Contents

Using public data on NIRD using s3 and saving results in private s3 object storage

Using public data on NIRD using s3 and saving results in private s3 object storage

How to discover (spatial and temporal search and subsetting) Obs-CERES-EBAF model output prepared for obs4MIPs OBSERVATIONS dataset
  • We show how to access s3 anonymous data in netCDF, make a geographical area selection and store into zarr on s3 private object storage
  • We do not address dask (chunking optimization, etc.)
import s3fs
import xarray as xr

Connect to bucket (anonymous login for public data only)

fs = s3fs.S3FileSystem(anon=True,
      client_kwargs={
         'endpoint_url': 'https://climate.uiogeo-apps.sigma2.no/'
      })

List bucket content

fs.ls('ESGF')

['ESGF/#README#',
 'ESGF/ALL_FILES_AND_DIRS_BELOW_HERE_SHALL_HAVE_GID_ns9252k_AND_WRITE_PERMS',
 'ESGF/CMIP6',
 'ESGF/cmip5',
 'ESGF/files_with_permission_problem_8Sept2021.txt',
 'ESGF/ns9252k_esgf_md5_betzy_sorted.txt',
 'ESGF/ns9252k_esgf_md5_sorted.txt',
 'ESGF/obs4MIPs',
 'ESGF/simass_SImon_EC-Earth3-CC_piControl_r1i1p1f1_gn_187201-187212.nc']

fs.ls('ESGF/obs4MIPs/CERES-EBAF/')

['ESGF/obs4MIPs/CERES-EBAF/rlut_CERES-EBAF_L3B_Ed2-8_200003-201404.nc',
 'ESGF/obs4MIPs/CERES-EBAF/rsut_CERES-EBAF_L3B_Ed2-8_200003-201404.nc']

Access data files

  • if netCDF format is used, data access can be slow)

  • you should try to use cloud zarr format

s3path = 's3://ESGF/obs4MIPs/CERES-EBAF/*.nc'

remote_files = fs.glob(s3path)

remote_files

['ESGF/obs4MIPs/CERES-EBAF/rlut_CERES-EBAF_L3B_Ed2-8_200003-201404.nc',
 'ESGF/obs4MIPs/CERES-EBAF/rsut_CERES-EBAF_L3B_Ed2-8_200003-201404.nc']

# Iterate through remote_files to create a fileset
fileset = [fs.open(file) for file in remote_files]

# This works
dset = xr.open_mfdataset(fileset, combine='by_coords')

dset

<xarray.Dataset>
Dimensions:    (bnds: 2, lat: 180, lon: 360, time: 170)
Coordinates:
  * time       (time) datetime64[ns] 2000-03-16T12:00:00 ... 2014-04-16
  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon        (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(170, 2), meta=np.ndarray>
    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>
    rlut       (time, lat, lon) float32 dask.array<chunksize=(170, 180, 360), meta=np.ndarray>
    rsut       (time, lat, lon) float32 dask.array<chunksize=(170, 180, 360), meta=np.ndarray>
Attributes: (12/27)
    institution:         NASA Langley Research Center, Hampton, Va, USA
    institute_id:        NASA-LaRC
    experiment_id:       obs
    source:              CERES retrievals
    model_id:            Obs-CERES-EBAF
    contact:             Norman Loeb, norman.g.loeb@nasa.gov
    ...                  ...
    Conventions:         CF-1.4
    project_id:          obs4MIPs
    table_id:            Table Amon_obs (10 March 2011) c2e3700dd6af44ea84f7a...
    title:               Obs-CERES-EBAF model output prepared for obs4MIPs OB...
    modeling_realm:      atmos
    cmor_version:        2.8.3

Shift longitude from 0 to 360 to -180 to 180 for convenience (when subsetting)

dset = dset.assign_coords(lon=(((dset.lon + 180) % 360) - 180)).sortby('lon')

Plot a single time

!pip install cmaps

WARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Requirement already satisfied: cmaps in /opt/conda/lib/python3.8/site-packages (1.0.3)
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.8/site-packages (from cmaps) (3.3.4)
Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from cmaps) (1.20.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->cmaps) (1.3.1)
Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->cmaps) (8.1.2)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->cmaps) (2.7.5)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib->cmaps) (0.10.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.8/site-packages (from matplotlib->cmaps) (2.4.7)
Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->cmaps) (1.15.0)

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cmaps

fig=plt.figure(figsize=(20,10))

# We're using cartopy and are plotting in Orthographic projection 
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.AlbersEqualArea(central_longitude=20.0, central_latitude=40.0))
ax.coastlines(resolution='10m')

# custom colormap

lcmap = cmaps.BlueYellowRed

# We need to project our data to the new Mercator projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
# we only plot values greather than 0
dset['rlut'].sel(time='2011-10-16').sel(lat=slice(50,90), lon=slice(-10,50)).plot(ax=ax, transform=ccrs.PlateCarree(), cmap=lcmap)  
ax.set_title('Obs-CERES-EBAF model output prepared for obs4MIPs OBSERVATIONS\n ', fontsize=20)
plt.savefig('Obs-CERES-EBAF_rlut_2011-10-16.png')
../../_images/nird-ESGF_19_0.png

Save results in zarr on NIRD for further analysis

  • your credentials are in $HOME/.aws/credentials

  • check with your instructor to get the secret access key (replace XXX by the right key)

[default]
aws_access_key_id=forces2021-work
aws_secret_access_key=XXXXXXXXXXXX
aws_endpoint_url=https://forces2021.uiogeo-apps.sigma2.no/

import fsspec

Set the path to your group’s location (ask your instructor)

target = fsspec.get_mapper("s3://work/obs4MIPs_CERES-EBAFObs_rlut_rsut.zarr", 
                  client_kwargs={
                  'endpoint_url': 'https://forces2021.uiogeo-apps.sigma2.no/'
                  }
            )

dset.sel(lat=slice(50,90), lon=slice(-10,50)).to_zarr(store=target, mode="w", consolidated=True, compute=True)

<xarray.backends.zarr.ZarrStore at 0x7f4b1c96bd60>

Check what you have stored in s3

Initilize the S3 file system

fsg = s3fs.S3FileSystem(anon=False,
      client_kwargs={
         'endpoint_url': 'https://forces2021.uiogeo-apps.sigma2.no/'
      })

fsg.ls('work')

['work/obs4MIPs_CERES-EBAFObs_rlut_rsut.zarr']

Set path to s3 data

s3_path = 's3://work/obs4MIPs_CERES-EBAFObs_rlut_rsut.zarr'

Initialize the S3 filesystem

store = s3fs.S3Map(root=s3_path, s3=fsg, check=False)

ds = xr.open_zarr(store=store, consolidated=True)

ds

<xarray.Dataset>
Dimensions:    (bnds: 2, lat: 40, lon: 60, time: 170)
Coordinates:
  * lat        (lat) float64 50.5 51.5 52.5 53.5 54.5 ... 86.5 87.5 88.5 89.5
  * lon        (lon) float64 -9.5 -8.5 -7.5 -6.5 -5.5 ... 46.5 47.5 48.5 49.5
  * time       (time) datetime64[ns] 2000-03-16T12:00:00 ... 2014-04-16
Dimensions without coordinates: bnds
Data variables:
    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(40, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(60, 2), meta=np.ndarray>
    rlut       (time, lat, lon) float32 dask.array<chunksize=(170, 40, 60), meta=np.ndarray>
    rsut       (time, lat, lon) float32 dask.array<chunksize=(170, 40, 60), meta=np.ndarray>
    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(170, 2), meta=np.ndarray>
Attributes: (12/27)
    Conventions:         CF-1.4
    cmor_version:        2.8.3
    contact:             Norman Loeb, norman.g.loeb@nasa.gov
    creation_date:       2014-07-29T14:04:11Z
    data_structure:      grid
    experiment_id:       obs
    ...                  ...
    source:              CERES retrievals
    source_id:           CERES-EBAF
    source_type:         satellite_retrieval
    table_id:            Table Amon_obs (10 March 2011) c2e3700dd6af44ea84f7a...
    title:               Obs-CERES-EBAF model output prepared for obs4MIPs OB...
    tracking_id:         5da4db80-31cb-4340-b5e1-0fbacaee873a

Plot TOA outgoing shortwave Radiation

  • Note that there is no need to select an area because the s3 dataset only cover the area of interest (selected when saving dataset in s3 storage)

fig=plt.figure(figsize=(20,10))

# We're using cartopy and are plotting in Orthographic projection 
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.AlbersEqualArea(central_longitude=20.0, central_latitude=40.0))
ax.coastlines(resolution='10m')

# custom colormap

lcmap = cmaps.BlueYellowRed

# We need to project our data to the new Mercator projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
# we only plot values greather than 0
ds['rsut'].sel(time='2011-10-16').plot(ax=ax, transform=ccrs.PlateCarree(), cmap=lcmap)  
ax.set_title('Obs-CERES-EBAF model output prepared for obs4MIPs OBSERVATIONS\n TOA outgoing shortwave Radiation\n' + str(ds.time.sel(time='2011-10-16').dt.strftime("%B %d, %Y, %r").values[0]), fontsize=20)
plt.savefig('Obs-CERES-EBAF_rsut_2011-10-16.png')
../../_images/nird-ESGF_36_0.png

previous

Using masks and computing weighted average

next

Using cartopy and projections for plotting