ECCO_layers

Rerun of Estimating the Circulation and Climate of the Ocean (ECCO) with the layers package

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean.yaml")
ds  = cat["ECCO_layers"].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://ecco-v4-python-tutorial.readthedocs.io/intro.html
tags	['ocean', 'model', 'layers']

Dataset Contents

xarray.Dataset

Dimensions:
- face: 13
- i: 90
- i_g: 90
- j: 90
- j_g: 90
- k: 50
- k_l: 50
- k_p1: 51
- k_u: 50
- l1_b: 222
- l1_c: 221
- l1_i: 220
- l2_b: 222
- l2_c: 221
- l2_i: 220
- l3_b: 222
- l3_c: 221
- l3_i: 220
- time: 288

Coordinates: (58)

(face, j, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

Depth

(face, j, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefC

(k)

float32

dask.array<chunksize=(50,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  200 B   200 B 
     Shape  (50,)   (50,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefF

(k_p1)

float32

dask.array<chunksize=(51,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  204 B   204 B 
     Shape  (51,)   (51,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(face, j_g, i_g)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(face, j_g, i_g)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(k)

float32

dask.array<chunksize=(50,), meta=np.ndarray>

long_name :: vertical coordinate of cell center
positive :: down
standard_name :: depth
units :: m





  
      Array  Chunk 
  
  
     Bytes  200 B   200 B 
     Shape  (50,)   (50,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(k_l)

float32

dask.array<chunksize=(50,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  200 B   200 B 
     Shape  (50,)   (50,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

Zp1

(k_p1)

float32

dask.array<chunksize=(51,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  204 B   204 B 
     Shape  (51,)   (51,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(k_u)

float32

dask.array<chunksize=(50,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  200 B   200 B 
     Shape  (50,)   (50,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

drC

(k_p1)

float32

dask.array<chunksize=(51,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  204 B   204 B 
     Shape  (51,)   (51,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

drF

(k)

float32

dask.array<chunksize=(50,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  200 B   200 B 
     Shape  (50,)   (50,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxC

(face, j, i_g)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxG

(face, j_g, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyC

(face, j_g, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyG

(face, j, i_g)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 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

(k, face, j, i)

float32

dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  21.06 MB   21.06 MB 
     Shape  (50, 13, 90, 90)   (50, 13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacS

(k, face, j_g, i)

float32

dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  21.06 MB   21.06 MB 
     Shape  (50, 13, 90, 90)   (50, 13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacW

(k, face, j, i_g)

float32

dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  21.06 MB   21.06 MB 
     Shape  (50, 13, 90, 90)   (50, 13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

(i)

int64

0 1 2 3 4 5 6 ... 84 85 86 87 88 89

axis :: X
long_name :: x-dimension of the t grid
standard_name :: x_grid_index
swap_dim :: XC

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])

i_g

(i_g)

int64

0 1 2 3 4 5 6 ... 84 85 86 87 88 89

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,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])

iter

(time)

int64

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

long_name :: model timestep number
standard_name :: timestep





  
      Array  Chunk 
  
  
     Bytes  2.30 kB   8 B 
     Shape  (288,)   (1,) 
     Count  289 Tasks  288 Chunks 
     Type  int64  numpy.ndarray

(j)

int64

0 1 2 3 4 5 6 ... 84 85 86 87 88 89

axis :: Y
long_name :: y-dimension of the t grid
standard_name :: y_grid_index
swap_dim :: YC

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])

j_g

(j_g)

int64

0 1 2 3 4 5 6 ... 84 85 86 87 88 89

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,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])

(k)

int64

0 1 2 3 4 5 6 ... 44 45 46 47 48 49

axis :: Z
long_name :: z-dimension of the t grid
standard_name :: z_grid_index
swap_dim :: Z

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

k_l

(k_l)

int64

0 1 2 3 4 5 6 ... 44 45 46 47 48 49

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([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

k_p1

(k_p1)

int64

0 1 2 3 4 5 6 ... 45 46 47 48 49 50

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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])

k_u

(k_u)

int64

0 1 2 3 4 5 6 ... 44 45 46 47 48 49

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([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

l1_b
(l1_b)
int64
0 1 2 3 4 5 ... 217 218 219 220 221
standard_name :
1SLT_layer_grid_index_at_layer_bounds
swap_dim :
layer_1SLT_bounds
```
array([  0,   1,   2, ..., 219, 220, 221])
```
l1_c
(l1_c)
int64
0 1 2 3 4 5 ... 216 217 218 219 220
standard_name :
1SLT_layer_grid_index_at_layer_center
swap_dim :
layer_1SLT_center
```
array([  0,   1,   2, ..., 218, 219, 220])
```
l1_i
(l1_i)
int64
0 1 2 3 4 5 ... 215 216 217 218 219
standard_name :
1SLT_layer_grid_index_at_layer_interface
swap_dim :
layer_1SLT_interface
```
array([  0,   1,   2, ..., 217, 218, 219])
```
l2_b
(l2_b)
int64
0 1 2 3 4 5 ... 217 218 219 220 221
standard_name :
2TH_layer_grid_index_at_layer_bounds
swap_dim :
layer_2TH_bounds
```
array([  0,   1,   2, ..., 219, 220, 221])
```
l2_c
(l2_c)
int64
0 1 2 3 4 5 ... 216 217 218 219 220
standard_name :
2TH_layer_grid_index_at_layer_center
swap_dim :
layer_2TH_center
```
array([  0,   1,   2, ..., 218, 219, 220])
```
l2_i
(l2_i)
int64
0 1 2 3 4 5 ... 215 216 217 218 219
standard_name :
2TH_layer_grid_index_at_layer_interface
swap_dim :
layer_2TH_interface
```
array([  0,   1,   2, ..., 217, 218, 219])
```
l3_b
(l3_b)
int64
0 1 2 3 4 5 ... 217 218 219 220 221
standard_name :
3RHO_layer_grid_index_at_layer_bounds
swap_dim :
layer_3RHO_bounds
```
array([  0,   1,   2, ..., 219, 220, 221])
```
l3_c
(l3_c)
int64
0 1 2 3 4 5 ... 216 217 218 219 220
standard_name :
3RHO_layer_grid_index_at_layer_center
swap_dim :
layer_3RHO_center
```
array([  0,   1,   2, ..., 218, 219, 220])
```
l3_i
(l3_i)
int64
0 1 2 3 4 5 ... 215 216 217 218 219
standard_name :
3RHO_layer_grid_index_at_layer_interface
swap_dim :
layer_3RHO_interface
```
array([  0,   1,   2, ..., 217, 218, 219])
```

layer_1SLT_bounds

(l1_b)

float32

dask.array<chunksize=(222,), meta=np.ndarray>

axis :: 1SLT
c_grid_axis_shift :: -0.5
long_name :: boundaries points of layer 1SLT
standard_name :: ocean_layer_coordinate_1SLT_bounds





  
      Array  Chunk 
  
  
     Bytes  888 B   888 B 
     Shape  (222,)   (222,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_1SLT_center

(l1_c)

float32

dask.array<chunksize=(221,), meta=np.ndarray>

axis :: 1SLT
long_name :: center points of layer 1SLT
standard_name :: ocean_layer_coordinate_1SLT_center





  
      Array  Chunk 
  
  
     Bytes  884 B   884 B 
     Shape  (221,)   (221,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_1SLT_interface

(l1_i)

float32

dask.array<chunksize=(220,), meta=np.ndarray>

axis :: 1SLT
c_grid_axis_shift :: -0.5
long_name :: interface points of layer 1SLT
standard_name :: ocean_layer_coordinate_1SLT_interface





  
      Array  Chunk 
  
  
     Bytes  880 B   880 B 
     Shape  (220,)   (220,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_2TH_bounds

(l2_b)

float32

dask.array<chunksize=(222,), meta=np.ndarray>

axis :: 2TH
c_grid_axis_shift :: -0.5
long_name :: boundaries points of layer 2TH
standard_name :: ocean_layer_coordinate_2TH_bounds





  
      Array  Chunk 
  
  
     Bytes  888 B   888 B 
     Shape  (222,)   (222,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_2TH_center

(l2_c)

float32

dask.array<chunksize=(221,), meta=np.ndarray>

axis :: 2TH
long_name :: center points of layer 2TH
standard_name :: ocean_layer_coordinate_2TH_center





  
      Array  Chunk 
  
  
     Bytes  884 B   884 B 
     Shape  (221,)   (221,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_2TH_interface

(l2_i)

float32

dask.array<chunksize=(220,), meta=np.ndarray>

axis :: 2TH
c_grid_axis_shift :: -0.5
long_name :: interface points of layer 2TH
standard_name :: ocean_layer_coordinate_2TH_interface





  
      Array  Chunk 
  
  
     Bytes  880 B   880 B 
     Shape  (220,)   (220,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_3RHO_bounds

(l3_b)

float32

dask.array<chunksize=(222,), meta=np.ndarray>

axis :: 3RHO
c_grid_axis_shift :: -0.5
long_name :: boundaries points of layer 3RHO
standard_name :: ocean_layer_coordinate_3RHO_bounds





  
      Array  Chunk 
  
  
     Bytes  888 B   888 B 
     Shape  (222,)   (222,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_3RHO_center

(l3_c)

float32

dask.array<chunksize=(221,), meta=np.ndarray>

axis :: 3RHO
long_name :: center points of layer 3RHO
standard_name :: ocean_layer_coordinate_3RHO_center





  
      Array  Chunk 
  
  
     Bytes  884 B   884 B 
     Shape  (221,)   (221,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

layer_3RHO_interface

(l3_i)

float32

dask.array<chunksize=(220,), meta=np.ndarray>

axis :: 3RHO
c_grid_axis_shift :: -0.5
long_name :: interface points of layer 3RHO
standard_name :: ocean_layer_coordinate_3RHO_interface





  
      Array  Chunk 
  
  
     Bytes  880 B   880 B 
     Shape  (220,)   (220,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

maskC

(k, face, j, i)

bool

dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>

long_name :: mask denoting wet point at center
standard_name :: sea_binary_mask_at_t_location





  
      Array  Chunk 
  
  
     Bytes  5.26 MB   5.26 MB 
     Shape  (50, 13, 90, 90)   (50, 13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  bool  numpy.ndarray

maskS

(k, face, j_g, i)

bool

dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>

long_name :: mask denoting wet point at interface
standard_name :: cell_vertical_fraction_at_v_location





  
      Array  Chunk 
  
  
     Bytes  5.26 MB   5.26 MB 
     Shape  (50, 13, 90, 90)   (50, 13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  bool  numpy.ndarray

maskW

(k, face, j, i_g)

bool

dask.array<chunksize=(50, 13, 90, 90), meta=np.ndarray>

long_name :: mask denoting wet point at interface
standard_name :: cell_vertical_fraction_at_u_location





  
      Array  Chunk 
  
  
     Bytes  5.26 MB   5.26 MB 
     Shape  (50, 13, 90, 90)   (50, 13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  bool  numpy.ndarray

(face, j, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAs

(face, j_g, i)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAw

(face, j, i_g)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAz

(face, j_g, i_g)

float32

dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  421.20 kB   421.20 kB 
     Shape  (13, 90, 90)   (13, 90, 90) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time

(time)

datetime64[ns]

1992-01-15 ... 2015-12-14

axis :: T
long_name :: Time
standard_name :: time

array(['1992-01-15T00:00:00.000000000', '1992-02-13T00:00:00.000000000',
       '1992-03-15T00:00:00.000000000', ..., '2015-10-15T00:00:00.000000000',
       '2015-11-14T00:00:00.000000000', '2015-12-14T00:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (8)

LaAx3RHO

(time, l3_c, face, j, i_g)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Advection of THETA on layers inx
mate :: LaAy3RHO
standard_name :: LaAx3RHO
units :: m^3 deg./s





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaAy3RHO

(time, l3_c, face, j_g, i)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Advection of THETA on layers iny
mate :: LaAx3RHO
standard_name :: LaAy3RHO
units :: m^3 deg./s





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaFu3RHO

(time, l3_c, face, j, i_g)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Layer Integrated Temp (tH, m deg.)
mate :: LaFv3RHO
standard_name :: LaFu3RHO
units :: m.deg





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaFv3RHO

(time, l3_c, face, j_g, i)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Layer Integrated Temp (tH, m deg.)
mate :: LaFu3RHO
standard_name :: LaFv3RHO
units :: m.deg





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaHs3RHO

(time, l3_c, face, j_g, i)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Layer Thickness at V points (m)
mate :: LaHw3RHO
standard_name :: LaHs3RHO
units :: m





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaHw3RHO

(time, l3_c, face, j, i_g)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Layer Thickness at U points (m)
mate :: LaHs3RHO
standard_name :: LaHw3RHO
units :: m





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaUH3RHO

(time, l3_c, face, j, i_g)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Layer Integrated zonal Transport (UH, m^2/s)
mate :: LaVH3RHO
standard_name :: LaUH3RHO
units :: m.m/s





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

LaVH3RHO

(time, l3_c, face, j_g, i)

float64

dask.array<chunksize=(1, 221, 13, 90, 90), meta=np.ndarray>

long_name :: Layer Integrated merid. Transport (VH, m^2/s)
mate :: LaUH3RHO
standard_name :: LaVH3RHO
units :: m.m/s





  
      Array  Chunk 
  
  
     Bytes  53.62 GB   186.17 MB 
     Shape  (288, 221, 13, 90, 90)   (1, 221, 13, 90, 90) 
     Count  289 Tasks  288 Chunks 
     Type  float64  numpy.ndarray

Attributes: (0)