NATL60_SSH

Daily outputs of NATL60-CJM165 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["NATL60_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://github.com/meom-configurations/NATL60-CJM165
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- axis_nbounds: 2
- time_counter: 8760
- x: 5422
- y: 3454

Coordinates: (4)

nav_lat

(y, x)

float32

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

long_name :: Latitude
nav_model :: grid_T
standard_name :: latitude
units :: degrees_north





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

nav_lon

(y, x)

float32

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

long_name :: Longitude
nav_model :: grid_T
standard_name :: longitude
units :: degrees_east





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

time_centered

(time_counter)

datetime64[ns]

dask.array<chunksize=(744,), meta=np.ndarray>

bounds :: time_centered_bounds
long_name :: Time axis
standard_name :: time
time_origin :: 1958-01-01 00:00:00





  
      Array  Chunk 
  
  
     Bytes  70.08 kB   5.95 kB 
     Shape  (8760,)   (744,) 
     Count  13 Tasks  12 Chunks 
     Type  datetime64[ns]  numpy.ndarray

time_counter

(time_counter)

datetime64[ns]

2012-10-01T00:30:00 ... 2013-09-30T23:30:00

axis :: T
bounds :: time_counter_bounds
long_name :: Time axis
standard_name :: time
time_origin :: 1958-01-01 00:00:00

array(['2012-10-01T00:30:00.000000000', '2012-10-01T01:30:00.000000000',
       '2012-10-01T02:30:00.000000000', ..., '2013-09-30T21:30:00.000000000',
       '2013-09-30T22:30:00.000000000', '2013-09-30T23:30:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (2)

sossheig

(time_counter, y, x)

float32

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

cell_methods :: time: mean (interval: 40 s)
interval_operation :: 40 s
interval_write :: 1 h
long_name :: sea surface height
online_operation :: average
standard_name :: sea_surface_height_above_geoid
units :: m





  
      Array  Chunk 
  
  
     Bytes  656.21 GB   74.91 MB 
     Shape  (8760, 3454, 5422)   (1, 3454, 5422) 
     Count  8761 Tasks  8760 Chunks 
     Type  float32  numpy.ndarray

time_counter_bounds

(time_counter, axis_nbounds)

datetime64[ns]

dask.array<chunksize=(744, 2), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  140.16 kB   11.90 kB 
     Shape  (8760, 2)   (744, 2) 
     Count  13 Tasks  12 Chunks 
     Type  datetime64[ns]  numpy.ndarray

Attributes: (10)
CASE :
CJM165
CONFIG :
NATL60
Conventions :
CF-1.5
NCO :
4.4.6
description :
ocean T grid variables
history :
Tue Oct 30 18:04:29 2018: ncks -v sossheig ALL/NATL60-CJM165_y2012m10d01.1h_gridT.nc SSH/NATL60-CJM165_y2012m10d01.1h_SSH.nc
output_frequency :
1h
production :
An IPSL model
start_date :
20120301
title :
ocean T grid variables