Modes of ecco_access

Andrew Delman, 2024-10-10

Introduction

Query-only modes

• ls/query mode

• s3_ls/s3_query mode

Direct download modes

• download mode

• download_ifspace mode

• download_subset mode

In-cloud only access modes

• s3_open mode

• s3_open_fsspec mode

• s3_get mode

• s3_get_ifspace mode

Time comparison of access modes

Introduction

In the previous tutorial the ecco_access library was introduced, with a few examples of how it can be used to search and access ECCOv4 output available from PO.DAAC. This tutorial summarizes and compares the various modes that ecco_access supports for HTTPS and (in-cloud) S3 data access. We’ll use each mode to search/download/retrieve native grid monthly SSH and wind stress datasets for 6 months (Jan-Jun 2010).

As the time comparison at the end will show, mode = s3_open_fsspec is typically fastest when working in the cloud and you have the necessary json files available. Hence the other tutorials in the ecco-2024 repository generally use this access mode.

import numpy as np
import xarray as xr
from os.path import join,expanduser

import ecco_access as ea

SSH_shortname = 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4'
windstr_shortname = 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4'

Query-only modes

These modes return the URLs (ls/query) or S3 file paths (s3_ls/s3_query) to access the ECCO output. These modes only work with ecco_podaac_access (not ecco_podaac_to_xrdataset), since we are not opening a dataset, just querying the location of the data.

Note

The ls and query modes are interchangeable and have the same functionality, just by different names. The same is true for s3_ls and s3_query.

ls/query mode

urls_dict = ea.ecco_podaac_access([SSH_shortname,windstr_shortname],\
                                    StartDate='2010-01',EndDate='2010-06',\
                                    mode='ls')

urls_dict[SSH_shortname]

['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc']

urls_dict[windstr_shortname]

['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc']

s3_ls/s3_query mode

You can use the s3_ls/s3_query mode to find the S3 bucket file paths for AWS in-cloud access:

s3_paths_dict = ea.ecco_podaac_access([SSH_shortname,windstr_shortname],\
                                        StartDate='2010-01',EndDate='2010-06',\
                                        mode='s3_query')

{'ShortName': 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}
{'ShortName': 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

s3_paths_dict

{'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4': ['s3://podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc'],
 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4': ['s3://podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
  's3://podaac-ops-cumulus-protected/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc']}

The dictionary returned has the locations of each of the queried files on S3. Note that to read the data, the files need to be opened, e.g. using s3fs and NASA Earthdata authentication.

# log in to NASA Earthdata
# (will prompt for credentials if they are not already in ~/.netrc)
ea.setup_earthdata_login_auth()

import requests
import s3fs

# obtain NASA Earthdata credentials for in-cloud S3 access
creds = requests.get('https://archive.podaac.earthdata.nasa.gov/s3credentials').json()
s3 = s3fs.S3FileSystem(anon=False,
                       key=creds['accessKeyId'],
                       secret=creds['secretAccessKey'],
                       token=creds['sessionToken'])

# use list comprehension to open files 
# and create list that can be passed to xarray file opener
open_SSH_files = [s3.open(file) for file in s3_paths_dict[SSH_shortname]]

# open xarray dataset
ds_SSH_curr = xr.open_mfdataset(open_SSH_files,\
                                compat='override',data_vars='minimal',coords='minimal',\
                                parallel=True)

ds_SSH_curr

<xarray.Dataset> Size: 15MB
Dimensions:    (time: 6, tile: 13, j: 90, i: 90, i_g: 90, j_g: 90, nv: 2, nb: 4)
Coordinates: (12/13)
  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89
  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89
  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89
  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89
  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12
  * time       (time) datetime64[ns] 48B 2010-01-16T12:00:00 ... 2010-06-16
    ...         ...
    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    YG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    time_bnds  (time, nv) datetime64[ns] 96B dask.array<chunksize=(1, 2), meta=np.ndarray>
    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>
    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>
Dimensions without coordinates: nv, nb
Data variables:
    SSH        (time, tile, j, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
    SSHIBC     (time, tile, j, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
    SSHNOIBC   (time, tile, j, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
    ETAN       (time, tile, j, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
Attributes: (12/57)
    acknowledgement:              This research was carried out by the Jet Pr...
    author:                       Ian Fenty and Ou Wang
    cdm_data_type:                Grid
    comment:                      Fields provided on the curvilinear lat-lon-...
    Conventions:                  CF-1.8, ACDD-1.3
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2010-02-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2010-01-01T00:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean llc9...
    uuid:                         9ce7afa6-400c-11eb-ab45-0cc47a3f49c3

xarray.Dataset

• Dimensions:
  • time: 6
  • tile: 13
  • j: 90
  • i: 90
  • i_g: 90
  • j_g: 90
  • nv: 2
  • nb: 4
• Coordinates: (13)
  • i
    (i)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    long_name :

    grid index in x for variables at tracer and 'v' locations

    swap_dim :

    XC

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'v' variables (e.g., VVEL) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • i_g
    (i_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    long_name :

    grid index in x for variables at 'u' and 'g' locations

    c_grid_axis_shift :

    -0.5

    swap_dim :

    XG

    comment :

    In the Arakawa C-grid system, 'u' (e.g., UVEL) and 'g' variables (e.g., XG) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • j
    (j)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    long_name :

    grid index in y for variables at tracer and 'u' locations

    swap_dim :

    YC

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'u' variables (e.g., UVEL) have the same y coordinate on the model grid.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • j_g
    (j_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    long_name :

    grid index in y for variables at 'v' and 'g' locations

    c_grid_axis_shift :

    -0.5

    swap_dim :

    YG

    comment :

    In the Arakawa C-grid system, 'v' (e.g., VVEL) and 'g' variables (e.g., XG) have the same y coordinate.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • tile
    (tile)
    int32
    0 1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    lat-lon-cap tile index

    comment :

    The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.

    coverage_content_type :

    coordinate

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)

  • time
    (time)
    datetime64[ns]
    2010-01-16T12:00:00 ... 2010-06-16

    long_name :

    center time of averaging period

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    standard_name :

    time

    array(['2010-01-16T12:00:00.000000000', '2010-02-15T00:00:00.000000000',
           '2010-03-16T12:00:00.000000000', '2010-04-16T00:00:00.000000000',
           '2010-05-16T12:00:00.000000000', '2010-06-16T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • XC
    (tile, j, i)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    longitude of tracer grid cell center

    units :

    degrees_east

    coordinate :

    YC XC

    bounds :

    XC_bnds

    comment :

    nonuniform grid spacing

    coverage_content_type :

    coordinate

    standard_name :

    longitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YC
    (tile, j, i)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    latitude of tracer grid cell center

    units :

    degrees_north

    coordinate :

    YC XC

    bounds :

    YC_bnds

    comment :

    nonuniform grid spacing

    coverage_content_type :

    coordinate

    standard_name :

    latitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • XG
    (tile, j_g, i_g)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    longitude of 'southwest' corner of tracer grid cell

    units :

    degrees_east

    coordinate :

    YG XG

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    standard_name :

    longitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YG
    (tile, j_g, i_g)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    latitude of 'southwest' corner of tracer grid cell

    units :

    degrees_north

    coordinate :

    YG XG

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    standard_name :

    latitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    Array Chunk Bytes 96 B 16 B Shape (6, 2) (1, 2) Dask graph 6 chunks in 13 graph layers Data type datetime64[ns] numpy.ndarray

  • XC_bnds
    (tile, j, i, nb)
    float32
    dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>

    comment :

    Bounds array follows CF conventions. XC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), XC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), XC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), XC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    longitudes of tracer grid cell corners

    Array Chunk Bytes 1.61 MiB 1.61 MiB Shape (13, 90, 90, 4) (13, 90, 90, 4) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YC_bnds
    (tile, j, i, nb)
    float32
    dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>

    comment :

    Bounds array follows CF conventions. YC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), YC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), YC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), YC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    latitudes of tracer grid cell corners

    Array Chunk Bytes 1.61 MiB 1.61 MiB Shape (13, 90, 90, 4) (13, 90, 90, 4) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Data variables: (4)
  • SSH
    (time, tile, j, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Dynamic sea surface height anomaly

    units :

    m

    coverage_content_type :

    modelResult

    standard_name :

    sea_surface_height_above_geoid

    comment :

    Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    valid_min :

    -1.8805772066116333

    valid_max :

    1.4207719564437866

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • SSHIBC
    (time, tile, j, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading

    units :

    m

    coverage_content_type :

    modelResult

    comment :

    Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    valid_min :

    -0.30144819617271423

    valid_max :

    0.5248842239379883

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • SSHNOIBC
    (time, tile, j, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Sea surface height anomaly without the inverted barometer (IB) correction

    units :

    m

    coverage_content_type :

    modelResult

    comment :

    Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.

    valid_min :

    -1.6654272079467773

    valid_max :

    1.4550364017486572

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • ETAN
    (time, tile, j, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Model sea level anomaly

    units :

    m

    coverage_content_type :

    modelResult

    comment :

    Model sea level anomaly WITHOUT corrections for global mean density (steric) changes, inverted barometer effect, or volume displacement due to submerged sea-ice and snow . Note: ETAN should NOT be used for comparisons with altimetry data products because ETAN is NOT corrected for (a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH) nor (b) sea level displacement due to submerged sea-ice and snow (see sIceLoad). These corrections ARE made for the variables SSH and SSHNOIBC.

    valid_min :

    -8.304216384887695

    valid_max :

    1.460192084312439

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

• Indexes: (6)
  • i
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i'))

  • i_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i_g'))

  • j
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j'))

  • j_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j_g'))

  • tile
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int32', name='tile'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2010-01-16 12:00:00', '2010-02-15 00:00:00',
                   '2010-03-16 12:00:00', '2010-04-16 00:00:00',
                   '2010-05-16 12:00:00', '2010-06-16 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (57)

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations. Note: ETAN is model sea level anomaly and should not be compared with satellite altimetery products, see SSH and ETAN for more details.

  Conventions :

  CF-1.8, ACDD-1.3

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-16T18:07:27

  date_issued :

  2020-12-16T18:07:27

  date_metadata_modified :

  2021-03-15T21:56:12

  date_modified :

  2021-03-15T21:56:12

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  variable

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  variable

  geospatial_lon_units :

  degrees_east

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC

  id :

  10.5067/ECL5M-SSH44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > SEA SURFACE TOPOGRAPHY > SEA SURFACE HEIGHT, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  2010-02-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2010-01-01T00:00:00

  title :

  ECCO Sea Surface Height - Monthly Mean llc90 Grid (Version 4 Release 4)

  uuid :

  9ce7afa6-400c-11eb-ab45-0cc47a3f49c3

Direct download modes

download mode

The download mode directly downloads the queried files under a root directory of your choosing, creating the directory if needed. If ecco_podaac_access is called using this mode, the dictionary returned includes list(s) of the downloaded files that can be passed to xarray.open_mfdataset (or xarray.open_dataset, one file at a time). If ecco_podaac_to_xrdataset is used, the xarray.open_mfdataset step is included and an xarray Dataset is returned.

files_dict = ea.ecco_podaac_access([SSH_shortname,windstr_shortname],\
                                    StartDate='2010-01',EndDate='2010-06',\
                                    mode='download',\
                                    download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC'))

created download directory /home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4
DL Progress: 100%|###########################| 6/6 [00:02<00:00,  2.28it/s]

=====================================
total downloaded: 35.5 Mb
avg download speed: 13.44 Mb/s
Time spent = 2.6406123638153076 seconds


created download directory /home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4
DL Progress: 100%|###########################| 6/6 [00:02<00:00,  2.46it/s]

=====================================
total downloaded: 35.79 Mb
avg download speed: 14.6 Mb/s
Time spent = 2.451402187347412 seconds

files_dict

{'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4': ['/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc'],
 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4': ['/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
  '/home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4/OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc']}

Or if ecco_podaac_to_xrdataset is used (we’ll time how long this takes so it can be compared to other access modes):

import os

# remove files just downloaded
for shortname,files_list in files_dict.items():
    for file in files_list:
        os.remove(file)

%%time

ds_dict = ea.ecco_podaac_to_xrdataset([SSH_shortname,windstr_shortname],\
                                    StartDate='2010-01',EndDate='2010-06',\
                                    mode='download',\
                                    download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC'))

created download directory /home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4
DL Progress: 100%|###########################| 6/6 [00:01<00:00,  3.21it/s]

=====================================
total downloaded: 35.5 Mb
avg download speed: 18.89 Mb/s
Time spent = 1.878847599029541 seconds


created download directory /home/jovyan/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4
DL Progress: 100%|###########################| 6/6 [00:02<00:00,  2.49it/s]

=====================================
total downloaded: 35.79 Mb
avg download speed: 14.78 Mb/s
Time spent = 2.422295331954956 seconds


CPU times: user 929 ms, sys: 793 ms, total: 1.72 s
Wall time: 5.52 s

Since multiple datasets were queried, the results of ecco_podaac_to_xrdataset are returned in the form of a dictionary. The contents of the wind stress dataset can be seen here:

ds_dict[windstr_shortname]

<xarray.Dataset> Size: 15MB
Dimensions:    (time: 6, tile: 13, j: 90, i: 90, i_g: 90, j_g: 90, nv: 2, nb: 4)
Coordinates: (12/13)
  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89
  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89
  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89
  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89
  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12
  * time       (time) datetime64[ns] 48B 2010-01-16T12:00:00 ... 2010-06-16
    ...         ...
    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    YG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    time_bnds  (time, nv) datetime64[ns] 96B dask.array<chunksize=(1, 2), meta=np.ndarray>
    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>
    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>
Dimensions without coordinates: nv, nb
Data variables:
    EXFtaux    (time, tile, j, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
    EXFtauy    (time, tile, j, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
    oceTAUX    (time, tile, j, i_g) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
    oceTAUY    (time, tile, j_g, i) float32 3MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>
Attributes: (12/57)
    acknowledgement:              This research was carried out by the Jet Pr...
    author:                       Ian Fenty and Ou Wang
    cdm_data_type:                Grid
    comment:                      Fields provided on the curvilinear lat-lon-...
    Conventions:                  CF-1.8, ACDD-1.3
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2010-02-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2010-01-01T00:00:00
    title:                        ECCO Ocean and Sea-Ice Surface Stress - Mon...
    uuid:                         b48fa40a-400d-11eb-9063-0cc47a3f49c3

xarray.Dataset

• Dimensions:
  • time: 6
  • tile: 13
  • j: 90
  • i: 90
  • i_g: 90
  • j_g: 90
  • nv: 2
  • nb: 4
• Coordinates: (13)
  • i
    (i)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    long_name :

    grid index in x for variables at tracer and 'v' locations

    swap_dim :

    XC

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'v' variables (e.g., VVEL) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • i_g
    (i_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    long_name :

    grid index in x for variables at 'u' and 'g' locations

    c_grid_axis_shift :

    -0.5

    swap_dim :

    XG

    comment :

    In the Arakawa C-grid system, 'u' (e.g., UVEL) and 'g' variables (e.g., XG) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • j
    (j)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    long_name :

    grid index in y for variables at tracer and 'u' locations

    swap_dim :

    YC

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'u' variables (e.g., UVEL) have the same y coordinate on the model grid.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • j_g
    (j_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    long_name :

    grid index in y for variables at 'v' and 'g' locations

    c_grid_axis_shift :

    -0.5

    swap_dim :

    YG

    comment :

    In the Arakawa C-grid system, 'v' (e.g., VVEL) and 'g' variables (e.g., XG) have the same y coordinate.

    coverage_content_type :

    coordinate

    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],
          dtype=int32)

  • tile
    (tile)
    int32
    0 1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    lat-lon-cap tile index

    comment :

    The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.

    coverage_content_type :

    coordinate

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)

  • time
    (time)
    datetime64[ns]
    2010-01-16T12:00:00 ... 2010-06-16

    long_name :

    center time of averaging period

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    standard_name :

    time

    array(['2010-01-16T12:00:00.000000000', '2010-02-15T00:00:00.000000000',
           '2010-03-16T12:00:00.000000000', '2010-04-16T00:00:00.000000000',
           '2010-05-16T12:00:00.000000000', '2010-06-16T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • XC
    (tile, j, i)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    longitude of tracer grid cell center

    units :

    degrees_east

    coordinate :

    YC XC

    bounds :

    XC_bnds

    comment :

    nonuniform grid spacing

    coverage_content_type :

    coordinate

    standard_name :

    longitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YC
    (tile, j, i)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    latitude of tracer grid cell center

    units :

    degrees_north

    coordinate :

    YC XC

    bounds :

    YC_bnds

    comment :

    nonuniform grid spacing

    coverage_content_type :

    coordinate

    standard_name :

    latitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • XG
    (tile, j_g, i_g)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    longitude of 'southwest' corner of tracer grid cell

    units :

    degrees_east

    coordinate :

    YG XG

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    standard_name :

    longitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YG
    (tile, j_g, i_g)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    latitude of 'southwest' corner of tracer grid cell

    units :

    degrees_north

    coordinate :

    YG XG

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    standard_name :

    latitude

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    Array Chunk Bytes 96 B 16 B Shape (6, 2) (1, 2) Dask graph 6 chunks in 13 graph layers Data type datetime64[ns] numpy.ndarray

  • XC_bnds
    (tile, j, i, nb)
    float32
    dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>

    comment :

    Bounds array follows CF conventions. XC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), XC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), XC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), XC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    longitudes of tracer grid cell corners

    Array Chunk Bytes 1.61 MiB 1.61 MiB Shape (13, 90, 90, 4) (13, 90, 90, 4) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YC_bnds
    (tile, j, i, nb)
    float32
    dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>

    comment :

    Bounds array follows CF conventions. YC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), YC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), YC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), YC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    latitudes of tracer grid cell corners

    Array Chunk Bytes 1.61 MiB 1.61 MiB Shape (13, 90, 90, 4) (13, 90, 90, 4) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Data variables: (4)
  • EXFtaux
    (time, tile, j, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Wind stress in the model +x direction

    units :

    N m-2

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +x direction (UVEL)

    standard_name :

    surface_downward_x_stress

    comment :

    Wind stress in the +x direction at the tracer cell on the native model grid. Note: EXFtaux is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +x direction is NOT EXFtaux, but a combination of EXFtaux wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUX). EXFtaux is the sum of ERA-Interim stress and the control adjustment from ocean state estimation.

    valid_min :

    -1.6020684242248535

    valid_max :

    1.0077697038650513

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • EXFtauy
    (time, tile, j, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Wind stress in the model +y direction

    units :

    N m-2

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +y direction (VVEL)

    standard_name :

    surface_downward_y_stress

    comment :

    Wind stress in the +y direction at the tracer cell on the native model grid. Note: EXFtauy is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +y direction is NOT EXFtauy, but a combination of EXFtauy wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUY). EXFtaux is the sum of ERA-Interim stress and the control adjustment from ocean state estimation.

    valid_min :

    -0.7632240653038025

    valid_max :

    0.7666416764259338

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • oceTAUX
    (time, tile, j, i_g)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Ocean surface stress in the model +x direction

    units :

    N m-2

    mate :

    oceTAUY

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +x direction (UVEL)

    standard_name :

    downward_x_stress_at_sea_water_surface

    comment :

    Ocean surface stress due to wind and sea-ice in the +x direction centered over the 'u' side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUX(i_g,j) corresponds to +x momentum fluxes at 'u' edge of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.

    valid_min :

    -0.7581670880317688

    valid_max :

    0.7922194004058838

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

  • oceTAUY
    (time, tile, j_g, i)
    float32
    dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Ocean surface stress in the model +y direction

    units :

    N m-2

    mate :

    oceTAUX

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +y direction (VVEL)

    standard_name :

    downward_y_stress_at_sea_water_surface

    comment :

    Ocean surface stress due to wind and sea-ice in the +y direction centered over the 'v' side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUY(i_g,j) corresponds to +y momentum fluxes at 'v' edge of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.

    valid_min :

    -0.7589108347892761

    valid_max :

    0.6071499586105347

    Array Chunk Bytes 2.41 MiB 411.33 kiB Shape (6, 13, 90, 90) (1, 13, 90, 90) Dask graph 6 chunks in 13 graph layers Data type float32 numpy.ndarray

• Indexes: (6)
  • i
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i'))

  • i_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i_g'))

  • j
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j'))

  • j_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j_g'))

  • tile
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int32', name='tile'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2010-01-16 12:00:00', '2010-02-15 00:00:00',
                   '2010-03-16 12:00:00', '2010-04-16 00:00:00',
                   '2010-05-16 12:00:00', '2010-06-16 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (57)

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.

  Conventions :

  CF-1.8, ACDD-1.3

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-16T18:15:17

  date_issued :

  2020-12-16T18:15:17

  date_metadata_modified :

  2021-03-15T21:55:59

  date_modified :

  2021-03-15T21:55:59

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  variable

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  variable

  geospatial_lon_units :

  degrees_east

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC

  id :

  10.5067/ECL5M-STR44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > OCEAN WINDS > WIND STRESS, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged ocean and sea-ice surface stress on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  2010-02-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2010-01-01T00:00:00

  title :

  ECCO Ocean and Sea-Ice Surface Stress - Monthly Mean llc90 Grid (Version 4 Release 4)

  uuid :

  b48fa40a-400d-11eb-9063-0cc47a3f49c3

download_ifspace mode

This mode is similar to download, but it will also query how much storage is available at the target download location before carrying out downloads, and returns an error if the space to be occupied by the downloaded files is more than a specified fraction of available storage. The function also takes into account if some or all of the queried files are already on disk, and therefore do not need to be downloaded again.

%%time

ds_dict = ea.ecco_podaac_to_xrdataset([SSH_shortname,windstr_shortname],\
                                    StartDate='2010-01',EndDate='2010-06',\
                                    mode='download_ifspace',\
                                    download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC'))

Size of files to be downloaded to instance is 0.0 GB,
which is 0.0% of the 4.538 GB available storage.
Proceeding with file downloads via NASA Earthdata URLs

SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading
DL Progress: 100%|########################| 6/6 [00:00<00:00, 71902.35it/s]

=====================================
total downloaded: 0.0 Mb
avg download speed: 0.0 Mb/s
Time spent = 0.0026917457580566406 seconds



OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading

OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc already exists, and force=False, not re-downloading
DL Progress: 100%|########################| 6/6 [00:00<00:00, 85307.88it/s]

=====================================
total downloaded: 0.0 Mb
avg download speed: 0.0 Mb/s
Time spent = 0.002772808074951172 seconds


CPU times: user 210 ms, sys: 102 ms, total: 312 ms
Wall time: 482 ms

Notice that the size of files to be downloaded was zero, since all of these files were already on disk at the specified location.

# remove downloaded files
for shortname,files_list in files_dict.items():
    for file in files_list:
        os.remove(file)

download_subset mode

The download_subset mode is essentially a wrapper for the ecco_podaac_download_subset function, which uses Opendap to allow spatial, temporal, and variable-based subsetting of ECCO datasets and granules at the download stage. Depending on the size of the source dataset (e.g., whether the dataset has a depth dimension or not), this mode may be faster or slower than downloading the full granule files; it will almost certainly be slower than using mode = s3_open_fsspec when you have the json files available. But it can be a space- and time-saver when you are working on your local machine and not in the cloud.

Multiple examples of the ecco_podaac_download_subset functionality are provided in the Downloading Subsets of ECCO Datasets tutorial. Here is one example using ecco_podaac_to_xrdataset to open one year of monthly SST using mode = download_subset (in the ECCOv4r4 output, SST is the top depth layer of THETA):

ds_SST_2015 = ea.ecco_podaac_to_xrdataset('ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4',\
                                            StartDate='2015-01',EndDate='2015-12',\
                                            mode='download_subset',\
                                            download_root_dir=join(user_home_dir,'Downloads',\
                                                                   'ECCO_V4r4_PODAAC','SST_global'),\
                                            vars_to_include=['THETA'],\
                                            k_isel=[0,1,1])

Creating download directory /home/jovyan/Downloads/ECCO_V4r4_PODAAC/SST_global/ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4

Please wait while program searches for the granules ...


Total number of matching granules: 12
DL Progress: 100%|#########################| 12/12 [00:31<00:00,  2.61s/it]

=====================================
total downloaded: 16.73 Mb
avg download speed: 0.53 Mb/s
Time spent = 31.296209812164307 seconds

ds_SST_2015

<xarray.Dataset> Size: 10MB
Dimensions:    (time: 12, k: 1, tile: 13, j: 90, i: 90, j_g: 90, i_g: 90,
                k_p1: 2, k_l: 1, nb: 4, k_u: 1, nv: 2)
Coordinates: (12/24)
    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    Zp1        (k_p1) float32 8B dask.array<chunksize=(2,), meta=np.ndarray>
    Zl         (k_l) float32 4B dask.array<chunksize=(1,), meta=np.ndarray>
    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    XC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    YG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>
    ...         ...
  * k_p1       (k_p1) int32 8B 0 1
  * k_u        (k_u) int32 4B 0
  * nb         (nb) float32 16B 0.0 1.0 2.0 3.0
  * nv         (nv) float32 8B 0.0 1.0
  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12
  * time       (time) datetime64[ns] 96B 2015-01-16T12:00:00 ... 2015-12-16T1...
Data variables:
    THETA      (time, k, tile, j, i) float32 5MB dask.array<chunksize=(1, 1, 13, 90, 90), meta=np.ndarray>
Attributes: (12/63)
    acknowledgement:                 This research was carried out by the Jet...
    author:                          Ian Fenty and Ou Wang
    cdm_data_type:                   Grid
    comment:                         Fields provided on the curvilinear lat-l...
    Conventions:                     CF-1.8, ACDD-1.3
    coordinates_comment:             Note: the global 'coordinates' attribute...
    ...                              ...
    time_coverage_end:               2015-02-01T00:00:00
    time_coverage_resolution:        P1M
    time_coverage_start:             2015-01-01T00:00:00
    title:                           ECCO Ocean Temperature and Salinity - Mo...
    uuid:                            f4cfd0f6-4181-11eb-9946-0cc47a3f4815
    history_json:                    [{"$schema":"https:\/\/harmony.earthdata...

xarray.Dataset

• Dimensions:
  • time: 12
  • k: 1
  • tile: 13
  • j: 90
  • i: 90
  • j_g: 90
  • i_g: 90
  • k_p1: 2
  • k_l: 1
  • nb: 4
  • k_u: 1
  • nv: 2
• Coordinates: (24)
  • XG
    (tile, j_g, i_g)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    longitude of 'southwest' corner of tracer grid cell

    units :

    degrees_east

    coordinate :

    YG XG

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    standard_name :

    longitude

    origname :

    XG

    fullnamepath :

    /XG

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zp1
    (k_p1)
    float32
    dask.array<chunksize=(2,), meta=np.ndarray>

    long_name :

    depth of tracer grid cell interface

    units :

    m

    positive :

    up

    comment :

    Contains one element more than the number of vertical layers. First element is 0m, the depth of the upper interface of the surface grid cell. Last element is the depth of the lower interface of the deepest grid cell.

    coverage_content_type :

    coordinate

    standard_name :

    depth

    origname :

    Zp1

    fullnamepath :

    /Zp1

    Array Chunk Bytes 8 B 8 B Shape (2,) (2,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zl
    (k_l)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    m

    positive :

    up

    coverage_content_type :

    coordinate

    standard_name :

    depth

    long_name :

    depth of the top face of tracer grid cells

    comment :

    First element is 0m, the depth of the top face of the first tracer grid cell (ocean surface). Last element is the depth of the top face of the deepest grid cell. The use of 'l' in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.

    origname :

    Zl

    fullnamepath :

    /Zl

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YC
    (tile, j, i)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    latitude of tracer grid cell center

    units :

    degrees_north

    coordinate :

    YC XC

    bounds :

    YC_bnds

    comment :

    nonuniform grid spacing

    coverage_content_type :

    coordinate

    standard_name :

    latitude

    origname :

    YC

    fullnamepath :

    /YC

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • XC
    (tile, j, i)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    longitude of tracer grid cell center

    units :

    degrees_east

    coordinate :

    YC XC

    bounds :

    XC_bnds

    comment :

    nonuniform grid spacing

    coverage_content_type :

    coordinate

    standard_name :

    longitude

    origname :

    XC

    fullnamepath :

    /XC

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YG
    (tile, j_g, i_g)
    float32
    dask.array<chunksize=(13, 90, 90), meta=np.ndarray>

    long_name :

    latitude of 'southwest' corner of tracer grid cell

    units :

    degrees_north

    coordinate :

    YG XG

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    standard_name :

    latitude

    origname :

    YG

    fullnamepath :

    /YG

    Array Chunk Bytes 411.33 kiB 411.33 kiB Shape (13, 90, 90) (13, 90, 90) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • XC_bnds
    (tile, j, i, nb)
    float32
    dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>

    comment :

    Bounds array follows CF conventions. XC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), XC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), XC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), XC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    longitudes of tracer grid cell corners

    origname :

    XC_bnds

    fullnamepath :

    /XC_bnds

    Array Chunk Bytes 1.61 MiB 1.61 MiB Shape (13, 90, 90, 4) (13, 90, 90, 4) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Zu
    (k_u)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    units :

    m

    positive :

    up

    coverage_content_type :

    coordinate

    standard_name :

    depth

    long_name :

    depth of the bottom face of tracer grid cells

    comment :

    First element is -10m, the depth of the bottom face of the first tracer grid cell. Last element is the depth of the bottom face of the deepest grid cell. The use of 'u' in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.

    origname :

    Zu

    fullnamepath :

    /Zu

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Z_bnds
    (k, nv)
    float32
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    One pair of depths for each vertical level.

    coverage_content_type :

    coordinate

    long_name :

    depths of tracer grid cell upper and lower interfaces

    origname :

    Z_bnds

    fullnamepath :

    /Z_bnds

    Array Chunk Bytes 8 B 8 B Shape (1, 2) (1, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • YC_bnds
    (tile, j, i, nb)
    float32
    dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>

    comment :

    Bounds array follows CF conventions. YC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), YC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), YC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), YC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    latitudes of tracer grid cell corners

    origname :

    YC_bnds

    fullnamepath :

    /YC_bnds

    Array Chunk Bytes 1.61 MiB 1.61 MiB Shape (13, 90, 90, 4) (13, 90, 90, 4) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time_bnds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    origname :

    time_bnds

    fullnamepath :

    /time_bnds

    Array Chunk Bytes 192 B 16 B Shape (12, 2) (1, 2) Dask graph 12 chunks in 25 graph layers Data type datetime64[ns] numpy.ndarray

  • Z
    (k)
    float32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    depth of tracer grid cell center

    units :

    m

    positive :

    up

    bounds :

    Z_bnds

    comment :

    Non-uniform vertical spacing.

    coverage_content_type :

    coordinate

    standard_name :

    depth

    origname :

    Z

    fullnamepath :

    /Z

    Array Chunk Bytes 4 B 4 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • i
    (i)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    long_name :

    grid index in x for variables at tracer and 'v' locations

    swap_dim :

    XC

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'v' variables (e.g., VVEL) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    origname :

    i

    fullnamepath :

    /i

    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],
          dtype=int32)

  • i_g
    (i_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    long_name :

    grid index in x for variables at 'u' and 'g' locations

    c_grid_axis_shift :

    -0.5

    swap_dim :

    XG

    comment :

    In the Arakawa C-grid system, 'u' (e.g., UVEL) and 'g' variables (e.g., XG) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    origname :

    i_g

    fullnamepath :

    /i_g

    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],
          dtype=int32)

  • j
    (j)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    long_name :

    grid index in y for variables at tracer and 'u' locations

    swap_dim :

    YC

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'u' variables (e.g., UVEL) have the same y coordinate on the model grid.

    coverage_content_type :

    coordinate

    origname :

    j

    fullnamepath :

    /j

    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],
          dtype=int32)

  • j_g
    (j_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    long_name :

    grid index in y for variables at 'v' and 'g' locations

    c_grid_axis_shift :

    -0.5

    swap_dim :

    YG

    comment :

    In the Arakawa C-grid system, 'v' (e.g., VVEL) and 'g' variables (e.g., XG) have the same y coordinate.

    coverage_content_type :

    coordinate

    origname :

    j_g

    fullnamepath :

    /j_g

    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],
          dtype=int32)

  • k
    (k)
    int32
    0

    axis :

    Z

    long_name :

    grid index in z for tracer variables

    swap_dim :

    Z

    coverage_content_type :

    coordinate

    origname :

    k

    fullnamepath :

    /k

    array([0], dtype=int32)

  • k_l
    (k_l)
    int32
    0

    axis :

    Z

    c_grid_axis_shift :

    -0.5

    swap_dim :

    Zl

    coverage_content_type :

    coordinate

    long_name :

    grid index in z corresponding to the top face of tracer grid cells ('w' locations)

    comment :

    First index corresponds to the top surface of the uppermost tracer grid cell. The use of 'l' in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid.

    origname :

    k_l

    fullnamepath :

    /k_l

    array([0], dtype=int32)

  • k_p1
    (k_p1)
    int32
    0 1

    axis :

    Z

    long_name :

    grid index in z for variables at 'w' locations

    c_grid_axis_shift :

    [-0.5 0.5]

    swap_dim :

    Zp1

    comment :

    Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).

    coverage_content_type :

    coordinate

    origname :

    k_p1

    fullnamepath :

    /k_p1

    array([0, 1], dtype=int32)

  • k_u
    (k_u)
    int32
    0

    axis :

    Z

    c_grid_axis_shift :

    0.5

    swap_dim :

    Zu

    coverage_content_type :

    coordinate

    long_name :

    grid index in z corresponding to the bottom face of tracer grid cells ('w' locations)

    comment :

    First index corresponds to the bottom surface of the uppermost tracer grid cell. The use of 'u' in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid.

    origname :

    k_u

    fullnamepath :

    /k_u

    array([0], dtype=int32)

  • nb
    (nb)
    float32
    0.0 1.0 2.0 3.0

    array([0., 1., 2., 3.], dtype=float32)

  • nv
    (nv)
    float32
    0.0 1.0

    array([0., 1.], dtype=float32)

  • tile
    (tile)
    int32
    0 1 2 3 4 5 6 7 8 9 10 11 12

    long_name :

    lat-lon-cap tile index

    comment :

    The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.

    coverage_content_type :

    coordinate

    origname :

    tile

    fullnamepath :

    /tile

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)

  • time
    (time)
    datetime64[ns]
    2015-01-16T12:00:00 ... 2015-12-...

    long_name :

    center time of averaging period

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    standard_name :

    time

    origname :

    time

    fullnamepath :

    /time

    array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',
           '2015-03-16T12:00:00.000000000', '2015-04-16T00:00:00.000000000',
           '2015-05-16T12:00:00.000000000', '2015-06-16T00:00:00.000000000',
           '2015-07-16T12:00:00.000000000', '2015-08-16T12:00:00.000000000',
           '2015-09-16T00:00:00.000000000', '2015-10-16T12:00:00.000000000',
           '2015-11-16T00:00:00.000000000', '2015-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • THETA
    (time, k, tile, j, i)
    float32
    dask.array<chunksize=(1, 1, 13, 90, 90), meta=np.ndarray>

    long_name :

    Potential temperature

    units :

    degree_C

    coverage_content_type :

    modelResult

    standard_name :

    sea_water_potential_temperature

    comment :

    Sea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g ho_{0} z$.

    valid_min :

    -2.2909388542175293

    valid_max :

    36.032955169677734

    origname :

    THETA

    fullnamepath :

    /THETA

    Array Chunk Bytes 4.82 MiB 411.33 kiB Shape (12, 1, 13, 90, 90) (1, 1, 13, 90, 90) Dask graph 12 chunks in 25 graph layers Data type float32 numpy.ndarray

• Indexes: (12)
  • i
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i'))

  • i_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i_g'))

  • j
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j'))

  • j_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j_g'))

  • k
    PandasIndex

    PandasIndex(Index([0], dtype='int32', name='k'))

  • k_l
    PandasIndex

    PandasIndex(Index([0], dtype='int32', name='k_l'))

  • k_p1
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int32', name='k_p1'))

  • k_u
    PandasIndex

    PandasIndex(Index([0], dtype='int32', name='k_u'))

  • nb
    PandasIndex

    PandasIndex(Index([0.0, 1.0, 2.0, 3.0], dtype='float32', name='nb'))

  • nv
    PandasIndex

    PandasIndex(Index([0.0, 1.0], dtype='float32', name='nv'))

  • tile
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int32', name='tile'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-16 12:00:00', '2015-02-15 00:00:00',
                   '2015-03-16 12:00:00', '2015-04-16 00:00:00',
                   '2015-05-16 12:00:00', '2015-06-16 00:00:00',
                   '2015-07-16 12:00:00', '2015-08-16 12:00:00',
                   '2015-09-16 00:00:00', '2015-10-16 12:00:00',
                   '2015-11-16 00:00:00', '2015-12-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (63)

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.

  Conventions :

  CF-1.8, ACDD-1.3

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-18T14:39:57

  date_issued :

  2020-12-18T14:39:57

  date_metadata_modified :

  2021-03-15T21:56:10

  date_modified :

  2021-03-15T21:56:10

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  variable

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  variable

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  -6134.5

  geospatial_vertical_positive :

  up

  geospatial_vertical_resolution :

  variable

  geospatial_vertical_units :

  meter

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC 2024-10-08 18:12:46 GMT hyrax-1.17.0-166 https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%2520Ocean%2520Temperature%2520and%2520Salinity%2520-%2520Monthly%2520Mean%2520llc90%2520Grid%2520(Version%25204%2520Release%25204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_2015-01_ECCO_V4r4_native_llc0090.dap.nc4?dap4.ce=/XG%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;/Zp1%5B0:1:1%5D;/Zl%5B0:1:0%5D;/YC%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;/XC%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;/YG%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;/XC_bnds%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D%5B0:1:3%5D;/Zu%5B0:1:0%5D;/THETA%5B0:1:0%5D%5B0:1:0%5D%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;/Z_bnds%5B0:1:0%5D%5B0:1:1%5D;/YC_bnds%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D%5B0:1:3%5D;/time_bnds%5B0:1:0%5D%5B0:1:1%5D;/Z%5B0:1:0%5D;/i%5B0:1:89%5D;/i_g%5B0:1:89%5D;/j%5B0:1:89%5D;/j_g%5B0:1:89%5D;/k%5B0:1:0%5D;/k_l%5B0:1:0%5D;/k_p1%5B0:1:1%5D;/k_u%5B0:1:0%5D;/nb%5B0:1:3%5D;/nv%5B0:1:1%5D;/tile%5B0:1:12%5D;/time%5B0:1:0%5D

  id :

  10.5067/ECL5M-OTS44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY, EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > POTENTIAL TEMPERATURE

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  OCEAN_TEMPERATURE_SALINITY_mon_mean_2015-01_ECCO_V4r4_native_llc0090.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged ocean potential temperature and salinity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  2015-02-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2015-01-01T00:00:00

  title :

  ECCO Ocean Temperature and Salinity - Monthly Mean llc90 Grid (Version 4 Release 4)

  uuid :

  f4cfd0f6-4181-11eb-9946-0cc47a3f4815

  history_json :

  [{"$schema":"https:\/\/harmony.earthdata.nasa.gov\/schemas\/history\/0.1.0\/history-0.1.0.json","date_time":"2024-10-08T18:12:46.000+0000","program":"hyrax","version":"1.17.0-166","parameters":[{"request_url":"https:\/\/opendap.earthdata.nasa.gov\/providers\/POCLOUD\/collections\/ECCO%2520Ocean%2520Temperature%2520and%2520Salinity%2520-%2520Monthly%2520Mean%2520llc90%2520Grid%2520(Version%25204%2520Release%25204)\/granules\/OCEAN_TEMPERATURE_SALINITY_mon_mean_2015-01_ECCO_V4r4_native_llc0090.dap.nc4?dap4.ce=\/XG%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;\/Zp1%5B0:1:1%5D;\/Zl%5B0:1:0%5D;\/YC%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;\/XC%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;\/YG%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;\/XC_bnds%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D%5B0:1:3%5D;\/Zu%5B0:1:0%5D;\/THETA%5B0:1:0%5D%5B0:1:0%5D%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D;\/Z_bnds%5B0:1:0%5D%5B0:1:1%5D;\/YC_bnds%5B0:1:12%5D%5B0:1:89%5D%5B0:1:89%5D%5B0:1:3%5D;\/time_bnds%5B0:1:0%5D%5B0:1:1%5D;\/Z%5B0:1:0%5D;\/i%5B0:1:89%5D;\/i_g%5B0:1:89%5D;\/j%5B0:1:89%5D;\/j_g%5B0:1:89%5D;\/k%5B0:1:0%5D;\/k_l%5B0:1:0%5D;\/k_p1%5B0:1:1%5D;\/k_u%5B0:1:0%5D;\/nb%5B0:1:3%5D;\/nv%5B0:1:1%5D;\/tile%5B0:1:12%5D;\/time%5B0:1:0%5D"},{"decoded_constraint":"dap4.ce=\/XG[0:1:12][0:1:89][0:1:89];\/Zp1[0:1:1];\/Zl[0:1:0];\/YC[0:1:12][0:1:89][0:1:89];\/XC[0:1:12][0:1:89][0:1:89];\/YG[0:1:12][0:1:89][0:1:89];\/XC_bnds[0:1:12][0:1:89][0:1:89][0:1:3];\/Zu[0:1:0];\/THETA[0:1:0][0:1:0][0:1:12][0:1:89][0:1:89];\/Z_bnds[0:1:0][0:1:1];\/YC_bnds[0:1:12][0:1:89][0:1:89][0:1:3];\/time_bnds[0:1:0][0:1:1];\/Z[0:1:0];\/i[0:1:89];\/i_g[0:1:89];\/j[0:1:89];\/j_g[0:1:89];\/k[0:1:0];\/k_l[0:1:0];\/k_p1[0:1:1];\/k_u[0:1:0];\/nb[0:1:3];\/nv[0:1:1];\/tile[0:1:12];\/time[0:1:0]"}]}]

Now plot tile 11 of SST during Dec 2015:

ds_SST_2015.THETA.isel(time=11,k=0,tile=11).plot(cmap='RdYlBu_r')
plt.title('SST, Dec 2015')
plt.show()

In-cloud only access modes

s3_open mode

If you are working in the AWS cloud (in region us-west-2), you can open files from S3 storage without downloading them; this is called “direct access”.

%%time

ds_dict = ea.ecco_podaac_to_xrdataset([SSH_shortname,windstr_shortname],\
                                        StartDate='2010-01',EndDate='2010-06',\
                                        mode='s3_open')

{'ShortName': 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

Total number of matching granules: 6
{'ShortName': 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

Total number of matching granules: 6
CPU times: user 3.01 s, sys: 1.05 s, total: 4.06 s
Wall time: 14.1 s

s3_open_fsspec mode

The s3_open mode allows you to access data “remotely” from S3, but it is usually slower than downloading the data. However, the fsspec and kerchunk libraries provide an efficient way to access data by storing pointers to data chunks in json files. These files have been produced for the ECCO datasets, and by using mode = s3_open_fsspec we can access the data much more quickly without downloading it!

Note

For ECCO Hackweek participants, the json files needed for this mode are available on the efs_ecco volume at /efs_ecco/mzz-jsons/. Specify jsons_root_dir=join('/efs_ecco','mzz-jsons') to use this mode.

%%time

ds_dict = ea.ecco_podaac_to_xrdataset([SSH_shortname,windstr_shortname],\
                                        StartDate='2010-01',EndDate='2010-06',\
                                        mode='s3_open_fsspec',\
                                        jsons_root_dir=join('/efs_ecco','mzz-jsons'))

CPU times: user 77.7 ms, sys: 4.97 ms, total: 82.6 ms
Wall time: 3.34 s

ds = xr.merge(list(ds_dict.values()))
ds

<xarray.Dataset> Size: 25MB
Dimensions:    (time: 6, tile: 13, j: 90, i: 90, nb: 4, j_g: 90, i_g: 90, nv: 2)
Coordinates: (12/13)
    XC         (tile, j, i) float32 421kB -111.6 -111.3 -110.9 ... -105.6 -111.9
    XC_bnds    (tile, j, i, nb) float32 2MB -115.0 -115.0 ... -115.0 -108.5
    XG         (tile, j_g, i_g) float32 421kB -115.0 -115.0 ... -102.9 -109.0
    YC         (tile, j, i) float32 421kB -88.24 -88.38 -88.52 ... -88.08 -88.1
    YC_bnds    (tile, j, i, nb) float32 2MB -88.18 -88.32 ... -88.18 -88.16
    YG         (tile, j_g, i_g) float32 421kB -88.18 -88.32 ... -87.99 -88.02
    ...         ...
  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89
  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89
  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89
  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12
  * time       (time) datetime64[ns] 48B 2010-01-16T12:00:00 ... 2010-06-16
    time_bnds  (time, nv) datetime64[ns] 96B 2010-01-01 ... 2010-07-01
Dimensions without coordinates: nb, nv
Data variables:
    ETAN       (time, tile, j, i) float32 3MB ...
    SSH        (time, tile, j, i) float32 3MB ...
    SSHIBC     (time, tile, j, i) float32 3MB ...
    SSHNOIBC   (time, tile, j, i) float32 3MB ...
    EXFtaux    (time, tile, j, i) float32 3MB ...
    EXFtauy    (time, tile, j, i) float32 3MB ...
    oceTAUX    (time, tile, j, i_g) float32 3MB ...
    oceTAUY    (time, tile, j_g, i) float32 3MB ...
Attributes: (12/57)
    Conventions:                  CF-1.8, ACDD-1.3
    acknowledgement:              This research was carried out by the Jet Pr...
    author:                       Ian Fenty and Ou Wang
    cdm_data_type:                Grid
    comment:                      Fields provided on the curvilinear lat-lon-...
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            1992-02-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          1992-01-01T12:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean llc9...
    uuid:                         9302811e-400c-11eb-b69e-0cc47a3f49c3

xarray.Dataset

• Dimensions:
  • time: 6
  • tile: 13
  • j: 90
  • i: 90
  • nb: 4
  • j_g: 90
  • i_g: 90
  • nv: 2
• Coordinates: (13)
  • XC
    (tile, j, i)
    float32
    -111.6 -111.3 ... -105.6 -111.9

    bounds :

    XC_bnds

    comment :

    nonuniform grid spacing

    coordinate :

    YC XC

    coverage_content_type :

    coordinate

    long_name :

    longitude of tracer grid cell center

    standard_name :

    longitude

    units :

    degrees_east

    array([[[-111.60647 , -111.303   , -110.94285 , ...,   64.791115,
               64.80521 ,   64.81917 ],
            [-104.8196  , -103.928444, -102.87706 , ...,   64.36745 ,
               64.41012 ,   64.4524  ],
            [ -98.198784,  -96.788055,  -95.14185 , ...,   63.936497,
               64.008224,   64.0793  ],
            ...,
            [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,
               50.5     ,   51.5     ],
            [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,
               50.5     ,   51.5     ],
            [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,
               50.5     ,   51.5     ]],
    
           [[ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,
               50.5     ,   51.5     ],
            [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,
               50.5     ,   51.5     ],
            [ -37.5     ,  -36.5     ,  -35.5     , ...,   49.5     ,
               50.5     ,   51.5     ],
    ...
            [ -40.5     ,  -40.5     ,  -40.5     , ...,  -40.5     ,
              -40.5     ,  -40.5     ],
            [ -39.5     ,  -39.5     ,  -39.5     , ...,  -39.5     ,
              -39.5     ,  -39.5     ],
            [ -38.5     ,  -38.5     ,  -38.5     , ...,  -38.5     ,
              -38.5     ,  -38.5     ]],
    
           [[-127.5     , -127.5     , -127.5     , ..., -115.850204,
             -115.50567 , -115.166985],
            [-126.5     , -126.5     , -126.5     , ..., -115.78025 ,
             -115.464066, -115.153244],
            [-125.5     , -125.5     , -125.5     , ..., -115.71079 ,
             -115.42275 , -115.139595],
            ...,
            [ -40.5     ,  -40.5     ,  -40.5     , ..., -101.42989 ,
             -106.83081 , -112.28605 ],
            [ -39.5     ,  -39.5     ,  -39.5     , ..., -100.48844 ,
             -106.24874 , -112.090065],
            [ -38.5     ,  -38.5     ,  -38.5     , ...,  -99.42048 ,
             -105.58465 , -111.86579 ]]], dtype=float32)

  • XC_bnds
    (tile, j, i, nb)
    float32
    -115.0 -115.0 ... -115.0 -108.5

    comment :

    Bounds array follows CF conventions. XC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), XC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), XC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), XC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    longitudes of tracer grid cell corners

    array([[[[-115.      , -115.      , -107.915245, -108.47213 ],
             [-115.      , -115.      , -107.26045 , -107.915245],
             [-115.      , -115.      , -106.47914 , -107.26045 ],
             ...,
             [  65.      ,   65.      ,   64.59443 ,   64.56598 ],
             [  65.      ,   65.      ,   64.62261 ,   64.59443 ],
             [  65.      ,   65.      ,   64.65055 ,   64.62261 ]],
    
            [[-108.47213 , -107.915245, -100.91797 , -101.9954  ],
             [-107.915245, -107.26045 ,  -99.66073 , -100.91797 ],
             [-107.26045 , -106.47914 ,  -98.17517 ,  -99.66073 ],
             ...,
             [  64.56598 ,   64.59443 ,   64.18147 ,   64.12406 ],
             [  64.59443 ,   64.62261 ,   64.23835 ,   64.18147 ],
             [  64.62261 ,   64.65055 ,   64.29474 ,   64.23835 ]],
    
            [[-101.9954  , -100.91797 ,  -94.218956,  -95.74128 ],
             [-100.91797 ,  -99.66073 ,  -92.46296 ,  -94.218956],
             [ -99.66073 ,  -98.17517 ,  -90.417885,  -92.46296 ],
             ...,
    ...
             [ -99.28857 , -104.466286, -103.74563 ,  -98.24634 ],
             [-104.466286, -109.73619 , -109.36924 , -103.74563 ],
             [-109.73619 , -115.      , -115.      , -109.36924 ]],
    
            [[ -40.      ,  -40.      ,  -39.      ,  -39.      ],
             [ -40.      ,  -40.      ,  -39.      ,  -39.      ],
             [ -40.      ,  -40.      ,  -39.      ,  -39.      ],
             ...,
             [ -98.24634 , -103.74563 , -102.928925,  -97.07265 ],
             [-103.74563 , -109.36924 , -108.95171 , -102.928925],
             [-109.36924 , -115.      , -115.      , -108.95171 ]],
    
            [[ -39.      ,  -39.      ,  -38.      ,  -38.      ],
             [ -39.      ,  -39.      ,  -38.      ,  -38.      ],
             [ -39.      ,  -39.      ,  -38.      ,  -38.      ],
             ...,
             [ -97.07265 , -102.928925, -101.9954  ,  -95.74128 ],
             [-102.928925, -108.95171 , -108.47213 , -101.9954  ],
             [-108.95171 , -115.      , -115.      , -108.47213 ]]]],
          dtype=float32)

  • XG
    (tile, j_g, i_g)
    float32
    -115.0 -115.0 ... -102.9 -109.0

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coordinate :

    YG XG

    coverage_content_type :

    coordinate

    long_name :

    longitude of 'southwest' corner of tracer grid cell

    standard_name :

    longitude

    units :

    degrees_east

    array([[[-115.      , -115.      , -115.      , ...,   65.      ,
               65.      ,   65.      ],
            [-108.47213 , -107.915245, -107.26045 , ...,   64.56598 ,
               64.59443 ,   64.62261 ],
            [-101.9954  , -100.91797 ,  -99.66073 , ...,   64.12406 ,
               64.18147 ,   64.23835 ],
            ...,
            [ -38.      ,  -37.      ,  -36.      , ...,   49.      ,
               50.      ,   51.      ],
            [ -38.      ,  -37.      ,  -36.      , ...,   49.      ,
               50.      ,   51.      ],
            [ -38.      ,  -37.      ,  -36.      , ...,   49.      ,
               50.      ,   51.      ]],
    
           [[ -38.      ,  -37.      ,  -36.      , ...,   49.      ,
               50.      ,   51.      ],
            [ -38.      ,  -37.      ,  -36.      , ...,   49.      ,
               50.      ,   51.      ],
            [ -38.      ,  -37.      ,  -36.      , ...,   49.      ,
               50.      ,   51.      ],
    ...
            [ -41.      ,  -41.      ,  -41.      , ...,  -41.      ,
              -41.      ,  -41.      ],
            [ -40.      ,  -40.      ,  -40.      , ...,  -40.      ,
              -40.      ,  -40.      ],
            [ -39.      ,  -39.      ,  -39.      , ...,  -39.      ,
              -39.      ,  -39.      ]],
    
           [[-128.      , -128.      , -128.      , ..., -116.06686 ,
             -115.70526 , -115.34945 ],
            [-127.      , -127.      , -127.      , ..., -115.982254,
             -115.64933 , -115.32173 ],
            [-126.      , -126.      , -126.      , ..., -115.89828 ,
             -115.59381 , -115.29422 ],
            ...,
            [ -41.      ,  -41.      ,  -41.      , ...,  -99.28857 ,
             -104.466286, -109.73619 ],
            [ -40.      ,  -40.      ,  -40.      , ...,  -98.24634 ,
             -103.74563 , -109.36924 ],
            [ -39.      ,  -39.      ,  -39.      , ...,  -97.07265 ,
             -102.928925, -108.95171 ]]], dtype=float32)

  • YC
    (tile, j, i)
    float32
    -88.24 -88.38 ... -88.08 -88.1

    bounds :

    YC_bnds

    comment :

    nonuniform grid spacing

    coordinate :

    YC XC

    coverage_content_type :

    coordinate

    long_name :

    latitude of tracer grid cell center

    standard_name :

    latitude

    units :

    degrees_north

    array([[[-88.24259  , -88.382515 , -88.52242  , ..., -80.57383  ,
             -80.50492  , -80.43992  ],
            [-88.21676  , -88.354485 , -88.49178  , ..., -80.56925  ,
             -80.50038  , -80.43542  ],
            [-88.164764 , -88.29827  , -88.43065  , ..., -80.55984  ,
             -80.49105  , -80.42617  ],
            ...,
            [-58.321964 , -58.321964 , -58.321964 , ..., -58.321964 ,
             -58.321964 , -58.321964 ],
            [-57.79962  , -57.79962  , -57.79962  , ..., -57.79962  ,
             -57.79962  , -57.79962  ],
            [-57.271408 , -57.271408 , -57.271408 , ..., -57.271408 ,
             -57.271408 , -57.271408 ]],
    
           [[-56.73891  , -56.73891  , -56.73891  , ..., -56.73891  ,
             -56.73891  , -56.73891  ],
            [-56.2021   , -56.2021   , -56.2021   , ..., -56.2021   ,
             -56.2021   , -56.2021   ],
            [-55.65936  , -55.65936  , -55.65936  , ..., -55.65936  ,
             -55.65936  , -55.65936  ],
    ...
            [  9.482398 ,   8.516253 ,   7.5699615, ..., -55.65936  ,
             -56.2021   , -56.73891  ],
            [  9.482398 ,   8.516253 ,   7.5699615, ..., -55.65936  ,
             -56.2021   , -56.73891  ],
            [  9.482398 ,   8.516253 ,   7.5699615, ..., -55.65936  ,
             -56.2021   , -56.73891  ]],
    
           [[-57.271408 , -57.79962  , -58.321964 , ..., -80.36532  ,
             -80.3745   , -80.37896  ],
            [-57.271408 , -57.79962  , -58.321964 , ..., -80.30862  ,
             -80.317726 , -80.32216  ],
            [-57.271408 , -57.79962  , -58.321964 , ..., -80.25618  ,
             -80.26523  , -80.26963  ],
            ...,
            [-57.271408 , -57.79962  , -58.321964 , ..., -87.7596   ,
             -87.80198  , -87.822876 ],
            [-57.271408 , -57.79962  , -58.321964 , ..., -87.89526  ,
             -87.94044  , -87.96276  ],
            [-57.271408 , -57.79962  , -58.321964 , ..., -88.030365 ,
             -88.07871  , -88.10267  ]]], dtype=float32)

  • YC_bnds
    (tile, j, i, nb)
    float32
    -88.18 -88.32 ... -88.18 -88.16

    comment :

    Bounds array follows CF conventions. YC_bnds[i,j,0] = 'southwest' corner (j-1, i-1), YC_bnds[i,j,1] = 'southeast' corner (j-1, i+1), YC_bnds[i,j,2] = 'northeast' corner (j+1, i+1), YC_bnds[i,j,3] = 'northwest' corner (j+1, i-1). Note: 'southwest', 'southeast', northwest', and 'northeast' do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coverage_content_type :

    coordinate

    long_name :

    latitudes of tracer grid cell corners

    array([[[[-88.17569 , -88.31587 , -88.30251 , -88.16335 ],
             [-88.31587 , -88.45609 , -88.44153 , -88.30251 ],
             [-88.45609 , -88.596344, -88.580345, -88.44153 ],
             ...,
             [-80.610275, -80.53945 , -80.5372  , -80.608   ],
             [-80.53945 , -80.47247 , -80.470245, -80.5372  ],
             [-80.47247 , -80.40948 , -80.40726 , -80.470245]],
    
            [[-88.16335 , -88.30251 , -88.26202 , -88.12586 ],
             [-88.30251 , -88.44153 , -88.397514, -88.26202 ],
             [-88.44153 , -88.580345, -88.53216 , -88.397514],
             ...,
             [-80.608   , -80.5372  , -80.530266, -80.601006],
             [-80.5372  , -80.470245, -80.46337 , -80.530266],
             [-80.470245, -80.40726 , -80.400444, -80.46337 ]],
    
            [[-88.12586 , -88.26202 , -88.194695, -88.06328 ],
             [-88.26202 , -88.397514, -88.32474 , -88.194695],
             [-88.397514, -88.53216 , -88.453026, -88.32474 ],
             ...,
    ...
             [-87.66318 , -87.71477 , -87.85214 , -87.797325],
             [-87.71477 , -87.7454  , -87.884766, -87.85214 ],
             [-87.7454  , -87.75544 , -87.89547 , -87.884766]],
    
            [[-57.005695, -57.53605 , -57.53605 , -57.005695],
             [-57.53605 , -58.06183 , -58.06183 , -57.53605 ],
             [-58.06183 , -58.58002 , -58.58002 , -58.06183 ],
             ...,
             [-87.797325, -87.85214 , -87.9892  , -87.93075 ],
             [-87.85214 , -87.884766, -88.02409 , -87.9892  ],
             [-87.884766, -87.89547 , -88.03555 , -88.02409 ]],
    
            [[-57.005695, -57.53605 , -57.53605 , -57.005695],
             [-57.53605 , -58.06183 , -58.06183 , -57.53605 ],
             [-58.06183 , -58.58002 , -58.58002 , -58.06183 ],
             ...,
             [-87.93075 , -87.9892  , -88.12586 , -88.06328 ],
             [-87.9892  , -88.02409 , -88.16335 , -88.12586 ],
             [-88.02409 , -88.03555 , -88.17569 , -88.16335 ]]]],
          dtype=float32)

  • YG
    (tile, j_g, i_g)
    float32
    -88.18 -88.32 ... -87.99 -88.02

    comment :

    Nonuniform grid spacing. Note: 'southwest' does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.

    coordinate :

    YG XG

    coverage_content_type :

    coordinate

    long_name :

    latitude of 'southwest' corner of tracer grid cell

    standard_name :

    latitude

    units :

    degrees_north

    array([[[-88.17569 , -88.31587 , -88.45609 , ..., -80.610275,
             -80.53945 , -80.47247 ],
            [-88.16335 , -88.30251 , -88.44153 , ..., -80.608   ,
             -80.5372  , -80.470245],
            [-88.12586 , -88.26202 , -88.397514, ..., -80.601006,
             -80.530266, -80.46337 ],
            ...,
            [-58.58002 , -58.58002 , -58.58002 , ..., -58.58002 ,
             -58.58002 , -58.58002 ],
            [-58.06183 , -58.06183 , -58.06183 , ..., -58.06183 ,
             -58.06183 , -58.06183 ],
            [-57.53605 , -57.53605 , -57.53605 , ..., -57.53605 ,
             -57.53605 , -57.53605 ]],
    
           [[-57.005695, -57.005695, -57.005695, ..., -57.005695,
             -57.005695, -57.005695],
            [-56.471046, -56.471046, -56.471046, ..., -56.471046,
             -56.471046, -56.471046],
            [-55.931786, -55.931786, -55.931786, ..., -55.931786,
             -55.931786, -55.931786],
    ...
            [  9.96973 ,   8.997536,   8.039881, ..., -55.384766,
             -55.931786, -56.471046],
            [  9.96973 ,   8.997536,   8.039881, ..., -55.384766,
             -55.931786, -56.471046],
            [  9.96973 ,   8.997536,   8.039881, ..., -55.384766,
             -55.931786, -56.471046]],
    
           [[-57.005695, -57.53605 , -58.06183 , ..., -80.38878 ,
             -80.400444, -80.40726 ],
            [-57.005695, -57.53605 , -58.06183 , ..., -80.33003 ,
             -80.34161 , -80.34837 ],
            [-57.005695, -57.53605 , -58.06183 , ..., -80.27549 ,
             -80.286995, -80.29371 ],
            ...,
            [-57.005695, -57.53605 , -58.06183 , ..., -87.66318 ,
             -87.71477 , -87.7454  ],
            [-57.005695, -57.53605 , -58.06183 , ..., -87.797325,
             -87.85214 , -87.884766],
            [-57.005695, -57.53605 , -58.06183 , ..., -87.93075 ,
             -87.9892  , -88.02409 ]]], dtype=float32)

  • i
    (i)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'v' variables (e.g., VVEL) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    long_name :

    grid index in x for variables at tracer and 'v' locations

    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],
          dtype=int32)

  • i_g
    (i_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    X

    c_grid_axis_shift :

    -0.5

    comment :

    In the Arakawa C-grid system, 'u' (e.g., UVEL) and 'g' variables (e.g., XG) have the same x coordinate on the model grid.

    coverage_content_type :

    coordinate

    long_name :

    grid index in x for variables at 'u' and 'g' locations

    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],
          dtype=int32)

  • j
    (j)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    comment :

    In the Arakawa C-grid system, tracer (e.g., THETA) and 'u' variables (e.g., UVEL) have the same y coordinate on the model grid.

    coverage_content_type :

    coordinate

    long_name :

    grid index in y for variables at tracer and 'u' locations

    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],
          dtype=int32)

  • j_g
    (j_g)
    int32
    0 1 2 3 4 5 6 ... 84 85 86 87 88 89

    axis :

    Y

    c_grid_axis_shift :

    -0.5

    comment :

    In the Arakawa C-grid system, 'v' (e.g., VVEL) and 'g' variables (e.g., XG) have the same y coordinate.

    coverage_content_type :

    coordinate

    long_name :

    grid index in y for variables at 'v' and 'g' locations

    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],
          dtype=int32)

  • tile
    (tile)
    int32
    0 1 2 3 4 5 6 7 8 9 10 11 12

    comment :

    The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.

    coverage_content_type :

    coordinate

    long_name :

    lat-lon-cap tile index

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)

  • time
    (time)
    datetime64[ns]
    2010-01-16T12:00:00 ... 2010-06-16

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    long_name :

    center time of averaging period

    standard_name :

    time

    array(['2010-01-16T12:00:00.000000000', '2010-02-15T00:00:00.000000000',
           '2010-03-16T12:00:00.000000000', '2010-04-16T00:00:00.000000000',
           '2010-05-16T12:00:00.000000000', '2010-06-16T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_bnds
    (time, nv)
    datetime64[ns]
    2010-01-01 ... 2010-07-01

    comment :

    Start and end times of averaging period.

    coverage_content_type :

    coordinate

    long_name :

    time bounds of averaging period

    array([['2010-01-01T00:00:00.000000000', '2010-02-01T00:00:00.000000000'],
           ['2010-02-01T00:00:00.000000000', '2010-03-01T00:00:00.000000000'],
           ['2010-03-01T00:00:00.000000000', '2010-04-01T00:00:00.000000000'],
           ['2010-04-01T00:00:00.000000000', '2010-05-01T00:00:00.000000000'],
           ['2010-05-01T00:00:00.000000000', '2010-06-01T00:00:00.000000000'],
           ['2010-06-01T00:00:00.000000000', '2010-07-01T00:00:00.000000000']],
          dtype='datetime64[ns]')

• Data variables: (8)
  • ETAN
    (time, tile, j, i)
    float32
    ...

    comment :

    Model sea level anomaly WITHOUT corrections for global mean density (steric) changes, inverted barometer effect, or volume displacement due to submerged sea-ice and snow . Note: ETAN should NOT be used for comparisons with altimetry data products because ETAN is NOT corrected for (a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH) nor (b) sea level displacement due to submerged sea-ice and snow (see sIceLoad). These corrections ARE made for the variables SSH and SSHNOIBC.

    coverage_content_type :

    modelResult

    long_name :

    Model sea level anomaly

    units :

    m

    valid_max :

    1.460192084312439

    valid_min :

    -8.304216384887695

    [631800 values with dtype=float32]

  • SSH
    (time, tile, j, i)
    float32
    ...

    comment :

    Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    Dynamic sea surface height anomaly

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    valid_max :

    1.4207719564437866

    valid_min :

    -1.8805772066116333

    [631800 values with dtype=float32]

  • SSHIBC
    (time, tile, j, i)
    float32
    ...

    comment :

    Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading

    units :

    m

    valid_max :

    0.5248842239379883

    valid_min :

    -0.30144819617271423

    [631800 values with dtype=float32]

  • SSHNOIBC
    (time, tile, j, i)
    float32
    ...

    comment :

    Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.

    coverage_content_type :

    modelResult

    long_name :

    Sea surface height anomaly without the inverted barometer (IB) correction

    units :

    m

    valid_max :

    1.4550364017486572

    valid_min :

    -1.6654272079467773

    [631800 values with dtype=float32]

  • EXFtaux
    (time, tile, j, i)
    float32
    ...

    comment :

    Wind stress in the +x direction at the tracer cell on the native model grid. Note: EXFtaux is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +x direction is NOT EXFtaux, but a combination of EXFtaux wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUX). EXFtaux is the sum of ERA-Interim stress and the control adjustment from ocean state estimation.

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +x direction (UVEL)

    long_name :

    Wind stress in the model +x direction

    standard_name :

    surface_downward_x_stress

    units :

    N m-2

    valid_max :

    1.0077697038650513

    valid_min :

    -1.6020684242248535

    [631800 values with dtype=float32]

  • EXFtauy
    (time, tile, j, i)
    float32
    ...

    comment :

    Wind stress in the +y direction at the tracer cell on the native model grid. Note: EXFtauy is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +y direction is NOT EXFtauy, but a combination of EXFtauy wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUY). EXFtaux is the sum of ERA-Interim stress and the control adjustment from ocean state estimation.

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +y direction (VVEL)

    long_name :

    Wind stress in the model +y direction

    standard_name :

    surface_downward_y_stress

    units :

    N m-2

    valid_max :

    0.7666416764259338

    valid_min :

    -0.7632240653038025

    [631800 values with dtype=float32]

  • oceTAUX
    (time, tile, j, i_g)
    float32
    ...

    comment :

    Ocean surface stress due to wind and sea-ice in the +x direction centered over the 'u' side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUX(i_g,j) corresponds to +x momentum fluxes at 'u' edge of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +x direction (UVEL)

    long_name :

    Ocean surface stress in the model +x direction

    mate :

    oceTAUY

    standard_name :

    downward_x_stress_at_sea_water_surface

    units :

    N m-2

    valid_max :

    0.7922194004058838

    valid_min :

    -0.7581670880317688

    [631800 values with dtype=float32]

  • oceTAUY
    (time, tile, j_g, i)
    float32
    ...

    comment :

    Ocean surface stress due to wind and sea-ice in the +y direction centered over the 'v' side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUY(i_g,j) corresponds to +y momentum fluxes at 'v' edge of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.

    coverage_content_type :

    modelResult

    direction :

    >0 increases horizontal velocity in the +y direction (VVEL)

    long_name :

    Ocean surface stress in the model +y direction

    mate :

    oceTAUX

    standard_name :

    downward_y_stress_at_sea_water_surface

    units :

    N m-2

    valid_max :

    0.6071499586105347

    valid_min :

    -0.7589108347892761

    [631800 values with dtype=float32]

• Indexes: (6)
  • i
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i'))

  • i_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='i_g'))

  • j
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j'))

  • j_g
    PandasIndex

    PandasIndex(Index([ 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],
          dtype='int32', name='j_g'))

  • tile
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int32', name='tile'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2010-01-16 12:00:00', '2010-02-15 00:00:00',
                   '2010-03-16 12:00:00', '2010-04-16 00:00:00',
                   '2010-05-16 12:00:00', '2010-06-16 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (57)

  Conventions :

  CF-1.8, ACDD-1.3

  acknowledgement :

  This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.

  author :

  Ian Fenty and Ou Wang

  cdm_data_type :

  Grid

  comment :

  Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations. Note: ETAN is model sea level anomaly and should not be compared with satellite altimetery products, see SSH and ETAN for more details.

  coordinates_comment :

  Note: the global 'coordinates' attribute describes auxillary coordinates.

  creator_email :

  ecco-group@mit.edu

  creator_institution :

  NASA Jet Propulsion Laboratory (JPL)

  creator_name :

  ECCO Consortium

  creator_type :

  group

  creator_url :

  https://ecco-group.org

  date_created :

  2020-12-16T18:07:10

  date_issued :

  2020-12-16T18:07:10

  date_metadata_modified :

  2021-03-15T21:53:43

  date_modified :

  2021-03-15T21:53:43

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  variable

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  variable

  geospatial_lon_units :

  degrees_east

  history :

  Inaugural release of an ECCO Central Estimate solution to PO.DAAC

  id :

  10.5067/ECL5M-SSH44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > SEA SURFACE TOPOGRAPHY > SEA SURFACE HEIGHT, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Public Domain

  metadata_link :

  https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4

  naming_authority :

  gov.nasa.jpl

  platform :

  ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)

  platform_vocabulary :

  GCMD platform keywords

  processing_level :

  L4

  product_name :

  SEA_SURFACE_HEIGHT_mon_mean_1992-01_ECCO_V4r4_native_llc0090.nc

  product_time_coverage_end :

  2018-01-01T00:00:00

  product_time_coverage_start :

  1992-01-01T12:00:00

  product_version :

  Version 4, Release 4

  program :

  NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)

  project :

  Estimating the Circulation and Climate of the Ocean (ECCO)

  publisher_email :

  podaac@podaac.jpl.nasa.gov

  publisher_institution :

  PO.DAAC

  publisher_name :

  Physical Oceanography Distributed Active Archive Center (PO.DAAC)

  publisher_type :

  institution

  publisher_url :

  https://podaac.jpl.nasa.gov

  references :

  ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928

  source :

  The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  summary :

  This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

  time_coverage_duration :

  P1M

  time_coverage_end :

  1992-02-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  1992-01-01T12:00:00

  title :

  ECCO Sea Surface Height - Monthly Mean llc90 Grid (Version 4 Release 4)

  uuid :

  9302811e-400c-11eb-b69e-0cc47a3f49c3

s3_get mode

The s3_get mode functions much like the download mode, except files are accesed in-cloud and downloading them to your local instance. If used with ecco_podaac_access, a dictionary containing the file paths/names is returned, that can then be used to open an xarray Dataset.

files_dict = ea.ecco_podaac_access([SSH_shortname,windstr_shortname],\
                                    StartDate='2010-01',EndDate='2010-06',\
                                    mode='s3_get',\
                                    download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC_S3'))
files_dict[SSH_shortname]

{'ShortName': 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

Total number of matching granules: 6
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc

=====================================
Time spent = 0.44377636909484863 seconds


{'ShortName': 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

Total number of matching granules: 6
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc

=====================================
Time spent = 0.4701051712036133 seconds

['/home/jovyan/Downloads/ECCO_V4r4_PODAAC_S3/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc',
 '/home/jovyan/Downloads/ECCO_V4r4_PODAAC_S3/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc',
 '/home/jovyan/Downloads/ECCO_V4r4_PODAAC_S3/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc',
 '/home/jovyan/Downloads/ECCO_V4r4_PODAAC_S3/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc',
 '/home/jovyan/Downloads/ECCO_V4r4_PODAAC_S3/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc',
 '/home/jovyan/Downloads/ECCO_V4r4_PODAAC_S3/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc']

# remove downloaded files
for shortname,files_list in files_dict.items():
    for file in files_list:
        os.remove(file)

Now use the mode with ecco_podaac_to_xrdataset:

%%time

ds_dict = ea.ecco_podaac_to_xrdataset([SSH_shortname,windstr_shortname],\
                                        StartDate='2010-01',EndDate='2010-06',\
                                        mode='s3_get',\
                                        download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC_S3'))

{'ShortName': 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

Total number of matching granules: 6
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc
downloading SEA_SURFACE_HEIGHT_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc

=====================================
Time spent = 0.43352532386779785 seconds


{'ShortName': 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}

Total number of matching granules: 6
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-01_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-02_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-03_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-04_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-05_ECCO_V4r4_native_llc0090.nc
downloading OCEAN_AND_ICE_SURFACE_STRESS_mon_mean_2010-06_ECCO_V4r4_native_llc0090.nc

=====================================
Time spent = 0.4638087749481201 seconds


CPU times: user 588 ms, sys: 379 ms, total: 968 ms
Wall time: 4.8 s

# remove downloaded files
for shortname,files_list in files_dict.items():
    for file in files_list:
        os.remove(file)

s3_get_ifspace mode

This mode is similar to s3_get, but it will also query how much storage is available at the target download location before carrying out downloads. If the space to be occupied by the downloaded files is more than a specified fraction of available storage, the files are opened remotely (using s3_open), rather than using s3_get.

%%time

ds_dict = ea.ecco_podaac_to_xrdataset([SSH_shortname,windstr_shortname],\
                                        StartDate='2010-01',EndDate='2010-06',\
                                        mode='s3_get_ifspace',\
                                        download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC_S3'),\
                                        max_avail_frac=0.01)

{'ShortName': 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}
{'ShortName': 'ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2010-01-02,2010-06-30'}
Size of files to be downloaded to instance is 0.066 GB,
which is 1.47% of the 4.5040000000000004 GB available storage.
Download size is larger than specified fraction of available storage.
Generating file lists to open directly from S3.
CPU times: user 3.15 s, sys: 861 ms, total: 4.01 s
Wall time: 12.3 s

Notice that the files were opened remotely on S3 rather than downloaded, but only because max_avail_frac was set very low (0.01 or 1% of available storage). If max_avail_frac was set to its default value of 0.5 (50% of available storage), the files would have been downloaded.

Time comparison of access modes

Based on the examples above, here are the wall times for generating ds_dict for each function (except download_subset which used a different set of data):

• download: 5.52 s

• download_ifspace: comparable to download

• s3_open: 14.1 s

• s3_open_fsspec: 3.34 s

• s3_get: 4.8 s

• s3_get_ifspace: 12.3 s (similar to s3_open because files were opened remotely)

These numbers will vary depending on the size and layout of the dataset(s) requested. But the “winner” is usually s3_open_fsspec, and the notebooks in our tutorial book use this mode by default. (However, changing the access mode is as easy as changing the mode option in the ecco_podaac_to_xrdataset function!)