channel_ridge_05km_floats

MITgcm output from a wind and thermally driven channel with a ridge at 5km resolution (float vars)

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/channel.yaml")
ds  = cat["channel_ridge_05km_floats"].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

time_resolution	10 day profile snapshots
duration	5 years
uploader_github	cspencerjones
uploader_email	spencerj@ldeo.columbia.edu
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- depth: 40
- profile_index: 28998981

Coordinates: (1)

depth

(depth)

float32

-5.0 -15.0 ... -2830.5 -2933.5

long_name :: Depth where temperature was recorded at time_up
units :: m

array([   -5. ,   -15. ,   -25. ,   -36. ,   -49. ,   -64. ,   -81.5,  -102. ,
        -126. ,  -154. ,  -187. ,  -226. ,  -272. ,  -327. ,  -393. ,  -471.5,
        -565. ,  -667.5,  -770.5,  -873.5,  -976.5, -1079.5, -1182.5, -1285.5,
       -1388.5, -1491.5, -1594.5, -1697.5, -1800.5, -1903.5, -2006.5, -2109.5,
       -2212.5, -2315.5, -2418.5, -2521.5, -2624.5, -2727.5, -2830.5, -2933.5],
      dtype=float32)

Data variables: (8)

Temperature

(profile_index, depth)

float64

dask.array<chunksize=(400000, 40), meta=np.ndarray>

long_name :: temperature at time_up
units :: degC





  
      Array  Chunk 
  
  
     Bytes  9.28 GB   128.00 MB 
     Shape  (28998981, 40)   (400000, 40) 
     Count  74 Tasks  73 Chunks 
     Type  float64  numpy.ndarray

npart

(profile_index)

float64

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: float number





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  float64  numpy.ndarray

time_down

(profile_index)

object

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: Time when the float descended





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  object  numpy.ndarray

time_up

(profile_index)

object

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: Time when the float ascended





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  object  numpy.ndarray

x_down

(profile_index)

float64

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: Location in x at time_down
units :: m





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  float64  numpy.ndarray

x_up

(profile_index)

float64

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: Location in x at time_up
units :: m





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  float64  numpy.ndarray

y_down

(profile_index)

float64

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: Location in y at time_down
units :: m





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  float64  numpy.ndarray

y_up

(profile_index)

float64

dask.array<chunksize=(400000,), meta=np.ndarray>

long_name :: Location in y at time_up
units :: m





  
      Array  Chunk 
  
  
     Bytes  231.99 MB   3.20 MB 
     Shape  (28998981,)   (400000,) 
     Count  74 Tasks  73 Chunks 
     Type  float64  numpy.ndarray

Attributes: (5)
history :
06/06/2020, cspencerjones updated times to be in seconds since 0000-01-01
institution :
Lamont Doherty Earth Observatory, Columbia University
references :
Model: https://doi.org/10.1029/96JC02775, Configuration: https://doi.org/10.1016/j.ocemod.2013.07.004
source :
MITgcm checkpoint66b
title :
Temperature data from synthetic Argo floats in re-entrant channel simulation