NATL60_SSU
Daily outputs of NATL60-CJM165 Sea Surface Zonal Velocity
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_SSU"].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
gcloudcommand 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
gcloudto 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
- axis_nbounds: 2
- time_counter: 8760
- x: 5422
- y: 3454
- 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]')
- nav_lat(y, x)float32dask.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)float32dask.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_counter_bounds(time_counter, axis_nbounds)datetime64[ns]dask.array<chunksize=(744, 2), meta=np.ndarray>
- coordinates :
- time_centered
Array Chunk Bytes 140.16 kB 11.90 kB Shape (8760, 2) (744, 2) Count 13 Tasks 12 Chunks Type datetime64[ns] numpy.ndarray - vozocrtx(time_counter, y, x)float32dask.array<chunksize=(24, 120, 120), meta=np.ndarray>
- cell_methods :
- time: mean (interval: 40 s)
- coordinates :
- nav_lat time_centered nav_lon
- interval_operation :
- 40 s
- interval_write :
- 1 h
- long_name :
- ocean surface current along i-axis
- online_operation :
- average
- units :
- m/s
Array Chunk Bytes 656.21 GB 1.38 MB Shape (8760, 3454, 5422) (24, 120, 120) Count 486911 Tasks 486910 Chunks Type float32 numpy.ndarray
- CASE :
- CJM165
- CONFIG :
- NATL60
- Conventions :
- CF-1.5
- NCO :
- 4.4.6
- description :
- ocean U grid variables
- history :
- Sat May 11 06:51:40 2019: ncks -O -v vozocrtx /store/molines/NATL60/NATL60-CJM165-S/1h/2012/NATL60-CJM165_y2012m10d01.1h_gridU.nc NATL60-CJM165_y2012m10d01.1h_SSU.nc
- output_frequency :
- 1h
- production :
- An IPSL model
- start_date :
- 20120301
- title :
- ocean U grid variables