LLC4320_SST
MITgcm LLC4320 Ocean Simulation Sea Surface Temperature
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/llc4320.yaml")
ds = cat["LLC4320_SST"].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 | http://online.kitp.ucsb.edu/online/blayers18/menemenlis/ |
| tags | ['ocean', 'model'] |
Dataset Contents
xarray.Dataset
- face: 13
- i: 4320
- j: 4320
- time: 9030
- face(face)int640 1 2 3 4 5 6 7 8 9 10 11 12
- standard_name :
- face_index
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
- i(i)int640 1 2 3 4 ... 4316 4317 4318 4319
- axis :
- X
- long_name :
- x-dimension of the t grid
- standard_name :
- x_grid_index
- swap_dim :
- XC
array([ 0, 1, 2, ..., 4317, 4318, 4319])
- j(j)int640 1 2 3 4 ... 4316 4317 4318 4319
- axis :
- Y
- long_name :
- y-dimension of the t grid
- standard_name :
- y_grid_index
- swap_dim :
- YC
array([ 0, 1, 2, ..., 4317, 4318, 4319])
- time(time)datetime64[ns]2011-09-13 ... 2012-09-23T05:00:00
- axis :
- T
- long_name :
- Time
- standard_name :
- time
array(['2011-09-13T00:00:00.000000000', '2011-09-13T01:00:00.000000000', '2011-09-13T02:00:00.000000000', ..., '2012-09-23T03:00:00.000000000', '2012-09-23T04:00:00.000000000', '2012-09-23T05:00:00.000000000'], dtype='datetime64[ns]')
- SST(time, face, j, i)float32dask.array<chunksize=(1, 1, 4320, 4320), meta=np.ndarray>
Array Chunk Bytes 8.76 TB 74.65 MB Shape (9030, 13, 4320, 4320) (1, 1, 4320, 4320) Count 117391 Tasks 117390 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.6
- history :
- Created by calling `open_mdsdataset(llc_method='smallchunks', nz=None, ny=None, nx=None, default_dtype=dtype('>f4'), ignore_unknown_vars=True, chunks=None, endian='>', swap_dims=None, grid_vars_to_coords=True, geometry='llc', calendar='gregorian', ref_date=None, delta_t=25.0, read_grid=True, prefix=None, iters=[10368], grid_dir='/pleiades/u/dmenemen/llc_4320/grid/', data_dir='/pleiades/u/dmenemen/llc_4320/MITgcm/run/')`
- source :
- MITgcm
- title :
- netCDF wrapper of MITgcm MDS binary data