time_centered_bounds

(time_counter, axis_nbounds)

datetime64[ns]

dask.array<chunksize=(240, 2), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  187.01 kB   3.84 kB 
     Shape  (11688, 2)   (240, 2) 
     Count  50 Tasks  49 Chunks 
     Type  datetime64[ns]  numpy.ndarray

time_counter_bounds

(time_counter, axis_nbounds)

datetime64[ns]

dask.array<chunksize=(240, 2), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  187.01 kB   3.84 kB 
     Shape  (11688, 2)   (240, 2) 
     Count  50 Tasks  49 Chunks 
     Type  datetime64[ns]  numpy.ndarray

Attributes: (12)
Conventions :
CF-1.6
TimeStamp :
24/03/2019 14:43:29 +0100
description :
ocean T grid variables
file_name :
eNATL60-BLBT02X_1h_20100101_20100125_gridT-2D_20100119-20100119.nc
ibegin :
0
jbegin :
0
name :
/scratch/tmp/5251284/eNATL60-BLBT02X_1h_20100101_20100125_gridT-2D
ni :
8354
nj :
10
timeStamp :
2019-Mar-24 06:31:35 GMT
title :
ocean T grid variables
uuid :
80877374-74d2-468e-9fc6-d8cae8009094