channel_ridge_resolutions_01km

MITgcm output from a wind and thermally driven channel with a ridge at 1km resolution, and surface forced tracer

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_resolutions_01km"].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

publication	Submesoscale Vertical Velocities Enhance Tracer Subduction in an Idealized Antarctic Circumpolar Current, Balwada et al 2018 (GRL)
time_resolution	10 day snapshots
duration	1 year
uploader_github	charlesbluca
uploader_email	charles@ldeo.columbia.edu
tags	['ocean', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- XC: 2000
- XG: 2000
- YC: 2000
- YG: 2000
- Z: 76
- Zl: 76
- Zp1: 77
- Zu: 76
- layer_1TH_bounds: 43
- layer_1TH_center: 42
- layer_1TH_interface: 41
- time: 35

Coordinates: (32)

Depth

(YC, XC)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

PHrefC

(Z)

float32

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

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





  
      Array  Chunk 
  
  
     Bytes  304 B   4 B 
     Shape  (76,)   (1,) 
     Count  77 Tasks  76 Chunks 
     Type  float32  numpy.ndarray

PHrefF

(Zp1)

float32

dask.array<chunksize=(77,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  308 B   308 B 
     Shape  (77,)   (77,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

XC
(XC)
float32
500.0 1500.0 ... 1999500.0
axis :
X
coordinate :
YC XC
long_name :
x coordinate
standard_name :
plane_x_coordinate
units :
m
```
array([5.0000e+02, 1.5000e+03, 2.5000e+03, ..., 1.9975e+06, 1.9985e+06,
       1.9995e+06], dtype=float32)
```
XG
(XG)
float32
0.0 1000.0 ... 1998000.0 1999000.0
axis :
X
c_grid_axis_shift :
-0.5
coordinate :
YG XG
long_name :
x coordinate
standard_name :
plane_x_coordinate_at_f_location
units :
m
```
array([0.000e+00, 1.000e+03, 2.000e+03, ..., 1.997e+06, 1.998e+06, 1.999e+06],
      dtype=float32)
```
YC
(YC)
float32
500.0 1500.0 ... 1999500.0
axis :
Y
coordinate :
YC XC
long_name :
y coordinate
standard_name :
plane_y_coordinate
units :
m
```
array([5.0000e+02, 1.5000e+03, 2.5000e+03, ..., 1.9975e+06, 1.9985e+06,
       1.9995e+06], dtype=float32)
```
YG
(YG)
float32
0.0 1000.0 ... 1998000.0 1999000.0
axis :
Y
c_grid_axis_shift :
-0.5
long_name :
y coordinate
standard_name :
plane_y_coordinate_at_f_location
units :
m
```
array([0.000e+00, 1.000e+03, 2.000e+03, ..., 1.997e+06, 1.998e+06, 1.999e+06],
      dtype=float32)
```

(Z)

float32

-0.5 -1.57 ... -2757.325 -2912.665

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

array([-5.000000e-01, -1.570000e+00, -2.790000e+00, -4.185000e+00,
       -5.780000e+00, -7.595000e+00, -9.660000e+00, -1.201000e+01,
       -1.468000e+01, -1.770500e+01, -2.112500e+01, -2.499000e+01,
       -2.934500e+01, -3.424000e+01, -3.972500e+01, -4.585500e+01,
       -5.269000e+01, -6.028000e+01, -6.868500e+01, -7.796500e+01,
       -8.817500e+01, -9.937000e+01, -1.116000e+02, -1.249150e+02,
       -1.393650e+02, -1.549900e+02, -1.718250e+02, -1.899000e+02,
       -2.092350e+02, -2.298550e+02, -2.517700e+02, -2.749850e+02,
       -2.995050e+02, -3.253200e+02, -3.524200e+02, -3.807900e+02,
       -4.104100e+02, -4.412550e+02, -4.733050e+02, -5.065400e+02,
       -5.409350e+02, -5.764650e+02, -6.131100e+02, -6.508550e+02,
       -6.896850e+02, -7.295950e+02, -7.705850e+02, -8.126600e+02,
       -8.558350e+02, -9.001350e+02, -9.455950e+02, -9.922600e+02,
       -1.040180e+03, -1.089425e+03, -1.140080e+03, -1.192235e+03,
       -1.246005e+03, -1.301520e+03, -1.358920e+03, -1.418375e+03,
       -1.480075e+03, -1.544225e+03, -1.611060e+03, -1.680845e+03,
       -1.753875e+03, -1.830475e+03, -1.911015e+03, -1.995905e+03,
       -2.085595e+03, -2.180595e+03, -2.281470e+03, -2.388845e+03,
       -2.503415e+03, -2.625955e+03, -2.757325e+03, -2.912665e+03],
      dtype=float32)

(Zl)

float32

0.0 -1.0 ... -2689.32 -2825.33

axis :: Z
c_grid_axis_shift :: -0.5
long_name :: vertical coordinate of upper cell interface
positive :: down
standard_name :: depth_at_upper_w_location
units :: m

array([ 0.00000e+00, -1.00000e+00, -2.14000e+00, -3.44000e+00, -4.93000e+00,
       -6.63000e+00, -8.56000e+00, -1.07600e+01, -1.32600e+01, -1.61000e+01,
       -1.93100e+01, -2.29400e+01, -2.70400e+01, -3.16500e+01, -3.68300e+01,
       -4.26200e+01, -4.90900e+01, -5.62900e+01, -6.42700e+01, -7.31000e+01,
       -8.28300e+01, -9.35200e+01, -1.05220e+02, -1.17980e+02, -1.31850e+02,
       -1.46880e+02, -1.63100e+02, -1.80550e+02, -1.99250e+02, -2.19220e+02,
       -2.40490e+02, -2.63050e+02, -2.86920e+02, -3.12090e+02, -3.38550e+02,
       -3.66290e+02, -3.95290e+02, -4.25530e+02, -4.56980e+02, -4.89630e+02,
       -5.23450e+02, -5.58420e+02, -5.94510e+02, -6.31710e+02, -6.70000e+02,
       -7.09370e+02, -7.49820e+02, -7.91350e+02, -8.33970e+02, -8.77700e+02,
       -9.22570e+02, -9.68620e+02, -1.01590e+03, -1.06446e+03, -1.11439e+03,
       -1.16577e+03, -1.21870e+03, -1.27331e+03, -1.32973e+03, -1.38811e+03,
       -1.44864e+03, -1.51151e+03, -1.57694e+03, -1.64518e+03, -1.71651e+03,
       -1.79124e+03, -1.86971e+03, -1.95232e+03, -2.03949e+03, -2.13170e+03,
       -2.22949e+03, -2.33345e+03, -2.44424e+03, -2.56259e+03, -2.68932e+03,
       -2.82533e+03], dtype=float32)

Zp1

(Zp1)

float32

0.0 -1.0 -2.14 ... -2825.33 -3000.0

axis :: Z
c_grid_axis_shift :: [-0.5, 0.5]
long_name :: vertical coordinate of cell interface
positive :: down
standard_name :: depth_at_w_location
units :: m

array([ 0.00000e+00, -1.00000e+00, -2.14000e+00, -3.44000e+00, -4.93000e+00,
       -6.63000e+00, -8.56000e+00, -1.07600e+01, -1.32600e+01, -1.61000e+01,
       -1.93100e+01, -2.29400e+01, -2.70400e+01, -3.16500e+01, -3.68300e+01,
       -4.26200e+01, -4.90900e+01, -5.62900e+01, -6.42700e+01, -7.31000e+01,
       -8.28300e+01, -9.35200e+01, -1.05220e+02, -1.17980e+02, -1.31850e+02,
       -1.46880e+02, -1.63100e+02, -1.80550e+02, -1.99250e+02, -2.19220e+02,
       -2.40490e+02, -2.63050e+02, -2.86920e+02, -3.12090e+02, -3.38550e+02,
       -3.66290e+02, -3.95290e+02, -4.25530e+02, -4.56980e+02, -4.89630e+02,
       -5.23450e+02, -5.58420e+02, -5.94510e+02, -6.31710e+02, -6.70000e+02,
       -7.09370e+02, -7.49820e+02, -7.91350e+02, -8.33970e+02, -8.77700e+02,
       -9.22570e+02, -9.68620e+02, -1.01590e+03, -1.06446e+03, -1.11439e+03,
       -1.16577e+03, -1.21870e+03, -1.27331e+03, -1.32973e+03, -1.38811e+03,
       -1.44864e+03, -1.51151e+03, -1.57694e+03, -1.64518e+03, -1.71651e+03,
       -1.79124e+03, -1.86971e+03, -1.95232e+03, -2.03949e+03, -2.13170e+03,
       -2.22949e+03, -2.33345e+03, -2.44424e+03, -2.56259e+03, -2.68932e+03,
       -2.82533e+03, -3.00000e+03], dtype=float32)

(Zu)

float32

-1.0 -2.14 ... -2825.33 -3000.0

axis :: Z
c_grid_axis_shift :: 0.5
long_name :: vertical coordinate of lower cell interface
positive :: down
standard_name :: depth_at_lower_w_location
units :: m

array([-1.00000e+00, -2.14000e+00, -3.44000e+00, -4.93000e+00, -6.63000e+00,
       -8.56000e+00, -1.07600e+01, -1.32600e+01, -1.61000e+01, -1.93100e+01,
       -2.29400e+01, -2.70400e+01, -3.16500e+01, -3.68300e+01, -4.26200e+01,
       -4.90900e+01, -5.62900e+01, -6.42700e+01, -7.31000e+01, -8.28300e+01,
       -9.35200e+01, -1.05220e+02, -1.17980e+02, -1.31850e+02, -1.46880e+02,
       -1.63100e+02, -1.80550e+02, -1.99250e+02, -2.19220e+02, -2.40490e+02,
       -2.63050e+02, -2.86920e+02, -3.12090e+02, -3.38550e+02, -3.66290e+02,
       -3.95290e+02, -4.25530e+02, -4.56980e+02, -4.89630e+02, -5.23450e+02,
       -5.58420e+02, -5.94510e+02, -6.31710e+02, -6.70000e+02, -7.09370e+02,
       -7.49820e+02, -7.91350e+02, -8.33970e+02, -8.77700e+02, -9.22570e+02,
       -9.68620e+02, -1.01590e+03, -1.06446e+03, -1.11439e+03, -1.16577e+03,
       -1.21870e+03, -1.27331e+03, -1.32973e+03, -1.38811e+03, -1.44864e+03,
       -1.51151e+03, -1.57694e+03, -1.64518e+03, -1.71651e+03, -1.79124e+03,
       -1.86971e+03, -1.95232e+03, -2.03949e+03, -2.13170e+03, -2.22949e+03,
       -2.33345e+03, -2.44424e+03, -2.56259e+03, -2.68932e+03, -2.82533e+03,
       -3.00000e+03], dtype=float32)

drC

(Zp1)

float32

dask.array<chunksize=(77,), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  308 B   308 B 
     Shape  (77,)   (77,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

drF

(Z)

float32

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

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





  
      Array  Chunk 
  
  
     Bytes  304 B   4 B 
     Shape  (76,)   (1,) 
     Count  77 Tasks  76 Chunks 
     Type  float32  numpy.ndarray

dxC

(YC, XG)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dxG

(YG, XC)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyC

(YG, XC)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

dyG

(YC, XG)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

hFacC

(Z, YC, XC)

float32

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  1.22 GB   16.00 MB 
     Shape  (76, 2000, 2000)   (1, 2000, 2000) 
     Count  77 Tasks  76 Chunks 
     Type  float32  numpy.ndarray

hFacS

(Z, YG, XC)

float32

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  1.22 GB   16.00 MB 
     Shape  (76, 2000, 2000)   (1, 2000, 2000) 
     Count  77 Tasks  76 Chunks 
     Type  float32  numpy.ndarray

hFacW

(Z, YC, XG)

float32

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  1.22 GB   16.00 MB 
     Shape  (76, 2000, 2000)   (1, 2000, 2000) 
     Count  77 Tasks  76 Chunks 
     Type  float32  numpy.ndarray

iter

(time)

int64

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

long_name :: model timestep number
standard_name :: timestep





  
      Array  Chunk 
  
  
     Bytes  280 B   8 B 
     Shape  (35,)   (1,) 
     Count  36 Tasks  35 Chunks 
     Type  int64  numpy.ndarray

layer_1TH_bounds

(layer_1TH_bounds)

float32

-0.2 0.0 0.2 0.4 ... 7.8 8.0 8.2

axis :: 1TH
c_grid_axis_shift :: -0.5
long_name :: boundaries points of layer 1TH
standard_name :: ocean_layer_coordinate_1TH_bounds

array([-0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ,
        2.2,  2.4,  2.6,  2.8,  3. ,  3.2,  3.4,  3.6,  3.8,  4. ,  4.2,  4.4,
        4.6,  4.8,  5. ,  5.2,  5.4,  5.6,  5.8,  6. ,  6.2,  6.4,  6.6,  6.8,
        7. ,  7.2,  7.4,  7.6,  7.8,  8. ,  8.2], dtype=float32)

layer_1TH_center

(layer_1TH_center)

float32

-0.1 0.1 0.3 0.5 ... 7.7 7.9 8.1

axis :: 1TH
long_name :: center points of layer 1TH
standard_name :: ocean_layer_coordinate_1TH_center

array([-0.1,  0.1,  0.3,  0.5,  0.7,  0.9,  1.1,  1.3,  1.5,  1.7,  1.9,  2.1,
        2.3,  2.5,  2.7,  2.9,  3.1,  3.3,  3.5,  3.7,  3.9,  4.1,  4.3,  4.5,
        4.7,  4.9,  5.1,  5.3,  5.5,  5.7,  5.9,  6.1,  6.3,  6.5,  6.7,  6.9,
        7.1,  7.3,  7.5,  7.7,  7.9,  8.1], dtype=float32)

layer_1TH_interface

(layer_1TH_interface)

float32

0.0 0.2 0.4 0.6 ... 7.4 7.6 7.8 8.0

axis :: 1TH
c_grid_axis_shift :: -0.5
long_name :: interface points of layer 1TH
standard_name :: ocean_layer_coordinate_1TH_interface

array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6,
       2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. , 5.2, 5.4,
       5.6, 5.8, 6. , 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8, 8. ],
      dtype=float32)

maskC

(Z, YC, XC)

bool

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  304.00 MB   4.00 MB 
     Shape  (76, 2000, 2000)   (1, 2000, 2000) 
     Count  77 Tasks  76 Chunks 
     Type  bool  numpy.ndarray

maskS

(Z, YG, XC)

bool

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  304.00 MB   4.00 MB 
     Shape  (76, 2000, 2000)   (1, 2000, 2000) 
     Count  77 Tasks  76 Chunks 
     Type  bool  numpy.ndarray

maskW

(Z, YC, XG)

bool

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  304.00 MB   4.00 MB 
     Shape  (76, 2000, 2000)   (1, 2000, 2000) 
     Count  77 Tasks  76 Chunks 
     Type  bool  numpy.ndarray

(YC, XC)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAs

(YG, XC)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAw

(YC, XG)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

rAz

(YG, XG)

float32

dask.array<chunksize=(2000, 2000), meta=np.ndarray>

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





  
      Array  Chunk 
  
  
     Bytes  16.00 MB   16.00 MB 
     Shape  (2000, 2000)   (2000, 2000) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time

(time)

timedelta64[ns]

550 days 560 days ... 890 days

axis :: T
calendar :: gregorian
long_name :: Time
standard_name :: time

array([47520000000000000, 48384000000000000, 49248000000000000,
       50112000000000000, 50976000000000000, 51840000000000000,
       52704000000000000, 53568000000000000, 54432000000000000,
       55296000000000000, 56160000000000000, 57024000000000000,
       57888000000000000, 58752000000000000, 59616000000000000,
       60480000000000000, 61344000000000000, 62208000000000000,
       63072000000000000, 63936000000000000, 64800000000000000,
       65664000000000000, 66528000000000000, 67392000000000000,
       68256000000000000, 69120000000000000, 69984000000000000,
       70848000000000000, 71712000000000000, 72576000000000000,
       73440000000000000, 74304000000000000, 75168000000000000,
       76032000000000000, 76896000000000000], dtype='timedelta64[ns]')

Data variables: (7)

Eta

(time, YC, XC)

float32

dask.array<chunksize=(1, 2000, 2000), meta=np.ndarray>

long_name :: Surface Height Anomaly
standard_name :: ETAN
units :: m





  
      Array  Chunk 
  
  
     Bytes  560.00 MB   16.00 MB 
     Shape  (35, 2000, 2000)   (1, 2000, 2000) 
     Count  36 Tasks  35 Chunks 
     Type  float32  numpy.ndarray

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 1, 2000, 2000), meta=np.ndarray>

long_name :: Hydrostatic Pressure Pot.(p/rho) Anomaly
standard_name :: sea_water_dynamic_pressue
units :: m2 s-2





  
      Array  Chunk 
  
  
     Bytes  42.56 GB   16.00 MB 
     Shape  (35, 76, 2000, 2000)   (1, 1, 2000, 2000) 
     Count  2661 Tasks  2660 Chunks 
     Type  float32  numpy.ndarray

PTRACER01

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 1, 2000, 2000), meta=np.ndarray>

long_name :: Concentration of PTRACER01
standard_name :: PTRACER01_concentration
units :: kg m-3





  
      Array  Chunk 
  
  
     Bytes  42.56 GB   16.00 MB 
     Shape  (35, 76, 2000, 2000)   (1, 1, 2000, 2000) 
     Count  2661 Tasks  2660 Chunks 
     Type  float32  numpy.ndarray

(time, Z, YC, XC)

float32

dask.array<chunksize=(1, 1, 2000, 2000), meta=np.ndarray>

long_name :: Potential Temperature
standard_name :: sea_water_potential_temperature
units :: degree_Celcius





  
      Array  Chunk 
  
  
     Bytes  42.56 GB   16.00 MB 
     Shape  (35, 76, 2000, 2000)   (1, 1, 2000, 2000) 
     Count  2661 Tasks  2660 Chunks 
     Type  float32  numpy.ndarray

(time, Z, YC, XG)

float32

dask.array<chunksize=(1, 1, 2000, 2000), meta=np.ndarray>

long_name :: Zonal Component of Velocity
mate :: V
standard_name :: sea_water_x_velocity
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  42.56 GB   16.00 MB 
     Shape  (35, 76, 2000, 2000)   (1, 1, 2000, 2000) 
     Count  2661 Tasks  2660 Chunks 
     Type  float32  numpy.ndarray

(time, Z, YG, XC)

float32

dask.array<chunksize=(1, 1, 2000, 2000), meta=np.ndarray>

long_name :: Meridional Component of Velocity
mate :: U
standard_name :: sea_water_y_velocity
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  42.56 GB   16.00 MB 
     Shape  (35, 76, 2000, 2000)   (1, 1, 2000, 2000) 
     Count  2661 Tasks  2660 Chunks 
     Type  float32  numpy.ndarray

(time, Zl, YC, XC)

float32

dask.array<chunksize=(1, 1, 2000, 2000), meta=np.ndarray>

long_name :: Vertical Component of Velocity
standard_name :: sea_water_z_velocity
units :: m s-1





  
      Array  Chunk 
  
  
     Bytes  42.56 GB   16.00 MB 
     Shape  (35, 76, 2000, 2000)   (1, 1, 2000, 2000) 
     Count  2661 Tasks  2660 Chunks 
     Type  float32  numpy.ndarray

Attributes: (0)