From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

Amazon Estuary Exploration:

In AWS Cloud Version

This tutorial is one of two jupyter notebook versions of the same use case exploring multiple satellite data products over the Amazon Estuary. In this version, we use data products directly in the Amazon Web Services (AWS) Cloud.

Learning Objectives

  • Compare cloud access methods (in tandem with notebook “Amazon Estuary Exploration: Cloud Direct Download Version”)
  • Search for data products using earthaccess Python library
  • Access datasets using direct s3 access in cloud, xarray and visualize using hvplot or plot tools

This tutorial explores the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets listed below. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use cloud datasets and services. The notebook is designed to be executed in Amazon Web Services (AWS) (in us-west-2 region where the cloud data is located).

Cloud Datasets

The tutorial itself will use four different datasets:

1. TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3

DOI: https://doi.org/10.5067/TEMSC-3JC63

The Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season.

2. PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2

DOI: https://doi.org/10.5067/PSGRA-DA2V2

The NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.

3. OISSS_L4_multimission_7day_v1

DOI: https://doi.org/10.5067/SMP10-4U7CS

Optimally Interpolated Sea surface salinity (OISSS) is a level 4 product that combines the records from Aquarius (Sept 2011-June 2015), the Soil Moisture Active Passive (SMAP) satellite (April 2015-present), and ESAs Soil Moisture Ocean Salinity (SMOS) data to fill in data gaps.

4. MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0

DOI: https://doi.org/10.5067/MODAM-MO9N9

Sea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite.

More details on available collections are on the PO.DAAC Cloud Earthdata Search Portal. For more information on the PO.DAAC transition to the cloud, please visit: https://podaac.jpl.nasa.gov/cloud-datasets/about

Needed Packages

import os
import glob
import s3fs
import requests
import numpy as np
import pandas as pd
import xarray as xr
import hvplot.xarray
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy
import dask
from datetime import datetime
from os.path import isfile, basename, abspath
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store

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. We use earthaccess to authenticate your login credentials below.

auth = earthaccess.login(strategy="interactive", persist=True)

Liquid Water Equivalent (LWE) Thickness (GRACE & GRACE-FO)

Search for GRACE LWE Thickness data

Suppose we are interested in LWE data from the dataset (DOI:10.5067/TEMSC-3JC63) described on this PO.DAAC dataset landing page: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3 From the landing page, we see the dataset Short Name under the Information tab. This is what we will be using to search for the collection with earthaccess.

You can also access the short name of the dataset through Earthdata search at: https://search.earthdata.nasa.gov.

#search for the granules using the short name
grace_results = earthaccess.search_data(short_name="TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3")
Granules found: 1

Open the .nc file in the s3 bucket and load it into an xarray dataset

ds_GRACE = xr.open_mfdataset(earthaccess.open([grace_results[0]]), engine='h5netcdf')
ds_GRACE
 Opening 1 granules, approx size: 0.0 GB
<xarray.Dataset>
Dimensions:        (lon: 720, lat: 360, time: 219, bounds: 2)
Coordinates:
  * lon            (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8
  * lat            (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75
  * time           (time) datetime64[ns] 2002-04-17T12:00:00 ... 2023-03-16T1...
Dimensions without coordinates: bounds
Data variables:
    lwe_thickness  (time, lat, lon) float64 dask.array<chunksize=(219, 360, 720), meta=np.ndarray>
    uncertainty    (time, lat, lon) float64 dask.array<chunksize=(219, 360, 720), meta=np.ndarray>
    lat_bounds     (lat, bounds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>
    lon_bounds     (lon, bounds) float64 dask.array<chunksize=(720, 2), meta=np.ndarray>
    time_bounds    (time, bounds) datetime64[ns] dask.array<chunksize=(219, 2), meta=np.ndarray>
    land_mask      (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>
    scale_factor   (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>
    mascon_ID      (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>
Attributes: (12/53)
    Conventions:                   CF-1.6, ACDD-1.3, ISO 8601
    Metadata_Conventions:          Unidata Dataset Discovery v1.0
    standard_name_vocabulary:      NetCDF Climate and Forecast (CF) Metadata ...
    title:                         JPL GRACE and GRACE-FO MASCON RL06.1Mv03 CRI
    summary:                       Monthly gravity solutions from GRACE and G...
    keywords:                      Solid Earth, Geodetics/Gravity, Gravity, l...
    ...                            ...
    C_30_substitution:             TN-14; Loomis et al., 2019, Geophys. Res. ...
    user_note_1:                   The accelerometer on the GRACE-B spacecraf...
    user_note_2:                   The accelerometer on the GRACE-D spacecraf...
    journal_reference:             Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...
    CRI_filter_journal_reference:  Wiese, D. N., F. W. Landerer, and M. M. Wa...
    date_created:                  2023-05-22T06:05:03Z
    • lon
      PandasIndex
      PandasIndex(Index([  0.25,   0.75,   1.25,   1.75,   2.25,   2.75,   3.25,   3.75,   4.25,
         4.75,
       ...
       355.25, 355.75, 356.25, 356.75, 357.25, 357.75, 358.25, 358.75, 359.25,
       359.75],
      dtype='float64', name='lon', length=720))
    • lat
      PandasIndex
      PandasIndex(Index([-89.75, -89.25, -88.75, -88.25, -87.75, -87.25, -86.75, -86.25, -85.75,
       -85.25,
       ...
        85.25,  85.75,  86.25,  86.75,  87.25,  87.75,  88.25,  88.75,  89.25,
        89.75],
      dtype='float64', name='lat', length=360))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2002-04-17 12:00:00', '2002-05-10 12:00:00',
               '2002-08-16 12:00:00', '2002-09-16 00:00:00',
               '2002-10-16 12:00:00', '2002-11-16 00:00:00',
               '2002-12-16 12:00:00', '2003-01-16 12:00:00',
               '2003-02-15 00:00:00', '2003-03-16 12:00:00',
               ...
               '2022-06-16 00:00:00', '2022-07-16 12:00:00',
               '2022-08-16 12:00:00', '2022-09-16 00:00:00',
               '2022-10-16 12:00:00', '2022-11-16 00:00:00',
               '2022-12-16 12:00:00', '2023-01-16 12:00:00',
               '2023-02-15 00:00:00', '2023-03-16 12:00:00'],
              dtype='datetime64[ns]', name='time', length=219, freq=None))
  • Conventions :
    CF-1.6, ACDD-1.3, ISO 8601
    Metadata_Conventions :
    Unidata Dataset Discovery v1.0
    standard_name_vocabulary :
    NetCDF Climate and Forecast (CF) Metadata Convention-1.6
    title :
    JPL GRACE and GRACE-FO MASCON RL06.1Mv03 CRI
    summary :
    Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06.1Mv03 mascon solution - with CRI filter applied
    keywords :
    Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thickness
    keywords_vocabulary :
    NASA Global Change Master Directory (GCMD) Science Keywords
    platform :
    GRACE and GRACE-FO
    institution :
    NASA/JPL
    creator_name :
    David Wiese
    creator_email :
    grace@podaac.jpl.nasa.gov
    creator_url :
    https://grace.jpl.nasa.gov
    creator_type :
    group
    creator_institution :
    NASA/JPL
    publisher_name :
    Physical Oceanography Distributed Active Archive Center
    publisher_email :
    podaac@jpl.nasa.gov
    publisher_url :
    https://podaac.jpl.nasa.gov
    publisher_type :
    group
    publisher_institution :
    NASA/JPL
    project :
    NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)
    program :
    NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Program
    id :
    10.5067/TEMSC-3JC62
    naming_authority :
    org.doi.dx
    source :
    GRACE and GRACE-FO JPL RL06.1Mv03-CRI
    processing_level :
    2 and 3
    acknowledgement :
    GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAC
    license :
    https://science.nasa.gov/earth-science/earth-science-data/data-information-policy
    product_version :
    v3.0
    time_epoch :
    2002-01-01T00:00:00Z
    time_coverage_start :
    2002-04-16T00:00:00Z
    time_coverage_end :
    2023-03-16T23:59:59Z
    geospatial_lat_min :
    -89.75
    geospatial_lat_max :
    89.75
    geospatial_lat_units :
    degrees_north
    geospatial_lat_resolution :
    0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons
    geospatial_lon_min :
    0.25
    geospatial_lon_max :
    359.75
    geospatial_lon_units :
    degrees_east
    geospatial_lon_resolution :
    0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons
    time_mean_removed :
    2004.000 to 2009.999
    months_missing :
    2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09
    postprocess_1 :
    OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLY
    postprocess_2 :
    Water density used to convert to equivalent water height: 1000 kg/m^3
    postprocess_3 :
    Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlines
    GIA_removed :
    ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.
    geocenter_correction :
    We use a version of TN-13 based on the JPL mascons
    C_20_substitution :
    TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929
    C_30_substitution :
    TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.
    user_note_1 :
    The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.
    user_note_2 :
    The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.
    journal_reference :
    Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547.
    CRI_filter_journal_reference :
    Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344.
    date_created :
    2023-05-22T06:05:03Z

    • Plot a subset of the data

    Use the function xarray.DataSet.sel to select a subset of the data to plot with hvplot.

    lat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude
ds_GRACE_subset= ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))
ds_GRACE_subset

ds_GRACE_subset.lwe_thickness.hvplot.image(y='lat', x='lon', cmap='bwr_r',).opts(clim=(-80,80))

    River heights (Pre-SWOT MEaSUREs)

    The shortname for MEaSUREs is ‘PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2’ with the concept ID: C2036882359-POCLOUD. The guidebook explains the details of the Pre-SWOT MEaSUREs data.

    Our desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.

    To get the data for the exact area we need, we have set the boundaries of (-74.67188,-4.51279,-51.04688,0.19622) as reflected in our earthaccess data search.

    Let’s again access the netCDF file from an s3 bucket and look at the data structure.

    #earthaccess search using our established parameters to only find necessary granules
MEaSUREs_results = earthaccess.search_data(short_name="PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2", temporal = ("1993-04-08", "2019-04-20"), bounding_box=(-74.67188,-4.51279,-51.04688,0.19622))
    Granules found: 1
    ds_MEaSUREs = xr.open_mfdataset(earthaccess.open([MEaSUREs_results[0]]), engine='h5netcdf')
ds_MEaSUREs
     Opening 1 granules, approx size: 0.0 GB
    <xarray.Dataset>
Dimensions:              (X: 3311, Y: 3311, distance: 3311, time: 9469,
                          charlength: 26)
Coordinates:
  * time                 (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...
Dimensions without coordinates: X, Y, distance, charlength
Data variables:
    lon                  (X) float64 dask.array<chunksize=(3311,), meta=np.ndarray>
    lat                  (Y) float64 dask.array<chunksize=(3311,), meta=np.ndarray>
    FD                   (distance) float64 dask.array<chunksize=(3311,), meta=np.ndarray>
    height               (distance, time) float64 dask.array<chunksize=(3311, 9469), meta=np.ndarray>
    sat                  (charlength, time) |S1 dask.array<chunksize=(26, 9469), meta=np.ndarray>
    storage              (distance, time) float64 dask.array<chunksize=(3311, 9469), meta=np.ndarray>
    IceFlag              (time) float64 dask.array<chunksize=(9469,), meta=np.ndarray>
    LakeFlag             (distance) float64 dask.array<chunksize=(3311,), meta=np.ndarray>
    Storage_uncertainty  (distance, time) float64 dask.array<chunksize=(3311, 9469), meta=np.ndarray>
Attributes: (12/40)
    title:                         GRRATS (Global River Radar Altimetry Time ...
    Conventions:                   CF-1.6, ACDD-1.3
    institution:                   Ohio State University, School of Earth Sci...
    source:                        MEaSUREs OSU Storage toolbox 2018
    keywords:                      EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...
    keywords_vocabulary:           Global Change Master Directory (GCMD)
    ...                            ...
    geospatial_lat_max:            -0.6550700975069503
    geospatial_lat_units:          degree_north
    geospatial_vertical_max:       92.7681246287056
    geospatial_vertical_min:       -3.5634095181633763
    geospatial_vertical_units:     m
    geospatial_vertical_positive:  up
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1993-04-08 15:20:40.665117184',
               '1993-04-09 15:20:40.665117184',
               '1993-04-10 15:20:40.665117184',
               '1993-04-11 15:20:40.665117184',
               '1993-04-12 15:20:40.665117184',
               '1993-04-13 15:20:40.665117184',
               '1993-04-14 15:20:40.665117184',
               '1993-04-15 15:20:40.665117184',
               '1993-04-16 15:20:40.665117184',
               '1993-04-17 15:20:40.665117184',
               ...
               '2019-04-11 03:39:13.243964928',
               '2019-04-12 03:39:13.243964928',
               '2019-04-13 03:39:13.243964928',
               '2019-04-14 03:39:13.243964928',
               '2019-04-15 03:39:13.243964928',
               '2019-04-16 03:39:13.243964928',
               '2019-04-17 03:39:13.243964928',
               '2019-04-18 03:39:13.243964928',
               '2019-04-19 03:39:13.243964928',
               '2019-04-20 03:39:13.243964928'],
              dtype='datetime64[ns]', name='time', length=9469, freq=None))
  • title :
    GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Amazon River
    Conventions :
    CF-1.6, ACDD-1.3
    institution :
    Ohio State University, School of Earth Sciences
    source :
    MEaSUREs OSU Storage toolbox 2018
    keywords :
    EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHT
    keywords_vocabulary :
    Global Change Master Directory (GCMD)
    cdm_data_type :
    station
    creator_name :
    Coss,Steve
    creator_email :
    Coss.31@osu.edu
    project :
    MEaSUREs OSU
    program :
    NASA Earth Science Data Systems (ESDS)
    publisher_name :
    PO.DAAC (Physical Oceanography Distributed Active Archive Center)
    publisher_email :
    podaac@podaac.jpl.nasa.gov
    publisher_url :
    podaac.jpl.nasa.gov
    publisher_type :
    Institution
    publisher_institution :
    PO.DAAC
    processing_level :
    L2
    doi :
    10.5067/PSGRA-DA2V2
    history :
    This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements.
    platform :
    ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)
    platform_vocabulary :
    NASA/GCMD Platform Keywords. Version 8.6
    instrument :
    RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)
    instrument_vocabulary :
    NASA/GCMD Platform Keywords. Version 8.6
    references :
    in review :doi.org/10.5194/essd-2019-84
    id :
    GRRATS(Global River Radar Altimeter Time Series) 1km/daily
    summary :
    The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.
    time_coverage_resolution :
    1 day
    date_created :
    2021-06-30T08:03:41
    time_coverage_start :
    1992-04-08T15:20:40
    time_coverage_end :
    2018-04-20T03:39:13
    geospatial_lon_min :
    -73.35433106652545
    geospatial_lon_max :
    -51.0426448887506
    geospatial_lon_units :
    degree_east
    geospatial_lat_min :
    -4.3804275867636875
    geospatial_lat_max :
    -0.6550700975069503
    geospatial_lat_units :
    degree_north
    geospatial_vertical_max :
    92.7681246287056
    geospatial_vertical_min :
    -3.5634095181633763
    geospatial_vertical_units :
    m
    geospatial_vertical_positive :
    up

    • Plot a subset of the data

    Plotting the river distances and associated heights on the map at time t=9069 (March 16, 2018) using plt.

    fig = plt.figure(figsize=[11,7]) 
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_extent([-85, -30, -20, 20])
ax.add_feature(cartopy.feature.RIVERS)

plt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])
plt.colorbar(label='Interpolated River Heights (m)')
plt.clim(-10,100)

plt.show()

    Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)

    The shortname for this dataset is ‘OISSS_L4_multimission_7day_v1’ with the concept ID: C2095055342-POCLOUD. This dataset contains hundreds of granules, so it would be impractical to search and copy all of the URLs in Earthdata Search. Using the shortname, we can access all the files at once in the cloud (1.1 GB data).

    sss_results = earthaccess.search_data(short_name="OISSS_L4_multimission_7day_v1")
    Granules found: 998

    The following cell may take up to around 10 minutes to run due to the size of the dataset

    #this line ignores warnings about large chunk sizes
dask.config.set(**{'array.slicing.split_large_chunks': False})

ds_sss = xr.open_mfdataset(earthaccess.open(sss_results),
                           combine='by_coords',
                           mask_and_scale=True,
                           decode_cf=True,
                           chunks='auto',
                           engine='h5netcdf')
ds_sss
     Opening 998 granules, approx size: 0.0 GB
    <xarray.Dataset>
Dimensions:                    (longitude: 1440, latitude: 720, time: 998)
Coordinates:
  * longitude                  (longitude) float32 -179.9 -179.6 ... 179.6 179.9
  * latitude                   (latitude) float32 -89.88 -89.62 ... 89.62 89.88
  * time                       (time) datetime64[ns] 2011-08-28 ... 2022-08-02
Data variables:
    sss                        (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>
    sss_empirical_uncertainty  (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 962), meta=np.ndarray>
    sss_uncertainty            (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>
Attributes: (12/42)
    Conventions:                   CF-1.8, ACDD-1.3
    standard_name_vocabulary:      CF Standard Name Table v27
    Title:                         Multi-Mission Optimally Interpolated Sea S...
    Short_Name:                    OISSS_L4_multimission_7d_v1
    Version:                       V1.0
    Processing_Level:              Level 4
    ...                            ...
    geospatial_lat_resolution:     0.25
    geospatial_lat_units:          degrees_north
    geospatial_lon_min:            -180.0
    geospatial_lon_max:            180.0
    geospatial_lon_resolution:     0.25
    geospatial_lon_units:          degrees_east
    • longitude
      PandasIndex
      PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
       -178.125, -177.875, -177.625,
       ...
        177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
        179.375,  179.625,  179.875],
      dtype='float32', name='longitude', length=1440))
    • latitude
      PandasIndex
      PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
       -87.875, -87.625,
       ...
        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
        89.625,  89.875],
      dtype='float32', name='latitude', length=720))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2011-08-28', '2011-09-01', '2011-09-05', '2011-09-09',
               '2011-09-13', '2011-09-17', '2011-09-21', '2011-09-25',
               '2011-09-29', '2011-10-03',
               ...
               '2022-06-27', '2022-07-01', '2022-07-05', '2022-07-09',
               '2022-07-13', '2022-07-17', '2022-07-21', '2022-07-25',
               '2022-07-29', '2022-08-02'],
              dtype='datetime64[ns]', name='time', length=998, freq=None))
  • Conventions :
    CF-1.8, ACDD-1.3
    standard_name_vocabulary :
    CF Standard Name Table v27
    Title :
    Multi-Mission Optimally Interpolated Sea Surface Salinity 7-Day Global Dataset V1.0
    Short_Name :
    OISSS_L4_multimission_7d_v1
    Version :
    V1.0
    Processing_Level :
    Level 4
    source :
    Aquarius V5.0 Level 2 SSS; SMAP RSS V4.0 Level 2 SSS_40km; SMOS Level 2 SSS L2OS version 662
    sourse_of_input_Aquarius_SSS :
    Aquarius Official Release Level 2 Sea Surface Salinity & Wind Speed Cal Data V5.0. Distributed by PO.DAAC at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_CAL_V5
    sourse_of_input_SMAP_SSS :
    Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.
    sourse_of_input_SMOS_SSS :
    ESA SMOS online dissemination service at https://smos-diss.eo.esa.int/oads/access
    platform :
    Aquarius/SAC-D, SMAP, SMOS
    instrument :
    Aquarius radiometer, SMAP radiometer, SMOS MIRAS
    Creation_Date :
    2023-01-16T04:04:41Z
    Creator_Name :
    Oleg Melnichenko
    Creator_Email :
    oleg@hawaii.edu
    Creator_URL :
    http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB
    Project :
    NASA Ocean Salinity
    Keywords :
    Sea Surface Salinity, SSS, Aquarius, SMAP, Optimum Interpolation, OISSS
    Keywords_vocabulary :
    NASA Global Change Master Directory (GCMD) Science Keywords
    Institution :
    IPRC/SOEST, University of Hawaii, Honolulu, HI; Remote Sensing Systems (RSS), Santa Rosa, CA
    Publisher_Name :
    Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank Wentz
    Publisher_Email :
    oleg@hawaii.edu.org
    Publisher_URL :
    http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB
    Dataset_Citation_Authors :
    Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank Wentz
    Dataset_Citation_Year :
    2021
    Dataset_Citation_Product :
    Aquarius/SMAP Sea Surface Salinity Optimum Interpolation Analysis
    Technical_Notes :
    http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdf
    year_of_observation :
    2022
    month_of_observation :
    3
    day_of_observation :
    11
    time_coverage_start :
    2022-03-07T12:00:00Z
    time_coverage_end :
    2022-03-15T12:00:00Z
    time_coverage_resolution :
    P7D
    cdm_data_type :
    grid
    geospatial_lat_min :
    -90.0
    geospatial_lat_max :
    90.0
    geospatial_lat_resolution :
    0.25
    geospatial_lat_units :
    degrees_north
    geospatial_lon_min :
    -180.0
    geospatial_lon_max :
    180.0
    geospatial_lon_resolution :
    0.25
    geospatial_lon_units :
    degrees_east

    • Plot a subset of the data

    Use the function xarray.DataSet.sel to select a subset of the data at the outlet of the Amazon to plot at time t=0 (August 28, 2011) with hvplot.

    lat_bnds, lon_bnds = [-2, 6], [-52, -44] 
ds_sss_subset = ds_sss.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds_sss_subset

ds_sss_subset.sss[:,:,0].hvplot()

    Sea Surface Temperature (MODIS)

    MODIS has SST data that coincides with the period used for SSS, 2011-present (943 MB), and has the short name: “MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0”. Let’s access this in the same method as before.

    Get a list of files so we can open them all at once, creating an xarray dataset using the open_mfdataset() function to “read in” all of the netCDF4 files in one call. MODIS does not have a built-in time variable like SSS, but it is subset by latitude and longitude coordinates. We need to combine the files using the nested format with a created ‘time’ dimension.

    modis_results = earthaccess.search_data(short_name="MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0", temporal = ("2011-01-01", "2023-01-01"))
    Granules found: 143

    MODIS did not come with a time variable, so it needs to be extracted from the file names and added in the file preprocessing so files can be successfully concatenated.

    #function for time dimension added to each netCDF file
def preprocessing(ds): 
    file_name = ds.product_name
    file_date = basename(file_name).split("_")[2][:6]
    file_date_c = datetime.strptime(file_date, "%Y%m")
    time_point = [file_date_c]
    ds.coords['time'] = ('time', time_point) #expand the dimensions to include time
    return ds

ds_MODIS = xr.open_mfdataset(earthaccess.open(modis_results), combine='by_coords', join='override', mask_and_scale=True, decode_cf=True, chunks='auto',preprocess = preprocessing)
ds_MODIS
     Opening 143 granules, approx size: 0.0 GB
    <xarray.Dataset>
Dimensions:    (time: 143, lat: 2160, lon: 4320, rgb: 3, eightbitcolor: 256)
Coordinates:
  * lat        (lat) float32 89.96 89.88 89.79 89.71 ... -89.79 -89.88 -89.96
  * lon        (lon) float32 -180.0 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0
  * time       (time) datetime64[ns] 2011-01-01 2011-02-01 ... 2023-01-01
Dimensions without coordinates: rgb, eightbitcolor
Data variables:
    sst4       (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>
    qual_sst4  (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>
    palette    (time, lon, lat, rgb, eightbitcolor) uint8 dask.array<chunksize=(1, 4320, 2160, 3, 256), meta=np.ndarray>
Attributes: (12/59)
    product_name:                     AQUA_MODIS.20110101_20110131.L3m.MO.SST...
    instrument:                       MODIS
    title:                            MODISA Level-3 Standard Mapped Image
    project:                          Ocean Biology Processing Group (NASA/GS...
    platform:                         Aqua
    temporal_range:                   month
    ...                               ...
    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov
    processing_level:                 L3 Mapped
    cdm_data_type:                    grid
    data_bins:                        4834400
    data_minimum:                     -1.635
    data_maximum:                     32.06999
    • lat
      PandasIndex
      PandasIndex(Index([ 89.95833587646484,             89.875,  89.79167175292969,
        89.70833587646484,             89.625,  89.54167175292969,
        89.45833587646484,             89.375,  89.29167175292969,
        89.20833587646484,
       ...
       -89.20833587646484, -89.29166412353516, -89.37500762939453,
       -89.45833587646484, -89.54166412353516, -89.62500762939453,
       -89.70833587646484, -89.79166412353516, -89.87500762939453,
       -89.95833587646484],
      dtype='float32', name='lat', length=2160))
    • lon
      PandasIndex
      PandasIndex(Index([ -179.9583282470703,            -179.875, -179.79165649414062,
        -179.7083282470703,            -179.625, -179.54165649414062,
        -179.4583282470703,            -179.375, -179.29165649414062,
        -179.2083282470703,
       ...
        179.20835876464844,   179.2916717529297,  179.37501525878906,
        179.45835876464844,   179.5416717529297,  179.62501525878906,
        179.70835876464844,   179.7916717529297,  179.87501525878906,
        179.95835876464844],
      dtype='float32', name='lon', length=4320))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
               '2011-05-01', '2011-06-01', '2011-07-01', '2011-08-01',
               '2011-09-01', '2011-10-01',
               ...
               '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
               '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
               '2022-11-01', '2023-01-01'],
              dtype='datetime64[ns]', name='time', length=143, freq=None))
  • product_name :
    AQUA_MODIS.20110101_20110131.L3m.MO.SST4.sst4.9km.nc
    instrument :
    MODIS
    title :
    MODISA Level-3 Standard Mapped Image
    project :
    Ocean Biology Processing Group (NASA/GSFC/OBPG)
    platform :
    Aqua
    temporal_range :
    month
    processing_version :
    R2019.0
    date_created :
    2019-12-17T18:28:57.000Z
    history :
    l3mapgen par=AQUA_MODIS.20110101_20110131.L3m.MO.SST4.sst4.9km.nc.param
    l2_flag_names :
    LAND,~HISOLZEN
    time_coverage_start :
    2010-12-31T12:10:01.000Z
    time_coverage_end :
    2011-01-31T14:29:59.000Z
    start_orbit_number :
    46065
    end_orbit_number :
    46518
    map_projection :
    Equidistant Cylindrical
    latitude_units :
    degrees_north
    longitude_units :
    degrees_east
    northernmost_latitude :
    90.0
    southernmost_latitude :
    -90.0
    westernmost_longitude :
    -180.0
    easternmost_longitude :
    180.0
    geospatial_lat_max :
    90.0
    geospatial_lat_min :
    -90.0
    geospatial_lon_max :
    180.0
    geospatial_lon_min :
    -180.0
    latitude_step :
    0.083333336
    longitude_step :
    0.083333336
    sw_point_latitude :
    -89.958336
    sw_point_longitude :
    -179.95833
    spatialResolution :
    9.28 km
    geospatial_lon_resolution :
    0.083333336
    geospatial_lat_resolution :
    0.083333336
    geospatial_lat_units :
    degrees_north
    geospatial_lon_units :
    degrees_east
    number_of_lines :
    2160
    number_of_columns :
    4320
    measure :
    Mean
    suggested_image_scaling_minimum :
    -2.0
    suggested_image_scaling_maximum :
    45.0
    suggested_image_scaling_type :
    LINEAR
    suggested_image_scaling_applied :
    No
    _lastModified :
    2019-12-17T18:28:57.000Z
    Conventions :
    CF-1.6 ACDD-1.3
    institution :
    NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group
    standard_name_vocabulary :
    CF Standard Name Table v36
    naming_authority :
    gov.nasa.gsfc.sci.oceandata
    id :
    AQUA_MODIS.20110101_20110131.L3b.MO.SST4.nc/L3/AQUA_MODIS.20110101_20110131.L3b.MO.SST4.nc
    license :
    https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
    creator_name :
    NASA/GSFC/OBPG
    publisher_name :
    NASA/GSFC/OBPG
    creator_email :
    data@oceancolor.gsfc.nasa.gov
    publisher_email :
    data@oceancolor.gsfc.nasa.gov
    creator_url :
    https://oceandata.sci.gsfc.nasa.gov
    publisher_url :
    https://oceandata.sci.gsfc.nasa.gov
    processing_level :
    L3 Mapped
    cdm_data_type :
    grid
    data_bins :
    4834400
    data_minimum :
    -1.635
    data_maximum :
    32.06999

    • Plot a subset of the data

    Use the function xarray.DataSet.sel to select a subset of the data at the outlet of the Amazon to plot with hvplot.

    lat_bnds, lon_bnds = [6,-2], [-52, -44] 
ds_MODIS_subset = ds_MODIS.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))
ds_MODIS_subset

ds_MODIS_subset.sst4.hvplot.image(y='lat', x='lon', cmap='viridis').opts(clim=(22,30))

    Time Series Comparison

    Plot each dataset for the time period 2011-2019.

    First, we need to average all pixels in the subset lat/lon per time for sea surface salinity and sea surface temperature to set up for the graphs.

    sss_mean = []
for t in np.arange(len(ds_sss_subset.time)):
    sss_mean.append(np.nanmean(ds_sss_subset.sss[:,:,t].values))

#sss_mean
    #MODIS
sst_MODIS_mean = []
for t in np.arange(len(ds_MODIS_subset.time)):
    sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))
    
#sst_MODIS_mean

    Combined timeseries plot of river height and LWE thickness

    Both datasets are mapped for the outlet of the Amazon River into the estuary.

    #plot river height and land water equivalent thickness
fig, ax1 = plt.subplots(figsize=[12,7])

#plot river height
ds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')

#plot LWE thickness on secondary axis
ax2 = ax1.twinx()
ax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')

ax1.set_xlabel('Date')
ax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')
ax1.set_ylabel('River Height (m)', color='darkblue')
ax2.legend(['GRACE-FO'], loc='upper right')
ax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')

plt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')
ax1.grid()
plt.show()

    LWE thickness captures the seasonality of Pre-SWOT MEaSUREs river heights well, and so LWE thickness can be compared to all other variables as a representative of the seasonality of both measurements for the purpose of this notebook.

    Combined timeseries plots of salinity and LWE thickness, followed by temperature

    #Combined Subplots
fig = plt.figure(figsize=(10,10))

ax1 = fig.add_subplot(211)
plt.title('Amazon Estuary, 2011-2019')
ax2 = ax1.twinx()
ax3 = plt.subplot(212)
ax4 = ax3.twinx()

#lwe thickness
ax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')
ax1.set_ylabel('LWE Thickness (cm)', color='darkorange')
ax1.grid()

#sea surface salinity
ax2.plot(ds_sss_subset.time[0:750], sss_mean[0:750], 'g')
ax2.set_ylabel('SSS (psu)', color='g')

#sea surface temperature
ax3.plot(ds_MODIS_subset.time[7:108], sst_MODIS_mean[7:108], 'darkred')
ax3.set_ylabel('SST (deg C)', color='darkred')
ax3.grid()

#river height at outlet
ds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')
ax4.set_ylabel('River Height (m)', color='darkblue')
    Text(0, 0.5, 'River Height (m)')

    Measurements of LWE thickness and SSS follow expected patterns. When lwe thickness is at its lowest, indicating less water is flowing through during the drought, salinity is at its highest. Without high volume of water pouring into the estuary, salinity increases. We can see that temperature is shifted a bit in time from river height as well at the outlet, a relationship that could be further explored.