channel_ridge_05km_float_run
MITgcm output from a wind and thermally driven channel with a ridge at 5km resolution (model 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_float_run"].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
gcloudcommand 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
gcloudto 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 | 3 day average |
| duration | 5 years |
| uploader_github | cspencerjones |
| uploader_email | spencerj@ldeo.columbia.edu |
| tags | ['ocean', 'model'] |
Dataset Contents
xarray.Dataset
- XC: 400
- XG: 400
- YC: 400
- YG: 400
- Z: 40
- Zl: 40
- Zp1: 41
- Zu: 40
- time: 609
- Depth(YC, XC)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- XC YC
- long_name :
- ocean depth
- standard_name :
- ocean_depth
- units :
- m
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PHrefC(Z)float32dask.array<chunksize=(40,), meta=np.ndarray>
- long_name :
- Reference Hydrostatic Pressure
- standard_name :
- cell_reference_pressure
- units :
- m2 s-2
Array Chunk Bytes 160 B 160 B Shape (40,) (40,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PHrefF(Zp1)float32dask.array<chunksize=(41,), meta=np.ndarray>
- long_name :
- Reference Hydrostatic Pressure
- standard_name :
- cell_reference_pressure
- units :
- m2 s-2
Array Chunk Bytes 164 B 164 B Shape (41,) (41,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - XC(XC)float322500.0 7500.0 ... 1997500.0
- axis :
- X
- coordinate :
- YC XC
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([ 2500., 7500., 12500., ..., 1987500., 1992500., 1997500.], dtype=float32) - XG(XG)float320.0 5000.0 ... 1990000.0 1995000.0
- axis :
- X
- c_grid_axis_shift :
- -0.5
- coordinate :
- YG XG
- long_name :
- longitude
- standard_name :
- longitude_at_f_location
- units :
- degrees_east
array([ 0., 5000., 10000., ..., 1985000., 1990000., 1995000.], dtype=float32) - YC(YC)float322500.0 7500.0 ... 1997500.0
- axis :
- Y
- coordinate :
- YC XC
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degrees_north
array([ 2500., 7500., 12500., ..., 1987500., 1992500., 1997500.], dtype=float32) - YG(YG)float320.0 5000.0 ... 1990000.0 1995000.0
- axis :
- Y
- c_grid_axis_shift :
- -0.5
- long_name :
- latitude
- standard_name :
- latitude_at_f_location
- units :
- degrees_north
array([ 0., 5000., 10000., ..., 1985000., 1990000., 1995000.], dtype=float32) - Z(Z)float32-5.0 -15.0 ... -2830.5 -2933.5
- axis :
- Z
- long_name :
- vertical coordinate of cell center
- positive :
- down
- standard_name :
- depth
- 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) - Zl(Zl)float320.0 -10.0 -20.0 ... -2779.0 -2882.0
- 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., -10., -20., -30., -42., -56., -72., -91., -113., -139., -169., -205., -247., -297., -357., -429., -514., -616., -719., -822., -925., -1028., -1131., -1234., -1337., -1440., -1543., -1646., -1749., -1852., -1955., -2058., -2161., -2264., -2367., -2470., -2573., -2676., -2779., -2882.], dtype=float32) - Zp1(Zp1)float320.0 -10.0 -20.0 ... -2882.0 -2985.0
- 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., -10., -20., -30., -42., -56., -72., -91., -113., -139., -169., -205., -247., -297., -357., -429., -514., -616., -719., -822., -925., -1028., -1131., -1234., -1337., -1440., -1543., -1646., -1749., -1852., -1955., -2058., -2161., -2264., -2367., -2470., -2573., -2676., -2779., -2882., -2985.], dtype=float32) - Zu(Zu)float32-10.0 -20.0 ... -2882.0 -2985.0
- 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([ -10., -20., -30., -42., -56., -72., -91., -113., -139., -169., -205., -247., -297., -357., -429., -514., -616., -719., -822., -925., -1028., -1131., -1234., -1337., -1440., -1543., -1646., -1749., -1852., -1955., -2058., -2161., -2264., -2367., -2470., -2573., -2676., -2779., -2882., -2985.], dtype=float32) - drC(Zp1)float32dask.array<chunksize=(41,), meta=np.ndarray>
- long_name :
- cell z size
- standard_name :
- cell_z_size_at_w_location
- units :
- m
Array Chunk Bytes 164 B 164 B Shape (41,) (41,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - drF(Z)float32dask.array<chunksize=(40,), meta=np.ndarray>
- long_name :
- cell z size
- standard_name :
- cell_z_size
- units :
- m
Array Chunk Bytes 160 B 160 B Shape (40,) (40,) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - dxC(YC, XG)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YC XG
- long_name :
- cell x size
- standard_name :
- cell_x_size_at_u_location
- units :
- m
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - dxG(YG, XC)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YG XC
- long_name :
- cell x size
- standard_name :
- cell_x_size_at_v_location
- units :
- m
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - dyC(YG, XC)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YG XC
- long_name :
- cell y size
- standard_name :
- cell_y_size_at_v_location
- units :
- m
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - dyG(YC, XG)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YC XG
- long_name :
- cell y size
- standard_name :
- cell_y_size_at_u_location
- units :
- m
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - hFacC(Z, YC, XC)float32dask.array<chunksize=(40, 400, 400), meta=np.ndarray>
- long_name :
- vertical fraction of open cell
- standard_name :
- cell_vertical_fraction
Array Chunk Bytes 25.60 MB 25.60 MB Shape (40, 400, 400) (40, 400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - hFacS(Z, YG, XC)float32dask.array<chunksize=(40, 400, 400), meta=np.ndarray>
- long_name :
- vertical fraction of open cell
- standard_name :
- cell_vertical_fraction_at_v_location
Array Chunk Bytes 25.60 MB 25.60 MB Shape (40, 400, 400) (40, 400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - hFacW(Z, YC, XG)float32dask.array<chunksize=(40, 400, 400), meta=np.ndarray>
- long_name :
- vertical fraction of open cell
- standard_name :
- cell_vertical_fraction_at_u_location
Array Chunk Bytes 25.60 MB 25.60 MB Shape (40, 400, 400) (40, 400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - iter(time)int64dask.array<chunksize=(4,), meta=np.ndarray>
- long_name :
- model timestep number
- standard_name :
- timestep
Array Chunk Bytes 4.87 kB 32 B Shape (609,) (4,) Count 154 Tasks 153 Chunks Type int64 numpy.ndarray - maskC(Z, YC, XC)booldask.array<chunksize=(40, 400, 400), meta=np.ndarray>
- long_name :
- mask denoting wet point at center
- standard_name :
- sea_binary_mask_at_t_location
Array Chunk Bytes 6.40 MB 6.40 MB Shape (40, 400, 400) (40, 400, 400) Count 2 Tasks 1 Chunks Type bool numpy.ndarray - maskS(Z, YG, XC)booldask.array<chunksize=(40, 400, 400), meta=np.ndarray>
- long_name :
- mask denoting wet point at interface
- standard_name :
- cell_vertical_fraction_at_v_location
Array Chunk Bytes 6.40 MB 6.40 MB Shape (40, 400, 400) (40, 400, 400) Count 2 Tasks 1 Chunks Type bool numpy.ndarray - maskW(Z, YC, XG)booldask.array<chunksize=(40, 400, 400), meta=np.ndarray>
- long_name :
- mask denoting wet point at interface
- standard_name :
- cell_vertical_fraction_at_u_location
Array Chunk Bytes 6.40 MB 6.40 MB Shape (40, 400, 400) (40, 400, 400) Count 2 Tasks 1 Chunks Type bool numpy.ndarray - rA(YC, XC)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YC XC
- long_name :
- cell area
- standard_name :
- cell_area
- units :
- m2
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - rAs(YG, XC)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- long_name :
- cell area
- standard_name :
- cell_area_at_v_location
- units :
- m2
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - rAw(YC, XG)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YG XC
- long_name :
- cell area
- standard_name :
- cell_area_at_u_location
- units :
- m2
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - rAz(YG, XG)float32dask.array<chunksize=(400, 400), meta=np.ndarray>
- coordinate :
- YG XG
- long_name :
- cell area
- standard_name :
- cell_area_at_f_location
- units :
- m
Array Chunk Bytes 640.00 kB 640.00 kB Shape (400, 400) (400, 400) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - time(time)object0000-01-01 02:40:00 ... 0005-01-25 02:40:00
array([cftime.Datetime360Day(0, 1, 1, 2, 40, 0, 0), cftime.Datetime360Day(0, 1, 4, 2, 40, 0, 0), cftime.Datetime360Day(0, 1, 7, 2, 40, 0, 0), ..., cftime.Datetime360Day(5, 1, 19, 2, 40, 0, 0), cftime.Datetime360Day(5, 1, 22, 2, 40, 0, 0), cftime.Datetime360Day(5, 1, 25, 2, 40, 0, 0)], dtype=object)
- ETAN(time, YC, XC)float32dask.array<chunksize=(4, 400, 400), meta=np.ndarray>
- long_name :
- Surface Height Anomaly, mean over previous 3 days
- standard_name :
- ETAN
- units :
- m
Array Chunk Bytes 389.76 MB 2.56 MB Shape (609, 400, 400) (4, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - ETANSQ(time, YC, XC)float32dask.array<chunksize=(4, 400, 400), meta=np.ndarray>
- long_name :
- Square of Surface Height Anomaly, mean over previous 3 days
- standard_name :
- ETANSQ
- units :
- m^2
Array Chunk Bytes 389.76 MB 2.56 MB Shape (609, 400, 400) (4, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - PHIBOT(time, YC, XC)float32dask.array<chunksize=(4, 400, 400), meta=np.ndarray>
- long_name :
- Bottom Pressure Pot.(p/rho) Anomaly, mean over previous 3 days
- standard_name :
- PHIBOT
- units :
- m^2/s^2
Array Chunk Bytes 389.76 MB 2.56 MB Shape (609, 400, 400) (4, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - PHIBOTSQ(time, YC, XC)float32dask.array<chunksize=(4, 400, 400), meta=np.ndarray>
- long_name :
- Square of Bottom Pressure Pot.(p/rho) Anomaly, mean over previous 3 days
- standard_name :
- PHIBOTSQ
- units :
- m^4/s^4
Array Chunk Bytes 389.76 MB 2.56 MB Shape (609, 400, 400) (4, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - PHIHYD(time, Z, YC, XC)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Hydrostatic Pressure Pot.(p/rho) Anomaly, mean over previous 3 days
- standard_name :
- PHIHYD
- units :
- m^2/s^2
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - THETA(time, Z, YC, XC)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- temperature, mean over previous 3 days
- standard_name :
- THETA
- units :
- degC
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - THETASQ(time, Z, YC, XC)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Square of Temperature, mean over previous 3 days
- standard_name :
- THETASQ
- units :
- degC^2
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - UVEL(time, Z, YC, XG)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Zonal Component of Velocity (m/s), mean over previous 3 days
- mate :
- VVEL
- standard_name :
- UVEL
- units :
- m/s
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - UVELSQ(time, Z, YC, XG)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Square of Zonal Comp of Velocity (m^2/s^2), mean over previous 3 days
- mate :
- VVELSQ
- standard_name :
- UVELSQ
- units :
- m^2/s^2
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - UV_VEL_Z(time, Z, YG, XG)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Meridional Transport of Zonal Momentum (m^2/s^2), mean over previous 3 days
- mate :
- UV_VEL_Z
- standard_name :
- UV_VEL_Z
- units :
- m^2/s^2
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - VVEL(time, Z, YG, XC)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Meridional Component of Velocity (m/s), mean over previous 3 days
- mate :
- UVEL
- standard_name :
- VVEL
- units :
- m/s
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - VVELSQ(time, Z, YG, XC)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Square of Meridional Comp of Velocity (m^2/s^2), mean over previous 3 days
- mate :
- UVELSQ
- standard_name :
- VVELSQ
- units :
- m^2/s^2
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray - WVEL(time, Zl, YC, XC)float32dask.array<chunksize=(4, 40, 400, 400), meta=np.ndarray>
- long_name :
- Vertical Component of Velocity (r_units/s), mean over previous 3 days
- standard_name :
- WVEL
- units :
- m/s
Array Chunk Bytes 15.59 GB 102.40 MB Shape (609, 40, 400, 400) (4, 40, 400, 400) Count 154 Tasks 153 Chunks Type float32 numpy.ndarray
- 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 :
- 3 day mean data from re-entrant channel simulation