PyDAP Authentication

📘 Learning Objectives

1. Repeat the NASA notebook but illustrating PyDAP authentication.

Overview

See the 3-nasa.ipynb notebook for the recommended way to authenticate until pydap > 3.5.3 (Jan 2025) is released. This notebook is included to document PyDAP authentication. When the new version is released, this authentication works similar to token-based (meaning works for all cases tested).

import requests
my_session=requests.Session()

PyDAP will automatically look for ~/.netrc file and use that so it may seem that you are not authenticating (if you have that file). But you are. The “silent” method fails if the data require a User Agreement (EULA) that you have to accept on your Earthdata profile because it cannot handle redirects. You can set up a session with username/password which will work for single files with xarray.open_dataset but fails with xarray.open_mfdataset. Setting up a session with a token works in all cases, but is more of a hassle. earthaccess will authenticate from your ~/.netrc file and handle the token for you. So I recommend that approach.

I am going to first show how to set up a token and create a “session” that is passed to xarray. For what I have found (in March 2025), this works to get past “re-direct” errors that arise when the data require a EULA. Tokens also get past the redirect errors from other non-NASA OPeNDAP servers that use NASA EDL (USDA and NSIDC). At the end, I will show how xarray+pydap can automatically use your .netrc file to authenticate, but this won’t work for data that need a EULA so it might be best to use the token method as that will work for data that need a EULA too.

References

• https://podaac.jpl.nasa.gov/OPeNDAP-in-the-Cloud

Prerequisites

Set up a NASA EDL token. Go to https://urs.earthdata.nasa.gov/profile and click on Generate Token. Copy your token (it is exceedingly long) and paste into this code. Run this in a terminal windown (not notebook). Alternatively, open ~/.env and paste in NASA_EDL_TOKEN='your_edl_token_here'. Note the token only last a month, so you will need to refresh your tokens regularly.

echo "NASA_EDL_TOKEN='your_edl_token_here'" >> ~/.env

Now we can load the token into any Jupyter notebook with

from dotenv import load_dotenv
load_dotenv()
edl_token = os.getenv("NASA_EDL_TOKEN")

For the section, where I cover authentication with username and password, I assume you have a .netrc file at ~ (home). ~/.netrc should look just like this with your username and password. Create that file if needed.

machine urs.earthdata.nasa.gov
        login yourusername
        password yourpassword

Packages

import xarray as xr
import pydap.client

Three ways to authenticate with pydap

1. Let pydap find your ~/.netrc file
2. Set up a session with your Earthdata username and password
3. Set up a session with a Earthdata token

I suggest you only use the latter since that works for all cases: data w or wo a EULA, singles files and multiple files, xarray open_dataset and open_mfdataset.

Let pydap use your .netrc file

If the data need Earthdata authentication but don’t need a EULA, then you can let pydap use your .netrc file silently. But this method is the most likely to produce redirect errors and access errors.

The following code works because pydap uses the .netrc file that is in ~/.netrc. It does this silently for you. But this only works because this particular dataset does not require a User Agreement to be accepted on your Earthdata profile.

# doesn't need a eula
url="dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0"
ds = xr.open_dataset(url, engine="pydap", decode_timedelta=False)
ds["wind_speed"].load()

<xarray.DataArray 'wind_speed' (/time: 1, /lat: 2400, /lon: 2400)> Size: 46MB
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ...,  8.,  8.,  8.],
        [nan, nan, nan, ...,  8.,  8., nan]]])
Dimensions without coordinates: /time, /lat, /lon
Attributes:
    long_name:      10m wind speed
    standard_name:  wind_speed
    units:          m s-1
    height:         10 m
    valid_min:      0
    valid_max:      127
    time_offset:    0.0
    source:         WSP-ECMWF-Forecast
    comment:        These wind speeds were created by the ECMWF and represent...
    Maps:           ()

xarray.DataArray

'wind_speed'

• /time: 1
• /lat: 2400
• /lon: 2400

• nan nan nan nan nan nan nan nan ... 8.0 8.0 8.0 8.0 8.0 8.0 8.0 nan

  array([[[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ...,  8.,  8.,  8.],
          [nan, nan, nan, ...,  8.,  8., nan]]])

• Coordinates: (0)
• Indexes: (0)
• Attributes: (10)

  long_name :

  10m wind speed

  standard_name :

  wind_speed

  units :

  m s-1

  height :

  10 m

  valid_min :

  0

  valid_max :

  127

  time_offset :

  0.0

  source :

  WSP-ECMWF-Forecast

  comment :

  These wind speeds were created by the ECMWF and represent winds at 10 metres above the sea surface.

  Maps :

  ()

Set up a session with username and password

Using the session argument with pydap is more robust because it will open data that require a EULA. You can set up a session like this with your username and password in your .netrc file. This mostly works but it fails when using xarray.open_mfdataset() with data that require a EULA. So using a token is better though more of a hassle.

import netrc
# Get credentials from .netrc
auth_host = "urs.earthdata.nasa.gov"
try:
    credentials = netrc.netrc().authenticators(auth_host)
    if credentials:
        username, _, password = credentials
    else:
        raise ValueError("No credentials found in .netrc!")
except FileNotFoundError:
    raise FileNotFoundError("Could not find ~/.netrc. Ensure it exists and is configured correctly.")

import pydap
from pydap.client import open_url
from pydap.cas.urs import setup_session
pw_session = setup_session(username, password)

Set up a session with a EDL token (pydap = 3.5.3)

This is the robust way to set up an authenticated session. Note see the notebook where I authenticate with earthaccess, which makes this easier and you don’t need to save anything to .env.

Load the token. Make sure you have your token set up in your ~/.env file. See prerequisites.

from dotenv import load_dotenv
import os
load_dotenv()
edl_token = os.getenv("NASA_EDL_TOKEN")

Next we set up a NASA EDL session with this token.

import requests
auth_hdr="Bearer " + edl_token
token_session = requests.Session()
token_session.headers={"Authorization": auth_hdr}

If pydap version > 3.5.3

If you have pydap version > 3.5.3, then you do not need to generate your own token. You can use your .netrc file (with EDL username and password) and use this:

# uncomment to use
#import requests
#token_session=requests.Session()

For the rest of the notebook, the token_session will be used.

Load non-EULA data

# doesn't point to specific file but we will spec protocol
url="https://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0"

# this is another test url that works for this example
# url="https://opendap.earthdata.nasa.gov/hyrax/data/nc/fnoc1.nc"

%%time
#from pydap.client import open_url
pydap_ds = pydap.client.open_url(url, protocol="dap4", session=token_session)

CPU times: user 13 ms, sys: 11.9 ms, total: 24.9 ms
Wall time: 744 ms

%%time
# getting the data does work
test_ds = pydap_ds['lat'][:]

CPU times: user 18 ms, sys: 628 µs, total: 18.6 ms
Wall time: 2.67 s

ds = xr.open_dataset(
    url.replace("https", "dap4", 1), engine="pydap", 
    decode_cf=False, session=token_session)

ds["wind_speed"].plot();

/srv/conda/envs/notebook/lib/python3.12/site-packages/xarray/plot/utils.py:260: RuntimeWarning: overflow encountered in scalar absolute
  vlim = max(abs(vmin - center), abs(vmax - center))

%%time
# This is doing the same thing as engine="pydap" I think but I don't have to know the dap4 replacement
# ds created is the same though
store = xr.backends.PydapDataStore(pydap_ds)
ds = xr.open_dataset(store, decode_cf=False)

CPU times: user 2.11 ms, sys: 0 ns, total: 2.11 ms
Wall time: 2.11 ms

Data with a EULA

Letting pydap silently use your .netrc fails (redirect error) when there is a EULA and you have accepted the EULA. You need to set up a session with username and password or with a token.

Prerequisites

Make sure you have the GESDISC EULA accepted.

1. Log into https://urs.earthdata.nasa.gov
2. Then go here https://urs.earthdata.nasa.gov/profile
3. Then click EULAs
4. Go to unaccepted EULAs and make sure that GESDISC is accepted

# the GESDISC data requires a EULA
eula_url = 'https://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2T1NXSLV.5.12.4/2016/06/MERRA2_400.tavg1_2d_slv_Nx.20160601.nc4'

%%time
import pydap
from pydap.client import open_url
pydap_ds = open_url(eula_url, session=token_session, protocol="dap4")

CPU times: user 41.3 ms, sys: 3.79 ms, total: 45.1 ms
Wall time: 1.58 s

# works when we try to get data
test_ds = pydap_ds['lat'][:]

%%time
# this works but is a lot of syntax to remember
ds = xr.open_dataset(eula_url.replace("https", "dap4", 1), 
                     engine="pydap", decode_cf=False, 
                     session=token_session)

CPU times: user 42.4 ms, sys: 3.77 ms, total: 46.2 ms
Wall time: 1.08 s

# yeah! I can get the data
ds = ds.rename({"/time": "time", "/lat": "lat", "/lon": "lon"})
ds["T2M"].isel(time=1).plot();

%%time
import xarray as xr
# This works too but is so much slower
store = xr.backends.PydapDataStore(pydap_ds)
ds = xr.open_dataset(store)

CPU times: user 35.4 ms, sys: 11.5 ms, total: 46.9 ms
Wall time: 2.47 s

# I have to rename variables
ds = ds.rename({"/time": "time", "/lat": "lat", "/lon": "lon"})
ds["T2M"].isel(time=1).plot();

Creating data cubes

Our goal is not individual files rather data cubes from multiple files. We will use xarray.open_mfdataset but we need to make some tweaks.

1. We need to use dap4 instead of https. I don’t know what to do if that doesn’t work.
2. We need to concatenate using \time not time since the dim name has that slash in it.

Example 1 Non-EULA data

# doesn't point to specific file but we will spec protocol
url1="dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0"
url2="dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220813010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220813_010000-v02.0-fv01.0"
urls = [url1, url2]
urls

['dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0',
 'dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220813010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220813_010000-v02.0-fv01.0']

ds = xr.open_mfdataset(urls, engine="pydap", 
                       combine="nested", 
                       concat_dim="/time", decode_cf=False,
                      session=token_session)
ds

<xarray.Dataset> Size: 288MB
Dimensions:                    (/time: 2, /lat: 2400, /lon: 2400)
Dimensions without coordinates: /time, /lat, /lon
Data variables: (12/19)
    wind_speed                 (/time, /lat, /lon) int8 12MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    lon                        (/time, /lon) float32 19kB dask.array<chunksize=(1, 2400), meta=np.ndarray>
    or_longitude               (/time, /lat, /lon) int16 23MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    time                       (/time) int32 8B dask.array<chunksize=(1,), meta=np.ndarray>
    sses_standard_deviation    (/time, /lat, /lon) int8 12MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    sst_dtime                  (/time, /lat, /lon) int32 46MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    ...                         ...
    dt_analysis                (/time, /lat, /lon) int8 12MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    satellite_zenith_angle     (/time, /lat, /lon) int8 12MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    lat                        (/time, /lat) float32 19kB dask.array<chunksize=(1, 2400), meta=np.ndarray>
    adi_dtime_from_sst         (/time, /lat, /lon) int8 12MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    or_latitude                (/time, /lat, /lon) int16 23MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
    sses_bias                  (/time, /lat, /lon) int8 12MB dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>
Attributes: (12/53)
    Conventions:                CF-1.4
    title:                      Sea Surface Temperature
    summary:                    The L3C product derived from GOES16/ABI brigh...
    references:                 Geostationary Sea Surface Temperature Product...
    institution:                OSISAF
    comment:                    None
    ...                         ...
    netcdf_version_id:          4.6.3
    build_dmrpp:                3.20.9-91
    bes:                        3.20.9-91
    libdap:                     libdap-3.20.8-41
    configuration:              \n# TheBESKeys::get_as_config()\nAllowedHosts...
    invocation:                 build_dmrpp -c /tmp/conf_GGue -f /tmp/tmph648...

xarray.Dataset

• Dimensions:
  • /time: 2
  • /lat: 2400
  • /lon: 2400
• Coordinates: (0)
• Data variables: (19)
  • wind_speed
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    10m wind speed

    standard_name :

    wind_speed

    units :

    m s-1

    height :

    10 m

    add_offset :

    0.0

    scale_factor :

    1.0

    valid_min :

    0

    valid_max :

    127

    time_offset :

    0.0

    source :

    WSP-ECMWF-Forecast

    comment :

    These wind speeds were created by the ECMWF and represent winds at 10 metres above the sea surface.

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • lon
    (/time, /lon)
    float32
    dask.array<chunksize=(1, 2400), meta=np.ndarray>

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    geographical coordinates, WGS84 projection

    Maps :

    ()

    Array Chunk Bytes 18.75 kiB 9.38 kiB Shape (2, 2400) (1, 2400) Dask graph 2 chunks in 7 graph layers Data type float32 numpy.ndarray

  • or_longitude
    (/time, /lat, /lon)
    int16
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -32768

    long_name :

    original longitude of the SST value

    standard_name :

    longitude

    units :

    degrees_east

    add_offset :

    0.0

    scale_factor :

    0.01

    valid_min :

    -18000

    valid_max :

    18000

    comment :

    Original longitude of the SST value

    Maps :

    ()

    Array Chunk Bytes 21.97 MiB 10.99 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int16 numpy.ndarray

  • time
    (/time)
    int32
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    reference time of sst file

    standard_name :

    time

    axis :

    T

    units :

    seconds since 1981-01-01 00:00:00

    Maps :

    ()

    Array Chunk Bytes 8 B 4 B Shape (2,) (1,) Dask graph 2 chunks in 5 graph layers Data type int32 numpy.ndarray

  • sses_standard_deviation
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    SSES standard deviation

    units :

    kelvin

    add_offset :

    1.0

    scale_factor :

    0.01

    valid_min :

    -127

    valid_max :

    127

    comment :

    Standard deviation estimate derived using the techniques described at http://www.ghrsst.org/SSES-Description-of-schemes.html

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • sst_dtime
    (/time, /lat, /lon)
    int32
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -2147483648

    long_name :

    time difference from reference time

    units :

    seconds

    add_offset :

    0.0

    scale_factor :

    1.0

    valid_min :

    -2147483647

    valid_max :

    2147483647

    comment :

    time plus sst_dtime gives seconds after 00:00:00 UTC January 1, 1981

    Maps :

    ()

    Array Chunk Bytes 43.95 MiB 21.97 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int32 numpy.ndarray

  • solar_zenith_angle
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    solar zenith angle

    units :

    angular_degree

    add_offset :

    90.0

    scale_factor :

    1.0

    valid_min :

    -90

    valid_max :

    90

    comment :

    The solar zenith angle at the time of the SST observations.

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • sea_ice_fraction
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    sea ice fraction

    standard_name :

    sea_ice_area_fraction

    units :

    None

    add_offset :

    0.0

    scale_factor :

    0.01

    valid_min :

    0

    valid_max :

    100

    time_offset :

    0.0

    source :

    ICE-OSISAF

    comment :

    Fractional sea ice cover from OSISAF ice product

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • l2p_flags
    (/time, /lat, /lon)
    int32
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    long_name :

    L2P flags

    valid_min :

    0

    valid_max :

    15

    flag_meanings :

    microwave land ice lake

    flag_masks :

    [1, 2, 4, 8]

    comment :

    These flags are important to properly use the data.

    Maps :

    ()

    Array Chunk Bytes 43.95 MiB 21.97 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int32 numpy.ndarray

  • sources_of_adi
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    sources of aerosol dynamic indicator

    valid_min :

    0

    valid_max :

    2

    flag_meanings :

    no_data AOD-NAAPS-ADI SDI-OSISAF-ADI

    flag_values :

    [0, 1, 2]

    comment :

    This variable provides a pixel by pixel description of where aerosol optical depth were derived from.

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • aerosol_dynamic_indicator
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    aerosol dynamic indicator

    units :

    None

    add_offset :

    0.0

    scale_factor :

    0.1

    valid_min :

    0

    valid_max :

    127

    source :

    sources_of_adi

    comment :

    None

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • sea_surface_temperature
    (/time, /lat, /lon)
    int16
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -32768

    long_name :

    sea surface subskin temperature

    standard_name :

    sea_surface_subskin_temperature

    units :

    kelvin

    add_offset :

    273.15

    scale_factor :

    0.01

    valid_min :

    -300

    valid_max :

    4500

    depth :

    1 millimeter

    source :

    GOES_Imager

    comment :

    Temperature of the subskin of the ocean

    Maps :

    ()

    Array Chunk Bytes 21.97 MiB 10.99 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int16 numpy.ndarray

  • quality_level
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    quality level of SST pixel

    valid_min :

    0

    valid_max :

    5

    flag_meanings :

    no_data bad_data worst_quality low_quality acceptable_quality best_quality

    flag_values :

    [0, 1, 2, 3, 4, 5]

    comment :

    These are the overall quality indicators and are used for all GHRSST SSTs

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • dt_analysis
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    deviation from SST analysis or reference climatology

    units :

    kelvin

    add_offset :

    0.0

    scale_factor :

    0.1

    valid_min :

    -127

    valid_max :

    127

    reference :

    OSTIA

    comment :

    The difference between this SST and the previous day's SST analysis

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • satellite_zenith_angle
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    satellite zenith angle

    units :

    angular_degree

    add_offset :

    0.0

    scale_factor :

    1.0

    valid_min :

    -90

    valid_max :

    90

    comment :

    The satellite zenith angle at the time of the SST observations.

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • lat
    (/time, /lat)
    float32
    dask.array<chunksize=(1, 2400), meta=np.ndarray>

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    comment :

    geographical coordinates, WGS84 projection

    Maps :

    ()

    Array Chunk Bytes 18.75 kiB 9.38 kiB Shape (2, 2400) (1, 2400) Dask graph 2 chunks in 7 graph layers Data type float32 numpy.ndarray

  • adi_dtime_from_sst
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    time difference of ADI data from sst measurement

    units :

    hour

    add_offset :

    0.0

    scale_factor :

    0.1

    valid_min :

    -127

    valid_max :

    127

    comment :

    Difference in hours between the ADI and SST data

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

  • or_latitude
    (/time, /lat, /lon)
    int16
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -32768

    long_name :

    original latitude of the SST value

    standard_name :

    latitude

    units :

    degrees_north

    add_offset :

    0.0

    scale_factor :

    0.01

    valid_min :

    -9000

    valid_max :

    9000

    comment :

    Original latitude of the SST value

    Maps :

    ()

    Array Chunk Bytes 21.97 MiB 10.99 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int16 numpy.ndarray

  • sses_bias
    (/time, /lat, /lon)
    int8
    dask.array<chunksize=(1, 2400, 2400), meta=np.ndarray>

    _FillValue :

    -128

    long_name :

    SSES bias estimate

    units :

    kelvin

    add_offset :

    0.0

    scale_factor :

    0.01

    valid_min :

    -127

    valid_max :

    127

    comment :

    Bias estimate derived using the techniques described at http://www.ghrsst.org/SSES-Description-of-schemes.html

    Maps :

    ()

    Array Chunk Bytes 10.99 MiB 5.49 MiB Shape (2, 2400, 2400) (1, 2400, 2400) Dask graph 2 chunks in 5 graph layers Data type int8 numpy.ndarray

• Indexes: (0)
• Attributes: (53)

  Conventions :

  CF-1.4

  title :

  Sea Surface Temperature

  summary :

  The L3C product derived from GOES16/ABI brightness temperatures.

  references :

  Geostationary Sea Surface Temperature Product User Manual, http://www.osi-saf.org

  institution :

  OSISAF

  comment :

  None

  license :

  All intellectual property rights of the Ocean & Sea Ice SAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words 'Copyright EUMETSAT' under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the Ocean & Sea Ice SAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the Ocean & Sea Ice SAF products.

  id :

  GOES16-OSISAF-L3C-v1.0

  product_id :

  OSI-207-b

  naming_authority :

  org.ghrsst

  product_version :

  1.0

  gds_version_id :

  2.0

  file_quality_level :

  3

  spatial_resolution :

  0.05 degree

  northernmost_latitude :

  60.0

  southernmost_latitude :

  -60.0

  easternmost_longitude :

  -15.0

  westernmost_longitude :

  -135.0

  source :

  GOES_ABI

  platform :

  GOES16

  sensor :

  GOES_ABI

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  metadata_link :

  N/A

  keywords :

  Oceans > Ocean Temperature > Sea Surface Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  0.05

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  0.05

  acknowledgment :

  In case SAF data (pre-operational or operational) has been used for the study described in a paper the following sentence would be an appropriate reference to the funding coming from EUMETSAT: The data from the EUMETSAT Satellite Application Facility on Ocean & Sea Ice used in this study are accessible through the SAF's homepage http://www.osi-saf.org

  creator_name :

  O&SI SAF

  creator_email :

  osi-saf.helpdesk@meteo.fr

  creator_url :

  http://www.osi-saf.org

  project :

  Group for High Resolution Sea Surface Temperature

  publisher_name :

  The GHRSST Project Office

  publisher_url :

  http://www.ghrsst.org

  publisher_email :

  ghrsst-po@nceo.ac.uk

  processing_level :

  L3C

  cdm_data_type :

  grid

  history :

  METEO-FRANCE GEOSAFO v1.1.8

  uuid :

  DF556788-19E1-11ED-A08A-48DF370DAD10

  date_created :

  20220812T015542Z

  start_time :

  20220812T004042Z

  time_coverage_start :

  20220812T004042Z

  stop_time :

  20220812T011929Z

  time_coverage_end :

  20220812T011929Z

  netcdf_version_id :

  4.6.3

  build_dmrpp :

  3.20.9-91

  bes :

  3.20.9-91

  libdap :

  libdap-3.20.8-41

  configuration :

  # TheBESKeys::get_as_config() AllowedHosts=^https?:\/\/ BES.Catalog.catalog.Exclude=^\..*; BES.Catalog.catalog.FollowSymLinks=Yes BES.Catalog.catalog.Include=; BES.Catalog.catalog.RootDirectory=/tmp/tmph648lz17/ BES.Catalog.catalog.TypeMatch=dmrpp:.*\.(dmrpp)$; BES.Catalog.catalog.TypeMatch+=h5:.*(\.bz2|\.gz|\.Z)?$; BES.Container.Persistence=strict BES.Data.RootDirectory=/dev/null BES.DefaultResponseMethod=POST BES.FollowSymLinks=Yes BES.Group=group_name BES.Info.Buffered=no BES.Info.Type=xml BES.LogName=./bes.log BES.LogVerbose=no BES.Memory.GlobalArea.ControlHeap=no BES.Memory.GlobalArea.EmergencyPoolSize=1 BES.Memory.GlobalArea.MaximumHeapSize=20 BES.Memory.GlobalArea.Verbose=no BES.ProcessManagerMethod=multiple BES.ServerAdministrator=admin.email.address@your.domain.name BES.Uncompress.NumTries=10 BES.Uncompress.Retry=2000 BES.UncompressCache.dir=/tmp/hyrax_ux BES.UncompressCache.prefix=ux_ BES.UncompressCache.size=500 BES.User=user_name BES.module.cmd=/usr/lib64/bes/libdap_xml_module.so BES.module.dap=/usr/lib64/bes/libdap_module.so BES.module.dmrpp=/usr/lib64/bes/libdmrpp_module.so BES.module.fonc=/usr/lib64/bes/libfonc_module.so BES.module.h5=/usr/lib64/bes/libhdf5_module.so BES.module.nc=/usr/lib64/bes/libnc_module.so BES.modules=dap,cmd,h5,dmrpp,nc,fonc FONc.ClassicModel=false FONc.NoGlobalAttrs=true H5.Cache.latlon.path=/tmp/latlon H5.Cache.latlon.prefix=l H5.Cache.latlon.size=20000 H5.CheckIgnoreObj=false H5.DefaultHandleDimension=true H5.DisableStructMetaAttr=true H5.DiskCacheComp=true H5.DiskCacheCompThreshold=2.0 H5.DiskCacheCompVarSize=10000 H5.DiskCacheDataPath=/tmp H5.DiskCacheFilePrefix=c H5.DiskCacheFloatOnlyComp=true H5.DiskCacheSize=10000 H5.DiskMetaDataCachePath=/tmp H5.EnableAddPathAttrs=true H5.EnableCF=false H5.EnableCFDMR=true H5.EnableCheckNameClashing=true H5.EnableDiskDDSCache=false H5.EnableDiskDataCache=false H5.EnableDiskMetaDataCache=false H5.EnableDropLongString=true H5.EnableEOSGeoCacheFile=false H5.EnableFillValueCheck=true H5.KeepVarLeadingUnderscore=false H5.LargeDataMemCacheEntries=0 H5.MetaDataMemCacheEntries=300 H5.SmallDataMemCacheEntries=0

  invocation :

  build_dmrpp -c /tmp/conf_GGue -f /tmp/tmph648lz17//20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0.nc -r /tmp/dmr_HJF1 -u OPeNDAP_DMRpp_DATA_ACCESS_URL -M

Let’s do some slicing and see how fast we can get the data.

ds = ds.rename({"/time": "time", "/lat": "lat", "/lon": "lon"})

%%time
test = ds["wind_speed"].isel(time=1).load()

CPU times: user 32.1 ms, sys: 36 ms, total: 68.1 ms
Wall time: 6.08 s

Example 2 EULA data

token_session is critical so that things don’t go south with re-directs.

eula_url1 = 'dap4://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2T1NXSLV.5.12.4/2016/06/MERRA2_400.tavg1_2d_slv_Nx.20160601.nc4'
eula_url2 = 'dap4://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2T1NXSLV.5.12.4/2016/06/MERRA2_400.tavg1_2d_slv_Nx.20160602.nc4'
eula_urls = [eula_url1, eula_url2]

%%time
import xarray as xr
# very fast and this is 1Tb of data
ds = xr.open_mfdataset(eula_urls, engine="pydap", 
                       combine="nested", concat_dim="/time", 
                       decode_cf=False, session=token_session)
ds

CPU times: user 141 ms, sys: 12 ms, total: 153 ms
Wall time: 2.24 s

<xarray.Dataset> Size: 2GB
Dimensions:   (/time: 48, /lat: 361, /lon: 576)
Dimensions without coordinates: /time, /lat, /lon
Data variables: (12/50)
    U2M       (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    V250      (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    TROPT     (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    TROPPB    (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    T2M       (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    TQL       (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    ...        ...
    T2MWET    (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    U500      (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    QV10M     (/time, /lat, /lon) float32 40MB dask.array<chunksize=(24, 361, 576), meta=np.ndarray>
    lat       (/time, /lat) float64 139kB dask.array<chunksize=(24, 361), meta=np.ndarray>
    lon       (/time, /lon) float64 221kB dask.array<chunksize=(24, 576), meta=np.ndarray>
    time      (/time) int32 192B dask.array<chunksize=(24,), meta=np.ndarray>
Attributes: (12/31)
    History:                           Original file generated: Tue Jun 14 18...
    Comment:                           GMAO filename: d5124_m2_jan10.tavg1_2d...
    Filename:                          MERRA2_400.tavg1_2d_slv_Nx.20160601.nc4
    Conventions:                       CF-1
    Institution:                       NASA Global Modeling and Assimilation ...
    References:                        http://gmao.gsfc.nasa.gov
    ...                                ...
    identifier_product_doi:            10.5067/VJAFPLI1CSIV
    RangeBeginningDate:                2016-06-01
    RangeBeginningTime:                00:00:00.000000
    RangeEndingDate:                   2016-06-01
    RangeEndingTime:                   23:59:59.000000
    Unlimited_Dimension:               time

xarray.Dataset

• Dimensions:
  • /time: 48
  • /lat: 361
  • /lon: 576
• Coordinates: (0)
• Data variables: (50)
  • U2M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    2-meter_eastward_wind

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    2-meter_eastward_wind

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    U2M

    fullnamepath :

    /U2M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • V250
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    northward_wind_at_250_hPa

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    northward_wind_at_250_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    V250

    fullnamepath :

    /V250

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TROPT
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    tropopause_temperature_using_blended_TROPP_estimate

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    tropopause_temperature_using_blended_TROPP_estimate

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TROPT

    fullnamepath :

    /TROPT

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TROPPB
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    tropopause_pressure_based_on_blended_estimate

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    tropopause_pressure_based_on_blended_estimate

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TROPPB

    fullnamepath :

    /TROPPB

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T2M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    2-meter_air_temperature

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    2-meter_air_temperature

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T2M

    fullnamepath :

    /T2M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TQL
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    total_precipitable_liquid_water

    units :

    kg m-2

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    total_precipitable_liquid_water

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TQL

    fullnamepath :

    /TQL

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T500
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    air_temperature_at_500_hPa

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    air_temperature_at_500_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T500

    fullnamepath :

    /T500

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TOX
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    total_column_odd_oxygen

    units :

    kg m-2

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    total_column_odd_oxygen

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TOX

    fullnamepath :

    /TOX

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • U850
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    eastward_wind_at_850_hPa

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    eastward_wind_at_850_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    U850

    fullnamepath :

    /U850

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • PS
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    surface_pressure

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    surface_pressure

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    PS

    fullnamepath :

    /PS

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • V850
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    northward_wind_at_850_hPa

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    northward_wind_at_850_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    V850

    fullnamepath :

    /V850

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • OMEGA500
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    omega_at_500_hPa

    units :

    Pa s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    omega_at_500_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    OMEGA500

    fullnamepath :

    /OMEGA500

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • H250
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    height_at_250_hPa

    units :

    m

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    height_at_250_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    H250

    fullnamepath :

    /H250

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Q250
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    specific_humidity_at_250_hPa

    units :

    kg kg-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    specific_humidity_at_250_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    Q250

    fullnamepath :

    /Q250

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T2MDEW
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    dew_point_temperature_at_2_m

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    dew_point_temperature_at_2_m

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T2MDEW

    fullnamepath :

    /T2MDEW

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • PBLTOP
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    pbltop_pressure

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    pbltop_pressure

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    PBLTOP

    fullnamepath :

    /PBLTOP

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • CLDPRS
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    cloud_top_pressure

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    cloud_top_pressure

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    CLDPRS

    fullnamepath :

    /CLDPRS

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • V50M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    northward_wind_at_50_meters

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    northward_wind_at_50_meters

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    V50M

    fullnamepath :

    /V50M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Q500
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    specific_humidity_at_500_hPa

    units :

    kg kg-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    specific_humidity_at_500_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    Q500

    fullnamepath :

    /Q500

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • DISPH
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    zero_plane_displacement_height

    units :

    m

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    zero_plane_displacement_height

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    DISPH

    fullnamepath :

    /DISPH

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • H1000
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    height_at_1000_mb

    units :

    m

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    height_at_1000_mb

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    H1000

    fullnamepath :

    /H1000

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TO3
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    total_column_ozone

    units :

    Dobsons

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    total_column_ozone

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TO3

    fullnamepath :

    /TO3

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TS
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    surface_skin_temperature

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    surface_skin_temperature

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TS

    fullnamepath :

    /TS

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T10M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    10-meter_air_temperature

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    10-meter_air_temperature

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T10M

    fullnamepath :

    /T10M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TROPPT
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    tropopause_pressure_based_on_thermal_estimate

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    tropopause_pressure_based_on_thermal_estimate

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TROPPT

    fullnamepath :

    /TROPPT

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TQI
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    total_precipitable_ice_water

    units :

    kg m-2

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    total_precipitable_ice_water

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TQI

    fullnamepath :

    /TQI

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • SLP
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    sea_level_pressure

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    sea_level_pressure

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    SLP

    fullnamepath :

    /SLP

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • U250
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    eastward_wind_at_250_hPa

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    eastward_wind_at_250_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    U250

    fullnamepath :

    /U250

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Q850
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    specific_humidity_at_850_hPa

    units :

    kg kg-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    specific_humidity_at_850_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    Q850

    fullnamepath :

    /Q850

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • ZLCL
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    lifting_condensation_level

    units :

    m

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    lifting_condensation_level

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    ZLCL

    fullnamepath :

    /ZLCL

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TQV
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    total_precipitable_water_vapor

    units :

    kg m-2

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    total_precipitable_water_vapor

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TQV

    fullnamepath :

    /TQV

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • V2M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    2-meter_northward_wind

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    2-meter_northward_wind

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    V2M

    fullnamepath :

    /V2M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T250
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    air_temperature_at_250_hPa

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    air_temperature_at_250_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T250

    fullnamepath :

    /T250

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TROPQ
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    tropopause_specific_humidity_using_blended_TROPP_estimate

    units :

    kg kg-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    tropopause_specific_humidity_using_blended_TROPP_estimate

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TROPQ

    fullnamepath :

    /TROPQ

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • V10M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    10-meter_northward_wind

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    10-meter_northward_wind

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    V10M

    fullnamepath :

    /V10M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • H850
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    height_at_850_hPa

    units :

    m

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    height_at_850_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    H850

    fullnamepath :

    /H850

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T850
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    air_temperature_at_850_hPa

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    air_temperature_at_850_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T850

    fullnamepath :

    /T850

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • U50M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    eastward_wind_at_50_meters

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    eastward_wind_at_50_meters

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    U50M

    fullnamepath :

    /U50M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • U10M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    10-meter_eastward_wind

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    10-meter_eastward_wind

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    U10M

    fullnamepath :

    /U10M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • QV2M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    2-meter_specific_humidity

    units :

    kg kg-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    2-meter_specific_humidity

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    QV2M

    fullnamepath :

    /QV2M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • CLDTMP
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    cloud_top_temperature

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    cloud_top_temperature

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    CLDTMP

    fullnamepath :

    /CLDTMP

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • TROPPV
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    tropopause_pressure_based_on_EPV_estimate

    units :

    Pa

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    tropopause_pressure_based_on_EPV_estimate

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    TROPPV

    fullnamepath :

    /TROPPV

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • H500
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    height_at_500_hPa

    units :

    m

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    height_at_500_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    H500

    fullnamepath :

    /H500

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • V500
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    northward_wind_at_500_hPa

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    northward_wind_at_500_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    V500

    fullnamepath :

    /V500

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • T2MWET
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    wet_bulb_temperature_at_2_m

    units :

    K

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    wet_bulb_temperature_at_2_m

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    T2MWET

    fullnamepath :

    /T2MWET

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • U500
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    eastward_wind_at_500_hPa

    units :

    m s-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    eastward_wind_at_500_hPa

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    U500

    fullnamepath :

    /U500

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • QV10M
    (/time, /lat, /lon)
    float32
    dask.array<chunksize=(24, 361, 576), meta=np.ndarray>

    long_name :

    10-meter_specific_humidity

    units :

    kg kg-1

    _FillValue :

    999999987000000.0

    missing_value :

    999999987000000.0

    fmissing_value :

    999999987000000.0

    scale_factor :

    1.0

    add_offset :

    0.0

    standard_name :

    10-meter_specific_humidity

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    QV10M

    fullnamepath :

    /QV10M

    Maps :

    ()

    Array Chunk Bytes 38.07 MiB 19.04 MiB Shape (48, 361, 576) (24, 361, 576) Dask graph 2 chunks in 5 graph layers Data type float32 numpy.ndarray

  • lat
    (/time, /lat)
    float64
    dask.array<chunksize=(24, 361), meta=np.ndarray>

    long_name :

    latitude

    units :

    degrees_north

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    lat

    fullnamepath :

    /lat

    Maps :

    ()

    Array Chunk Bytes 135.38 kiB 67.69 kiB Shape (48, 361) (24, 361) Dask graph 2 chunks in 7 graph layers Data type float64 numpy.ndarray

  • lon
    (/time, /lon)
    float64
    dask.array<chunksize=(24, 576), meta=np.ndarray>

    long_name :

    longitude

    units :

    degrees_east

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    lon

    fullnamepath :

    /lon

    Maps :

    ()

    Array Chunk Bytes 216.00 kiB 108.00 kiB Shape (48, 576) (24, 576) Dask graph 2 chunks in 7 graph layers Data type float64 numpy.ndarray

  • time
    (/time)
    int32
    dask.array<chunksize=(24,), meta=np.ndarray>

    long_name :

    time

    units :

    minutes since 2016-06-01 00:30:00

    time_increment :

    10000

    begin_date :

    20160601

    begin_time :

    3000

    vmax :

    999999987000000.0

    vmin :

    -999999987000000.0

    valid_range :

    [-999999987000000.0, 999999987000000.0]

    origname :

    time

    fullnamepath :

    /time

    Maps :

    ()

    Array Chunk Bytes 192 B 96 B Shape (48,) (24,) Dask graph 2 chunks in 5 graph layers Data type int32 numpy.ndarray

• Indexes: (0)
• Attributes: (31)

  History :

  Original file generated: Tue Jun 14 18:02:46 2016 GMT

  Comment :

  GMAO filename: d5124_m2_jan10.tavg1_2d_slv_Nx.20160601.nc4

  Filename :

  MERRA2_400.tavg1_2d_slv_Nx.20160601.nc4

  Conventions :

  CF-1

  Institution :

  NASA Global Modeling and Assimilation Office

  References :

  http://gmao.gsfc.nasa.gov

  Format :

  NetCDF-4/HDF-5

  SpatialCoverage :

  global

  VersionID :

  5.12.4

  TemporalRange :

  1980-01-01 -> 2016-12-31

  identifier_product_doi_authority :

  http://dx.doi.org/

  ShortName :

  M2T1NXSLV

  GranuleID :

  MERRA2_400.tavg1_2d_slv_Nx.20160601.nc4

  ProductionDateTime :

  Original file generated: Tue Jun 14 18:02:46 2016 GMT

  LongName :

  MERRA2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics

  Title :

  MERRA2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics

  SouthernmostLatitude :

  -90.0

  NorthernmostLatitude :

  90.0

  WesternmostLongitude :

  -180.0

  EasternmostLongitude :

  179.375

  LatitudeResolution :

  0.5

  LongitudeResolution :

  0.625

  DataResolution :

  0.5 x 0.625

  Source :

  CVS tag: GEOSadas-5_12_4_p5

  Contact :

  http://gmao.gsfc.nasa.gov

  identifier_product_doi :

  10.5067/VJAFPLI1CSIV

  RangeBeginningDate :

  2016-06-01

  RangeBeginningTime :

  00:00:00.000000

  RangeEndingDate :

  2016-06-01

  RangeEndingTime :

  23:59:59.000000

  Unlimited_Dimension :

  time

print(f"Dataset size: {ds.nbytes/1e6:.2f} MB")

Dataset size: 1876.77 MB

# We can load the data
ds = ds.rename({"/time": "time", "/lat": "lat", "/lon": "lon"})
ds["T2M"].isel(time=1).load()

<xarray.DataArray 'T2M' (lat: 361, lon: 576)> Size: 832kB
array([[218.05759, 218.05759, 218.05759, ..., 218.05759, 218.05759,
        218.05759],
       [217.35446, 217.35446, 217.33884, ..., 217.38571, 217.38571,
        217.37009],
       [217.10446, 217.10446, 217.10446, ..., 217.10446, 217.10446,
        217.10446],
       ...,
       [266.9951 , 266.98727, 266.97946, ..., 267.01462, 267.0068 ,
        266.999  ],
       [268.10446, 268.10446, 268.10446, ..., 268.10056, 268.10056,
        268.10446],
       [269.124  , 269.124  , 269.124  , ..., 269.124  , 269.124  ,
        269.124  ]], dtype=float32)
Dimensions without coordinates: lat, lon
Attributes: (12/14)
    long_name:       2-meter_air_temperature
    units:           K
    _FillValue:      999999987000000.0
    missing_value:   999999987000000.0
    fmissing_value:  999999987000000.0
    scale_factor:    1.0
    ...              ...
    vmax:            999999987000000.0
    vmin:            -999999987000000.0
    valid_range:     [-999999987000000.0, 999999987000000.0]
    origname:        T2M
    fullnamepath:    /T2M
    Maps:            ()

xarray.DataArray

'T2M'

• lat: 361
• lon: 576

• 218.1 218.1 218.1 218.1 218.1 218.1 ... 269.1 269.1 269.1 269.1 269.1

  array([[218.05759, 218.05759, 218.05759, ..., 218.05759, 218.05759,
          218.05759],
         [217.35446, 217.35446, 217.33884, ..., 217.38571, 217.38571,
          217.37009],
         [217.10446, 217.10446, 217.10446, ..., 217.10446, 217.10446,
          217.10446],
         ...,
         [266.9951 , 266.98727, 266.97946, ..., 267.01462, 267.0068 ,
          266.999  ],
         [268.10446, 268.10446, 268.10446, ..., 268.10056, 268.10056,
          268.10446],
         [269.124  , 269.124  , 269.124  , ..., 269.124  , 269.124  ,
          269.124  ]], dtype=float32)

• Coordinates: (0)
• Indexes: (0)
• Attributes: (14)

  long_name :

  2-meter_air_temperature

  units :

  K

  _FillValue :

  999999987000000.0

  missing_value :

  999999987000000.0

  fmissing_value :

  999999987000000.0

  scale_factor :

  1.0

  add_offset :

  0.0

  standard_name :

  2-meter_air_temperature

  vmax :

  999999987000000.0

  vmin :

  -999999987000000.0

  valid_range :

  [-999999987000000.0, 999999987000000.0]

  origname :

  T2M

  fullnamepath :

  /T2M

  Maps :

  ()

Note, if you tried to use the username/password for creating the session instead of a token, you would get the redirect error.

Example 3 from PyDap documentation

In this example, constraint expression is used just to get certain variables. See full notebook here.

baseURL = 'dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/'
Temp_Salt = "ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_"
year = '2017-'
month = '01'
end_ = '_ECCO_V4r4_native_llc0090'
CE = '?dap4.ce=/THETA;/SALT;/tile;/j;/k;/i;/time'

Temp_2017 = [baseURL + Temp_Salt + year + f'{i:02}' + end_ + CE for i in range(1, 4)]
Temp_2017

['dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_2017-01_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/tile;/j;/k;/i;/time',
 'dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_2017-02_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/tile;/j;/k;/i;/time',
 'dap4://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%20Ocean%20Temperature%20and%20Salinity%20-%20Monthly%20Mean%20llc90%20Grid%20(Version%204%20Release%204)/granules/OCEAN_TEMPERATURE_SALINITY_mon_mean_2017-03_ECCO_V4r4_native_llc0090?dap4.ce=/THETA;/SALT;/tile;/j;/k;/i;/time']

Create data cube with open_mfdataset but not concat dim is /time not time. This takes a really long time, but if we didn’t do the constraint expression part, it would take much longer. So it is good to do that step.

Note token_session is not required since these data don’t need a EULA, but good practice to use.

%%time
# 13 seconds to assemble the data cube for a 126Mb dataset...slow
theta_salt_ds = xr.open_mfdataset(
    Temp_2017, 
    engine='pydap',
    parallel=True, 
    combine='nested', 
    concat_dim='/time',
    session=token_session
)
theta_salt_ds

CPU times: user 184 ms, sys: 12.7 ms, total: 196 ms
Wall time: 13 s

<xarray.Dataset> Size: 126MB
Dimensions:  (/time: 3, /k: 50, /tile: 13, /j: 90, /i: 90)
Coordinates:
    time     (/time) datetime64[ns] 24B dask.array<chunksize=(1,), meta=np.ndarray>
Dimensions without coordinates: /time, /k, /tile, /j, /i
Data variables:
    SALT     (/time, /k, /tile, /j, /i) float32 63MB dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
    THETA    (/time, /k, /tile, /j, /i) float32 63MB dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>
    i        (/time, /i) int32 1kB dask.array<chunksize=(1, 90), meta=np.ndarray>
    j        (/time, /j) int32 1kB dask.array<chunksize=(1, 90), meta=np.ndarray>
    k        (/time, /k) int32 600B dask.array<chunksize=(1, 50), meta=np.ndarray>
    tile     (/time, /tile) int32 156B dask.array<chunksize=(1, 13), meta=np.ndarray>
Attributes: (12/62)
    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_duration:          P1M
    time_coverage_end:               2017-02-01T00:00:00
    time_coverage_resolution:        P1M
    time_coverage_start:             2017-01-01T00:00:00
    title:                           ECCO Ocean Temperature and Salinity - Mo...
    uuid:                            f5b7028c-4181-11eb-b7e6-0cc47a3f47b1

xarray.Dataset

• Dimensions:
  • /time: 3
  • /k: 50
  • /tile: 13
  • /j: 90
  • /i: 90
• Coordinates: (1)
  • time
    (/time)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    center time of averaging period

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    standard_name :

    time

    origname :

    time

    fullnamepath :

    /time

    Maps :

    ()

    Array Chunk Bytes 24 B 8 B Shape (3,) (1,) Dask graph 3 chunks in 7 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (6)
  • SALT
    (/time, /k, /tile, /j, /i)
    float32
    dask.array<chunksize=(1, 50, 13, 90, 90), meta=np.ndarray>

    long_name :

    Salinity

    units :

    1e-3

    coverage_content_type :

    modelResult

    standard_name :

    sea_water_salinity

    comment :

    Defined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html

    valid_min :

    17.106637954711914

    valid_max :

    41.26802444458008

    origname :

    SALT

    fullnamepath :

    /SALT

    Maps :

    ()

    Array Chunk Bytes 60.25 MiB 20.08 MiB Shape (3, 50, 13, 90, 90) (1, 50, 13, 90, 90) Dask graph 3 chunks in 7 graph layers Data type float32 numpy.ndarray

  • THETA
    (/time, /k, /tile, /j, /i)
    float32
    dask.array<chunksize=(1, 50, 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

    Maps :

    ()

    Array Chunk Bytes 60.25 MiB 20.08 MiB Shape (3, 50, 13, 90, 90) (1, 50, 13, 90, 90) Dask graph 3 chunks in 7 graph layers Data type float32 numpy.ndarray

  • i
    (/time, /i)
    int32
    dask.array<chunksize=(1, 90), meta=np.ndarray>

    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

    Maps :

    ()

    Array Chunk Bytes 1.05 kiB 360 B Shape (3, 90) (1, 90) Dask graph 3 chunks in 10 graph layers Data type int32 numpy.ndarray

  • j
    (/time, /j)
    int32
    dask.array<chunksize=(1, 90), meta=np.ndarray>

    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

    Maps :

    ()

    Array Chunk Bytes 1.05 kiB 360 B Shape (3, 90) (1, 90) Dask graph 3 chunks in 10 graph layers Data type int32 numpy.ndarray

  • k
    (/time, /k)
    int32
    dask.array<chunksize=(1, 50), meta=np.ndarray>

    axis :

    Z

    long_name :

    grid index in z for tracer variables

    swap_dim :

    Z

    coverage_content_type :

    coordinate

    origname :

    k

    fullnamepath :

    /k

    Maps :

    ()

    Array Chunk Bytes 600 B 200 B Shape (3, 50) (1, 50) Dask graph 3 chunks in 10 graph layers Data type int32 numpy.ndarray

  • tile
    (/time, /tile)
    int32
    dask.array<chunksize=(1, 13), meta=np.ndarray>

    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

    Maps :

    ()

    Array Chunk Bytes 156 B 52 B Shape (3, 13) (1, 13) Dask graph 3 chunks in 10 graph layers Data type int32 numpy.ndarray

• Indexes: (0)
• Attributes: (62)

  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 :

  2020-12-18T14:39:59

  date_issued :

  2020-12-18T14:39:59

  date_metadata_modified :

  2021-03-15T21:56:21

  date_modified :

  2021-03-15T21:56:21

  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

  id :

  10.5067/ECL5M-OTS44

  institution :

  NASA Jet Propulsion Laboratory (JPL)

  instrument_vocabulary :

  GCMD instrument keywords

  keywords :

  EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, 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_2017-01_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 :

  2017-02-01T00:00:00

  time_coverage_resolution :

  P1M

  time_coverage_start :

  2017-01-01T00:00:00

  title :

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

  uuid :

  f5b7028c-4181-11eb-b7e6-0cc47a3f47b1

print(f"Dataset size: {theta_salt_ds.nbytes/1e6:.2f} MB")

Dataset size: 126.36 MB

theta_salt_ds = theta_salt_ds.rename({"/time": "time", "/j": "j", "/i": "i", "/tile": "tile", "/k": "k"})

/tmp/ipykernel_1511/704975482.py:1: UserWarning: rename '/time' to 'time' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.
  theta_salt_ds = theta_salt_ds.rename({"/time": "time", "/j": "j", "/i": "i", "/tile": "tile", "/k": "k"})

theta_salt_ds["THETA"].isel(time=1, tile=10, k=1).plot()

Conclusion

Use token-based sessions with pydap to ensure no redirect gotchas.

References

• https://pydap.github.io/pydap/intro.html
• https://opendap.github.io/documentation/tutorials/ClientAuthentication.html#_pydap
• https://github.com/OPENDAP/NASA-tutorials/tree/main
• https://pydap.github.io/pydap/notebooks/Authentication.html