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

MUR Sea Surface Temperature Analysis of Washington State

Requirement:

Compute environment

This tutorial can only be run in an AWS cloud 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.

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.

Learning Objectives:

Access cloud-stored MUR Global SST data within AWS cloud, without downloading it to your local machine
Visualize and analyze data in a use case example
Use earthaccess library to search for, access, and download the data

GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) Dataset:

MUR-JPL-L4-GLOB-v4.1

DOI: https://doi.org/10.5067/GHGMR-4FJ04

Notebook Author: Zoë Walschots, NASA PO.DAAC (Aug 2023)

Import Packages

import h5netcdf
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import cartopy.crs as ccrs
import cartopy.feature as cfeat
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store

In this notebook, we will be calling the authentication in the below cell.

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

Access and Visualize Data

Let’s look at the Sea Surface Temperature of first summer we have data for (2012) using earthaccess

mur_results = earthaccess.search_data(short_name = 'MUR-JPL-L4-GLOB-v4.1', temporal = ('2012-05-21', '2012-08-20'), bounding_box = ('-125.41992','45.61181','-116.64844','49.2315'))

Granules found: 92

ds_mur = xr.open_mfdataset(earthaccess.open(mur_results), engine = 'h5netcdf')
ds_mur

 Opening 92 granules, approx size: 0.0 GB

<xarray.Dataset>
Dimensions:           (time: 92, lat: 17999, lon: 36000)
Coordinates:
  * time              (time) datetime64[ns] 2012-05-21T09:00:00 ... 2012-08-2...
  * lat               (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99
  * lon               (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0
Data variables:
    analysed_sst      (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>
    analysis_error    (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>
    mask              (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>
    sea_ice_fraction  (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>
Attributes: (12/47)
    Conventions:                CF-1.5
    title:                      Daily MUR SST, Final product
    summary:                    A merged, multi-sensor L4 Foundation SST anal...
    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...
    institution:                Jet Propulsion Laboratory
    history:                    created at nominal 4-day latency; replaced nr...
    ...                         ...
    project:                    NASA Making Earth Science Data Records for Us...
    publisher_name:             GHRSST Project Office
    publisher_url:              http://www.ghrsst.org
    publisher_email:            ghrsst-po@nceo.ac.uk
    processing_level:           L4
    cdm_data_type:              grid

Indexes: (3)

time

PandasIndex

PandasIndex(DatetimeIndex(['2012-05-21 09:00:00', '2012-05-22 09:00:00',
               '2012-05-23 09:00:00', '2012-05-24 09:00:00',
               '2012-05-25 09:00:00', '2012-05-26 09:00:00',
               '2012-05-27 09:00:00', '2012-05-28 09:00:00',
               '2012-05-29 09:00:00', '2012-05-30 09:00:00',
               '2012-05-31 09:00:00', '2012-06-01 09:00:00',
               '2012-06-02 09:00:00', '2012-06-03 09:00:00',
               '2012-06-04 09:00:00', '2012-06-05 09:00:00',
               '2012-06-06 09:00:00', '2012-06-07 09:00:00',
               '2012-06-08 09:00:00', '2012-06-09 09:00:00',
               '2012-06-10 09:00:00', '2012-06-11 09:00:00',
               '2012-06-12 09:00:00', '2012-06-13 09:00:00',
               '2012-06-14 09:00:00', '2012-06-15 09:00:00',
               '2012-06-16 09:00:00', '2012-06-17 09:00:00',
               '2012-06-18 09:00:00', '2012-06-19 09:00:00',
               '2012-06-20 09:00:00', '2012-06-21 09:00:00',
               '2012-06-22 09:00:00', '2012-06-23 09:00:00',
               '2012-06-24 09:00:00', '2012-06-25 09:00:00',
               '2012-06-26 09:00:00', '2012-06-27 09:00:00',
               '2012-06-28 09:00:00', '2012-06-29 09:00:00',
               '2012-06-30 09:00:00', '2012-07-01 09:00:00',
               '2012-07-02 09:00:00', '2012-07-03 09:00:00',
               '2012-07-04 09:00:00', '2012-07-05 09:00:00',
               '2012-07-06 09:00:00', '2012-07-07 09:00:00',
               '2012-07-08 09:00:00', '2012-07-09 09:00:00',
               '2012-07-10 09:00:00', '2012-07-11 09:00:00',
               '2012-07-12 09:00:00', '2012-07-13 09:00:00',
               '2012-07-14 09:00:00', '2012-07-15 09:00:00',
               '2012-07-16 09:00:00', '2012-07-17 09:00:00',
               '2012-07-18 09:00:00', '2012-07-19 09:00:00',
               '2012-07-20 09:00:00', '2012-07-21 09:00:00',
               '2012-07-22 09:00:00', '2012-07-23 09:00:00',
               '2012-07-24 09:00:00', '2012-07-25 09:00:00',
               '2012-07-26 09:00:00', '2012-07-27 09:00:00',
               '2012-07-28 09:00:00', '2012-07-29 09:00:00',
               '2012-07-30 09:00:00', '2012-07-31 09:00:00',
               '2012-08-01 09:00:00', '2012-08-02 09:00:00',
               '2012-08-03 09:00:00', '2012-08-04 09:00:00',
               '2012-08-05 09:00:00', '2012-08-06 09:00:00',
               '2012-08-07 09:00:00', '2012-08-08 09:00:00',
               '2012-08-09 09:00:00', '2012-08-10 09:00:00',
               '2012-08-11 09:00:00', '2012-08-12 09:00:00',
               '2012-08-13 09:00:00', '2012-08-14 09:00:00',
               '2012-08-15 09:00:00', '2012-08-16 09:00:00',
               '2012-08-17 09:00:00', '2012-08-18 09:00:00',
               '2012-08-19 09:00:00', '2012-08-20 09:00:00'],
              dtype='datetime64[ns]', name='time', freq=None))

lat

PandasIndex

PandasIndex(Index([-89.98999786376953,  -89.9800033569336, -89.97000122070312,
       -89.95999908447266, -89.94999694824219, -89.94000244140625,
       -89.93000030517578, -89.91999816894531, -89.91000366210938,
        -89.9000015258789,
       ...
         89.9000015258789,  89.91000366210938,  89.91999816894531,
        89.93000030517578,  89.94000244140625,  89.94999694824219,
        89.95999908447266,  89.97000122070312,   89.9800033569336,
        89.98999786376953],
      dtype='float32', name='lat', length=17999))

lon

PandasIndex

PandasIndex(Index([-179.99000549316406, -179.97999572753906, -179.97000122070312,
        -179.9600067138672,  -179.9499969482422, -179.94000244140625,
       -179.92999267578125,  -179.9199981689453, -179.91000366210938,
       -179.89999389648438,
       ...
        179.91000366210938,   179.9199981689453,  179.92999267578125,
        179.94000244140625,   179.9499969482422,   179.9600067138672,
        179.97000122070312,  179.97999572753906,  179.99000549316406,
                     180.0],
      dtype='float32', name='lon', length=36000))

Attributes: (47)

Conventions :: CF-1.5
title :: Daily MUR SST, Final product
summary :: A merged, multi-sensor L4 Foundation SST analysis product from JPL.
references :: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST
institution :: Jet Propulsion Laboratory
history :: created at nominal 4-day latency; replaced nrt (1-day latency) version.
comment :: MUR = "Multi-scale Ultra-high Reolution"
license :: These data are available free of charge under data policy of JPL PO.DAAC.
id :: MUR-JPL-L4-GLOB-v04.1
naming_authority :: org.ghrsst
product_version :: 04.1
uuid :: 27665bc0-d5fc-11e1-9b23-0800200c9a66
gds_version_id :: 2.0
netcdf_version_id :: 4.1
date_created :: 20150711T060041Z
start_time :: 20120521T090000Z
stop_time :: 20120521T090000Z
time_coverage_start :: 20120520T210000Z
time_coverage_end :: 20120521T210000Z
file_quality_level :: 1
source :: AVHRR18_G-NAVO, AVHRR19_G-NAVO, AVHRR_METOP_A-EUMETSAT, MODIS_A-JPL, MODIS_T-JPL, WSAT-REMSS, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF
platform :: Aqua, DMSP, NOAA-POES, Suomi-NPP, Terra
sensor :: AMSR-E, AVHRR, MODIS, SSM/I, VIIRS, in-situ
Metadata_Conventions :: Unidata Observation Dataset v1.0
metadata_link :: http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1
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
southernmost_latitude :: -90.0
northernmost_latitude :: 90.0
westernmost_longitude :: -180.0
easternmost_longitude :: 180.0
spatial_resolution :: 0.01 degrees
geospatial_lat_units :: degrees north
geospatial_lat_resolution :: 0.01 degrees
geospatial_lon_units :: degrees east
geospatial_lon_resolution :: 0.01 degrees
acknowledgment :: Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.
creator_name :: JPL MUR SST project
creator_email :: ghrsst@podaac.jpl.nasa.gov
creator_url :: http://mur.jpl.nasa.gov
project :: NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program
publisher_name :: GHRSST Project Office
publisher_url :: http://www.ghrsst.org
publisher_email :: ghrsst-po@nceo.ac.uk
processing_level :: L4
cdm_data_type :: grid

# we want the sea surface temperature variable for this visualization ds = ds_mur['analysed_sst'] # Subset the dataset so that the program can run the code better ds_subset = ds.sel(time=slice('2012-05-21T09:00:00', '2012-08-20T09:00:00')) lat_range = slice(45.61181, 49.2315) lon_range = slice(-125.41992, -116.64844) ds_subset = ds_subset.sel(lat=lat_range, lon=lon_range)

#plot the figure fig = plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.PlateCarree()) colorbar_range = (282, 291) im = ax.imshow(mean_data.values, cmap='YlOrRd', origin='lower', transform=ccrs.PlateCarree(), extent=[mean_data.lon.min(), mean_data.lon.max(), mean_data.lat.min(), mean_data.lat.max()]) im.set_clim(vmin=colorbar_range[0], vmax=colorbar_range[1]) cbar = plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7) cbar.set_label('Sea Surface Temperature (Kelvin)') ax.set_title('Mean Sea Surface Temperature Summer 2012') ax.coastlines() ax.gridlines() plt.show()

fig = plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.PlateCarree()) colorbar_range = (282, 291) im = ax.imshow(mean_data_2.values, cmap='YlOrRd', origin='lower', transform=ccrs.PlateCarree(), extent=[mean_data_2.lon.min(), mean_data_2.lon.max(), mean_data_2.lat.min(), mean_data_2.lat.max()]) im.set_clim(vmin=colorbar_range[0], vmax=colorbar_range[1]) cbar = plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7) cbar.set_label('Sea Surface Temperature (Kelvin)') ax.set_title('Mean Sea Surface Temperature Summer 2022') ax.coastlines() ax.gridlines() plt.show()

fig = plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.PlateCarree()) colorbar_range = (-3, 3) im = ax.imshow(data_diff.values, cmap='coolwarm', origin='lower', transform=ccrs.PlateCarree(), extent=[mean_data.lon.min(), mean_data.lon.max(), mean_data.lat.min(), mean_data.lat.max()], vmin=colorbar_range[0], vmax=colorbar_range[1]) cbar = plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7) cbar.set_label('Sea Surface Temperature Difference (Kelvin)') ax.set_title('Sea Surface Temperature Difference (Avg of Summer 2022 - Avg of Summer 2012)') ax.coastlines() ax.gridlines() plt.show()

#subset the data for Lake Chelan in 2012 ds_subset_3 = ds.sel(time=slice('2012-05-21T09:00:00', '2012-08-20T09:00:00')) lat_range_2 = slice(47.7926, 48.44274) lon_range_2 = slice(-120.99902, -119.77734) ds_subset_3 = ds_subset_3.sel(lat=lat_range_2, lon=lon_range_2) mean_data_3 = ds_subset_3.mean(dim='time')

#subset the data for Lake Chelan in 2022 ds_subset_4 = ds_2.sel(time=slice('2022-05-21T09:00:00', '2022-08-20T09:00:00')) ds_subset_4 = ds_subset_4.sel(lat=lat_range_2, lon=lon_range_2) mean_data_4 = ds_subset_4.mean(dim='time')

fig = plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.PlateCarree()) colorbar_range = (2, 3.5) im = ax.imshow(data_diff_2.values, cmap='YlOrRd', origin='lower', transform=ccrs.PlateCarree(), extent=[data_diff_2.lon.min(), data_diff_2.lon.max(), data_diff_2.lat.min(), data_diff_2.lat.max()]) im.set_clim(vmin=colorbar_range[0], vmax=colorbar_range[1]) cbar = plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7) cbar.set_label('Sea Surface Temperature Difference (Kelvin)') ax.set_title('Lake Chelan Sea Surface Temperature Difference 2012 vs 2022') ax.gridlines() plt.show()