LLC4320_grid

MITgcm LLC4320 Ocean Simulation Grid

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_grid"].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	http://online.kitp.ucsb.edu/online/blayers18/menemenlis/
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- face: 13
- i: 4320
- i_g: 4320
- j: 4320
- j_g: 4320
- k_p1: 2
- time: 9030

Coordinates: (37)

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XC
long_name :: AngleCS
standard_name :: Cos of grid orientation angle
units :





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

Depth

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: XC YC
long_name :: ocean depth
standard_name :: ocean_depth
units :: m





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

PHrefC
()
float32
...
long_name :
Reference Hydrostatic Pressure
standard_name :
cell_reference_pressure
units :
m2 s-2
```
array(15.4017, dtype=float32)
```

PHrefF

(k_p1)

float32

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

long_name :: Reference Hydrostatic Pressure
standard_name :: cell_reference_pressure
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  8 B   8 B 
     Shape  (2,)   (2,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XC
long_name :: AngleSN
standard_name :: Sin of grid orientation angle
units :





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XC
long_name :: longitude
standard_name :: longitude
units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

(face, j_g, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YG XG
long_name :: longitude
standard_name :: longitude_at_f_location
units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XC
long_name :: latitude
standard_name :: latitude
units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

(face, j_g, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

long_name :: latitude
standard_name :: latitude_at_f_location
units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

Z
()
float32
...
long_name :
vertical coordinate of cell center
positive :
down
standard_name :
depth
units :
m
```
array(-1.57, dtype=float32)
```
Zl
()
float32
...
long_name :
vertical coordinate of upper cell interface
positive :
down
standard_name :
depth_at_upper_w_location
units :
m
```
array(-1., dtype=float32)
```

Zp1

(k_p1)

float32

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

long_name :: vertical coordinate of cell interface
positive :: down
standard_name :: depth_at_w_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  8 B   8 B 
     Shape  (2,)   (2,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

Zu
()
float32
...
long_name :
vertical coordinate of lower cell interface
positive :
down
standard_name :
depth_at_lower_w_location
units :
m
```
array(-2.14, dtype=float32)
```

drC

(k_p1)

float32

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

long_name :: cell z size
standard_name :: cell_z_size_at_w_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  8 B   8 B 
     Shape  (2,)   (2,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

drF
()
float32
...
long_name :
cell z size
standard_name :
cell_z_size
units :
m
```
array(1.14, dtype=float32)
```

dxC

(face, j, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XG
long_name :: cell x size
standard_name :: cell_x_size_at_u_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

dxG

(face, j_g, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell x size
standard_name :: cell_x_size_at_v_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

dyC

(face, j_g, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell y size
standard_name :: cell_y_size_at_v_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

dyG

(face, j, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XG
long_name :: cell y size
standard_name :: cell_y_size_at_u_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

face
(face)
int64
0 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])
```

hFacC

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

hFacS

(face, j_g, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction_at_v_location





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

hFacW

(face, j, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

long_name :: vertical fraction of open cell
standard_name :: cell_vertical_fraction_at_u_location





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

i
(i)
int64
0 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])
```
i_g
(i_g)
int64
0 1 2 3 4 ... 4316 4317 4318 4319
axis :
X
c_grid_axis_shift :
-0.5
long_name :
x-dimension of the u grid
standard_name :
x_grid_index_at_u_location
swap_dim :
XG
```
array([   0,    1,    2, ..., 4317, 4318, 4319])
```

iter

(time)

int64

dask.array<chunksize=(9030,), meta=np.ndarray>





  
      Array  Chunk 
  
  
     Bytes  72.24 kB   72.24 kB 
     Shape  (9030,)   (9030,) 
     Count  2 Tasks  1 Chunks 
     Type  int64  numpy.ndarray

j
(j)
int64
0 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])
```
j_g
(j_g)
int64
0 1 2 3 4 ... 4316 4317 4318 4319
axis :
Y
c_grid_axis_shift :
-0.5
long_name :
y-dimension of the v grid
standard_name :
y_grid_index_at_v_location
swap_dim :
YG
```
array([   0,    1,    2, ..., 4317, 4318, 4319])
```
k
()
int64
...
axis :
Z
long_name :
z-dimension of the t grid
standard_name :
z_grid_index
swap_dim :
Z
```
array(1)
```
k_l
()
int64
...
axis :
Z
c_grid_axis_shift :
-0.5
long_name :
z-dimension of the w grid
standard_name :
z_grid_index_at_upper_w_location
swap_dim :
Zl
```
array(1)
```
k_p1
(k_p1)
int64
0 1
axis :
Z
c_grid_axis_shift :
[-0.5, 0.5]
long_name :
z-dimension of the w grid
standard_name :
z_grid_index_at_w_location
swap_dim :
Zp1
```
array([0, 1])
```
k_u
()
int64
...
axis :
Z
c_grid_axis_shift :
0.5
long_name :
z-dimension of the w grid
standard_name :
z_grid_index_at_lower_w_location
swap_dim :
Zu
```
array(1)
```

(face, j, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YC XC
long_name :: cell area
standard_name :: cell_area
units :: m2





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

rAs

(face, j_g, i)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

long_name :: cell area
standard_name :: cell_area_at_v_location
units :: m2





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

rAw

(face, j, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YG XC
long_name :: cell area
standard_name :: cell_area_at_u_location
units :: m2





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

rAz

(face, j_g, i_g)

float32

dask.array<chunksize=(1, 4320, 4320), meta=np.ndarray>

coordinate :: YG XG
long_name :: cell area
standard_name :: cell_area_at_f_location
units :: m





  
      Array  Chunk 
  
  
     Bytes  970.44 MB   74.65 MB 
     Shape  (13, 4320, 4320)   (1, 4320, 4320) 
     Count  14 Tasks  13 Chunks 
     Type  float32  numpy.ndarray

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]')

Data variables: (0)
Attributes: (4)
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=False, grid_vars_to_coords=True, geometry='llc', calendar='gregorian', ref_date=None, delta_t=1, read_grid=True, prefix=None, iters=487152, grid_dir='/data/scratch/rpa/LLC/llc_4320/run', data_dir='/data/scratch/rpa/LLC/llc_4320/run')`
source :
MITgcm
title :
netCDF wrapper of MITgcm MDS binary data