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
- 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
- CS(face, j, i)float32dask.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)float32dask.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)float32dask.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)float32dask.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 - SN(face, j, i)float32dask.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 - XC(face, j, i)float32dask.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 - XG(face, j_g, i_g)float32dask.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 - YC(face, j, i)float32dask.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 - YG(face, j_g, i_g)float32dask.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 - Z(k)float32dask.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 - Zl(k_l)float32dask.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)float32dask.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 - Zu(k_u)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)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])
- hFacC(k, face, j, i)float32dask.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)float32dask.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)float32dask.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(i)int640 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)int640 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)int64dask.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(j)int640 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)int640 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(k)int640 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)int640 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)int640 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)int640 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)int640 1 2 3 4 5 ... 217 218 219 220 221
- standard_name :
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- swap_dim :
- layer_1SLT_bounds
array([ 0, 1, 2, ..., 219, 220, 221])
- l1_c(l1_c)int640 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)int640 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)int640 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)int640 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)int640 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)int640 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)int640 1 2 3 4 5 ... 216 217 218 219 220
- standard_name :
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- swap_dim :
- layer_3RHO_center
array([ 0, 1, 2, ..., 218, 219, 220])
- l3_i(l3_i)int640 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)float32dask.array<chunksize=(222,), meta=np.ndarray>
- axis :
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- c_grid_axis_shift :
- -0.5
- long_name :
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- 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)float32dask.array<chunksize=(221,), meta=np.ndarray>
- axis :
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- long_name :
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- 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)float32dask.array<chunksize=(220,), meta=np.ndarray>
- axis :
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- c_grid_axis_shift :
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- long_name :
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- standard_name :
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Array Chunk Bytes 880 B 880 B Shape (220,) (220,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - layer_2TH_bounds(l2_b)float32dask.array<chunksize=(222,), meta=np.ndarray>
- axis :
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- c_grid_axis_shift :
- -0.5
- long_name :
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- standard_name :
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Array Chunk Bytes 888 B 888 B Shape (222,) (222,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - layer_2TH_center(l2_c)float32dask.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)float32dask.array<chunksize=(220,), meta=np.ndarray>
- axis :
- 2TH
- c_grid_axis_shift :
- -0.5
- long_name :
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- 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)float32dask.array<chunksize=(222,), meta=np.ndarray>
- axis :
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- c_grid_axis_shift :
- -0.5
- long_name :
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- 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)float32dask.array<chunksize=(221,), meta=np.ndarray>
- axis :
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- long_name :
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- 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)float32dask.array<chunksize=(220,), meta=np.ndarray>
- axis :
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- c_grid_axis_shift :
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- long_name :
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- 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)booldask.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)booldask.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)booldask.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 - rA(face, j, i)float32dask.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)float32dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
- long_name :
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- 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)float32dask.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)float32dask.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]')
- LaAx3RHO(time, l3_c, face, j, i_g)float64dask.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)float64dask.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)float64dask.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)float64dask.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)float64dask.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)float64dask.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)float64dask.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)float64dask.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