PO.DAAC ECCO SSH – EarthData Cloud Cookbook

Requirements

1. AWS instance running in us-west-2

NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.

2. Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.

3. netrc File

You will need a netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.

Import required packages

import requests
import xarray as xr
import ujson
import s3fs
import fsspec
from tqdm import tqdm
from glob import glob
import os
import pathlib
import hvplot.xarray

from kerchunk.hdf import SingleHdf5ToZarr
from kerchunk.combine import MultiZarrToZarr

# The xarray produced from the reference file throws a SerializationWarning for each variable. Will need to explore why
import warnings
warnings.simplefilter("ignore")

Create Dask client to process the output json file in parallel

Generating the Kerchunk reference file can take some time depending on the internal structure of the data. Dask allows us to execute the reference file generation process in parallel, thus speeding up the overall process.

import dask
from dask.distributed import Client
client = Client(n_workers=4)
client

2022-05-11 15:27:29,674 - distributed.diskutils - INFO - Found stale lock file and directory '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-mezhdsy7', purging
/srv/conda/envs/notebook/lib/python3.9/contextlib.py:126: UserWarning: Creating scratch directories is taking a surprisingly long time. This is often due to running workers on a network file system. Consider specifying a local-directory to point workers to write scratch data to a local disk.
  next(self.gen)

Client

Client-ddf55e52-d13e-11ec-818c-b6609e8b92a4

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:41805/status

Cluster Info

LocalCluster

a24e60d3

Dashboard: http://127.0.0.1:41805/status	Workers: 4
Total threads: 4	Total memory: 15.18 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-8e045442-a409-4c3b-8c8c-a95470883931

Comm: tcp://127.0.0.1:36901	Workers: 4
Dashboard: http://127.0.0.1:41805/status	Total threads: 4
Started: Just now	Total memory: 15.18 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:34235	Total threads: 1
Dashboard: http://127.0.0.1:42845/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:37927
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-869qv5xb

Worker: 1

Comm: tcp://127.0.0.1:40997	Total threads: 1
Dashboard: http://127.0.0.1:41189/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:35257
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-3mo0d80c

Worker: 2

Comm: tcp://127.0.0.1:46429	Total threads: 1
Dashboard: http://127.0.0.1:42211/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:34287
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-o2fvmao4

Worker: 3

Comm: tcp://127.0.0.1:41615	Total threads: 1
Dashboard: http://127.0.0.1:41507/status	Memory: 3.80 GiB
Nanny: tcp://127.0.0.1:43053
Local directory: /home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/dask-worker-space/worker-9u77hywd

Get temporary S3 credentials

Temporary S3 credentials need to be passed to AWS. Note, these credentials must be refreshed after 1 hour.

s3_cred_endpoint = {
    'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',
    'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',
    'ornldaac':'https://data.ornldaac.earthdata.nasa.gov/s3credentials',
    'gesdisc':'https://data.gesdisc.earthdata.nasa.gov/s3credentials'
}

def get_temp_creds():
    temp_creds_url = s3_cred_endpoint['podaac']
    return requests.get(temp_creds_url).json()

temp_creds_req = get_temp_creds()

Direct Access a single netCDF4 file

Pass temporary credentials to our filesystem object to access the S3 assets

fs = s3fs.S3FileSystem(
    anon=False,
    key=temp_creds_req['accessKeyId'],
    secret=temp_creds_req['secretAccessKey'],
    token=temp_creds_req['sessionToken']
)

url = 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc'

s3_file_obj = fs.open(url, mode='rb')

Time how long it takes to directly access a cloud asset for comparisons later.

%%time

xr_ds = xr.open_dataset(s3_file_obj, chunks='auto', engine='h5netcdf')
xr_ds

CPU times: user 228 ms, sys: 8.51 ms, total: 237 ms
Wall time: 272 ms

<xarray.Dataset>
Dimensions:         (time: 1, latitude: 360, longitude: 720, nv: 2)
Coordinates:
  * time            (time) datetime64[ns] 2015-01-16T12:00:00
  * latitude        (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75
  * longitude       (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8
    time_bnds       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
    latitude_bnds   (latitude, nv) float32 dask.array<chunksize=(360, 2), meta=np.ndarray>
    longitude_bnds  (longitude, nv) float32 dask.array<chunksize=(720, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    SSH             (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHIBC          (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHNOIBC        (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), 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 a regular lat-lon grid. ...
    Conventions:                  CF-1.8, ACDD-1.3
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    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 Sea Surface Height - Monthly Mean 0.5 ...
    uuid:                         088d03b8-4158-11eb-876b-0cc47a3f47f1

Specify a list of S3 URLs

Data Collection: ECCO_L4_SSH_05DEG_MONTHLY_V4R4
Time Range: 2015

urls = ['s3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2014-12_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-02_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-03_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-04_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-05_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-06_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-07_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-08_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-10_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-11_ECCO_V4r4_latlon_0p50deg.nc',
 's3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-12_ECCO_V4r4_latlon_0p50deg.nc']

Generate the `Kerchunk` reference files.

Define a function to generate the Kerchunk reference files. These files can take a little time to generate.

def gen_json(u):
    so = dict(
        mode= "rb", 
        anon= False, 
        default_fill_cache= False,
        default_cache_type= "none"
    )
    with fs.open(u, **so) as infile:
        h5chunks = SingleHdf5ToZarr(infile, u, inline_threshold=300)
        with open(f"jsons/{u.split('/')[-1]}.json", 'wb') as outf:
            outf.write(ujson.dumps(h5chunks.translate()).encode())

Create output jsons directory if one does not exist.

pathlib.Path('./jsons/').mkdir(exist_ok=True)

Use the Dask Delayed function to create the Kerchunk reference file for each URL from the list of URLs in parallel

%%time

reference_files = []
for url in urls:
    ref = dask.delayed(gen_json)(url)
    reference_files.append(ref)

reference_files_compute = dask.compute(*reference_files)

CPU times: user 195 ms, sys: 83.4 ms, total: 278 ms
Wall time: 1.38 s

fs_ref_list = fsspec.filesystem('file')

reference_list = sorted([x for x in fs_ref_list.ls('jsons') if '.json' in x])
reference_list

['/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2014-12_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-02_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-03_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-04_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-05_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-06_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-07_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-08_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-10_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-11_ECCO_V4r4_latlon_0p50deg.nc.json',
 '/home/jovyan/earthdata-cloud-cookbook/examples/PODAAC/jsons/SEA_SURFACE_HEIGHT_mon_mean_2015-12_ECCO_V4r4_latlon_0p50deg.nc.json']

Read single netCDF4 using `Kerchunk` reference file

Open the first reference file to read into an xarray dataset

with open(reference_list[0]) as j:
    reference = ujson.load(j)

Set configurations options

s_opts = {'skip_instance_cache':True}   #json
r_opts = {'anon':False,          
          'key':temp_creds_req['accessKeyId'], 
          'secret':temp_creds_req['secretAccessKey'], 
          'token':temp_creds_req['sessionToken']}    #ncfiles

fs_single = fsspec.filesystem("reference",
                              fo=reference,
                              ref_storage_args=s_opts,
                              remote_protocol='s3', 
                              remote_options=r_opts)

Read in a single reference object. We get a lot of SerializationWarnings which are ignored here using the warning package.
NOTE, the fill value, data range, min value, and max value may not match the source file. Will need to look into this more.

%%time

m = fs_single.get_mapper("")
ds_single = xr.open_dataset(m, engine="zarr", backend_kwargs={'consolidated':False}, chunks={})
ds_single

CPU times: user 56.3 ms, sys: 26 ms, total: 82.2 ms
Wall time: 221 ms

<xarray.Dataset>
Dimensions:         (time: 1, latitude: 360, longitude: 720, nv: 2)
Coordinates:
  * latitude        (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75
    latitude_bnds   (latitude, nv) float32 dask.array<chunksize=(360, 2), meta=np.ndarray>
  * longitude       (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8
    longitude_bnds  (longitude, nv) float32 dask.array<chunksize=(720, 2), meta=np.ndarray>
  * time            (time) datetime64[ns] 2014-12-16T12:00:00
    time_bnds       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    SSH             (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHIBC          (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHNOIBC        (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
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 a regular lat-lon grid. ...
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2015-01-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2014-12-01T00:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean 0.5 ...
    uuid:                         08a2fc68-4158-11eb-b498-0cc47a3f6943

Read multiple netCDF4 files using Kerchunk reference file

Combine the individual reference files into a single time series reference object

%%time

ds_k =[]
for ref in reference_list:
    s_opts = s_opts
    r_opts = r_opts
    fs = fsspec.filesystem("reference",
                           fo=ref,
                           ref_storage_args=s_opts,
                           remote_protocol='s3',
                           remote_options=r_opts)
    m = fs.get_mapper("")
    ds_k.append(xr.open_dataset(m, engine="zarr", backend_kwargs={'consolidated':False}, chunks={}))
    
ds_multi = xr.concat(ds_k, dim='time')
    
ds_multi

CPU times: user 735 ms, sys: 31.4 ms, total: 766 ms
Wall time: 3.57 s

<xarray.Dataset>
Dimensions:         (time: 13, latitude: 360, longitude: 720, nv: 2)
Coordinates:
  * latitude        (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75
    latitude_bnds   (latitude, nv) float32 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0
  * longitude       (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8
    longitude_bnds  (longitude, nv) float32 -180.0 -179.5 -179.5 ... 179.5 180.0
  * time            (time) datetime64[ns] 2014-12-16T12:00:00 ... 2015-12-16T...
    time_bnds       (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    SSH             (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHIBC          (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
    SSHNOIBC        (time, latitude, longitude) float32 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
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 a regular lat-lon grid. ...
    coordinates_comment:          Note: the global 'coordinates' attribute de...
    ...                           ...
    time_coverage_duration:       P1M
    time_coverage_end:            2015-01-01T00:00:00
    time_coverage_resolution:     P1M
    time_coverage_start:          2014-12-01T00:00:00
    title:                        ECCO Sea Surface Height - Monthly Mean 0.5 ...
    uuid:                         08a2fc68-4158-11eb-b498-0cc47a3f6943

ds_multi['SSH']

<xarray.DataArray 'SSH' (time: 13, latitude: 360, longitude: 720)>
dask.array<concatenate, shape=(13, 360, 720), dtype=float32, chunksize=(1, 360, 720), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75
  * longitude  (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8
  * time       (time) datetime64[ns] 2014-12-16T12:00:00 ... 2015-12-16T12:00:00
Attributes:
    comment:                Dynamic sea surface height anomaly above the geoi...
    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

xarray.DataArray

'SSH'

time: 13
latitude: 360
longitude: 720

dask.array<chunksize=(1, 360, 720), meta=np.ndarray>

	Array	Chunk
Bytes	12.85 MiB	0.99 MiB
Shape	(13, 360, 720)	(1, 360, 720)
Count	39 Tasks	13 Chunks
Type	float32	numpy.ndarray

Coordinates: (3)
- latitude
  (latitude)
  float32
  -89.75 -89.25 ... 89.25 89.75
  axis :
  Y
  bounds :
  latitude_bnds
  comment :
  uniform grid spacing from -89.75 to 89.75 by 0.5
  coverage_content_type :
  coordinate
  long_name :
  latitude at grid cell center
  standard_name :
  latitude
  units :
  degrees_north
```
array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)
```
- longitude
  (longitude)
  float32
  -179.8 -179.2 ... 179.2 179.8
  axis :
  X
  bounds :
  longitude_bnds
  comment :
  uniform grid spacing from -179.75 to 179.75 by 0.5
  coverage_content_type :
  coordinate
  long_name :
  longitude at grid cell center
  standard_name :
  longitude
  units :
  degrees_east
```
array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
```
- time
  (time)
  datetime64[ns]
  2014-12-16T12:00:00 ... 2015-12-...
  axis :
  T
  bounds :
  time_bnds
  coverage_content_type :
  coordinate
  long_name :
  center time of averaging period
  standard_name :
  time
```
array(['2014-12-16T12:00:00.000000000', '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]')
```
Attributes: (7)
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

# Commenting for quarto site render
# ds_multi['SSH'].hvplot.image()

References

https://github.com/fsspec/kerchunk
https://medium.com/pangeo/fake-it-until-you-make-it-reading-goes-netcdf4-data-on-aws-s3-as-zarr-for-rapid-data-access-61e33f8fe685
https://medium.com/pangeo/cloud-performant-reading-of-netcdf4-hdf5-data-using-the-zarr-library-1a95c5c92314