Download Chl-a products of multiple sources and make regional plots

Author: Jiaxu Zhang (UW, NOAA, jiaxu.zhang@noaa.gov)

This tutorial demonstrates how to download chlorophyll-a concentration products from various satellites and create regional plots at publication quality.

Contents

1. Download and process PACE OCI data

2. Download and process SNPP VIIRS data

3. Download and process MODIS data

4. Download and process merged data (OC CCI)

5. Combine the plots side by side

We begin by importing the packages used in this notebook.

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import xarray as xr
import earthaccess
import requests

import sys
sys.path.append("../scripts")
import PACEToolkit.regional_plot as regional_plot

1. Download and Process PACE OCI Data

This tutorial demonstrates how to use the earthaccess package to create PACE OCI Chl-a plots without downloading a local copy. In this tutorial, we will use the hypercoast package to achieve the same goal.

You may need to install hypercoast in your environment (uncomment the following line).

# %pip install "hypercoast[extra]"
import hypercoast

# hypercoast.nasa_earth_login()

# Temporal range
temporal = ("2024-06-01", "2024-06-15")  
# results= hypercoast.search_pace_chla(temporal=temporal, 
#                                      bbox = bbox)
# hypercoast.download_nasa_data(results, "../data/pace_chla")

files = "../data/pace_chla/*nc"
array = hypercoast.read_pace_chla(files)
array

<xarray.DataArray 'chlor_a' (lat: 1800, lon: 3600, date: 15)> Size: 389MB
dask.array<transpose, shape=(1800, 3600, 15), dtype=float32, chunksize=(512, 1024, 1), chunktype=numpy.ndarray>
Coordinates:
  * lat          (lat) float32 7kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95
  * lon          (lon) float32 14kB -179.9 -179.9 -179.8 ... 179.8 179.9 180.0
  * date         (date) <U10 600B '2024-06-01' '2024-06-02' ... '2024-06-15'
    spatial_ref  int64 8B 0
Attributes:
    long_name:      Chlorophyll Concentration, OCI Algorithm
    units:          lg(mg m^-3)
    standard_name:  mass_concentration_of_chlorophyll_in_sea_water
    valid_min:      0.001
    valid_max:      100.0
    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...
    display_scale:  log
    display_min:    0.01
    display_max:    20.0
    date:           ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', ...

xarray.DataArray

'chlor_a'

• lat: 1800
• lon: 3600
• date: 15

• dask.array<chunksize=(512, 1024, 1), meta=np.ndarray>

  Array Chunk Bytes 370.79 MiB 2.00 MiB Shape (1800, 3600, 15) (512, 1024, 1) Dask graph 240 chunks in 48 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • lat
    (lat)
    float32
    89.95 89.85 89.75 ... -89.85 -89.95

    long_name :

    Latitude

    units :

    degrees_north

    standard_name :

    latitude

    valid_min :

    -90.0

    valid_max :

    90.0

    array([ 89.95    ,  89.85    ,  89.75    , ..., -89.75    , -89.850006,
           -89.950005], dtype=float32)

  • lon
    (lon)
    float32
    -179.9 -179.9 ... 179.9 180.0

    long_name :

    Longitude

    units :

    degrees_east

    standard_name :

    longitude

    valid_min :

    -180.0

    valid_max :

    180.0

    array([-179.95   , -179.85   , -179.75   , ...,  179.75   ,  179.85   ,
            179.95001], dtype=float32)

  • date
    (date)
    <U10
    '2024-06-01' ... '2024-06-15'

    array(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',
           '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',
           '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15'],
          dtype='<U10')

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    array(0)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.94999694824219,   89.8499984741211,              89.75,
             89.6500015258789,  89.55000305175781,  89.44999694824219,
             89.3499984741211,              89.25,   89.1500015258789,
            89.05000305175781,
           ...
           -89.05000305175781,  -89.1500015258789,             -89.25,
           -89.35000610351562, -89.45000457763672, -89.55000305175781,
            -89.6500015258789,             -89.75, -89.85000610351562,
           -89.95000457763672],
          dtype='float32', name='lat', length=1800))

  • lon
    PandasIndex

    PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,
           -179.64999389648438,  -179.5500030517578,  -179.4499969482422,
           -179.35000610351562,             -179.25, -179.14999389648438,
            -179.0500030517578,
           ...
             179.0500030517578,  179.15000915527344,              179.25,
            179.35000610351562,  179.45001220703125,   179.5500030517578,
            179.65000915527344,              179.75,  179.85000610351562,
            179.95001220703125],
          dtype='float32', name='lon', length=3600))

  • date
    PandasIndex

    PandasIndex(Index(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05',
           '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10',
           '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15'],
          dtype='object', name='date'))

• Attributes: (10)

  long_name :

  Chlorophyll Concentration, OCI Algorithm

  units :

  lg(mg m^-3)

  standard_name :

  mass_concentration_of_chlorophyll_in_sea_water

  valid_min :

  0.001

  valid_max :

  100.0

  reference :

  Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.

  display_scale :

  log

  display_min :

  0.01

  display_max :

  20.0

  date :

  ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15']

hypercoast has a plot function viz_pace_chla to easily take a quick view of the mean value of the period.

hypercoast.viz_pace_chla(array, cmap="viridis", size=5)

<matplotlib.collections.QuadMesh at 0x7f55d66cca90>

Custermize the dataset and subset it for plotting. First we calculate the mean of the 15-day data.

array_mean = array.mean('date')
array_mean.attrs.update(
    {
        "long_name": f'PACE {array.attrs["long_name"]}',
        "units": f'lg({array.attrs["units"]})',
    }
)

Define the boundary of the area and perform the reduction. Note that the latitude values descend in the index, so the slice will flip the values lat_bnds = [max_lat, min_lat].

min_lon = -179     # lower left longitude
min_lat = 50       # lower left latitude
max_lon = -140     # upper right longitude
max_lat = 72       # upper right latitude

lon_bnds = [min_lon, max_lon]
lat_bnds = [max_lat, min_lat]
array_mean_clip = array_mean.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))

array_mean_clip

<xarray.DataArray 'chlor_a' (lat: 220, lon: 390)> Size: 343kB
dask.array<getitem, shape=(220, 390), dtype=float32, chunksize=(220, 390), chunktype=numpy.ndarray>
Coordinates:
  * lat          (lat) float32 880B 71.95 71.85 71.75 ... 50.25 50.15 50.05
  * lon          (lon) float32 2kB -178.9 -178.9 -178.8 ... -140.2 -140.1 -140.1
    spatial_ref  int64 8B 0
Attributes:
    long_name:  PACE Chlorophyll Concentration, OCI Algorithm
    units:      lg(lg(mg m^-3))

xarray.DataArray

'chlor_a'

• lat: 220
• lon: 390

• dask.array<chunksize=(220, 390), meta=np.ndarray>

  Array Chunk Bytes 335.16 kiB 335.16 kiB Shape (220, 390) (220, 390) Dask graph 1 chunks in 52 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float32
    71.95 71.85 71.75 ... 50.15 50.05

    long_name :

    Latitude

    units :

    degrees_north

    standard_name :

    latitude

    valid_min :

    -90.0

    valid_max :

    90.0

    array([71.95    , 71.85    , 71.75    , ..., 50.25    , 50.149998, 50.05    ],
          dtype=float32)

  • lon
    (lon)
    float32
    -178.9 -178.9 ... -140.1 -140.1

    long_name :

    Longitude

    units :

    degrees_east

    standard_name :

    longitude

    valid_min :

    -180.0

    valid_max :

    180.0

    array([-178.95, -178.85, -178.75, ..., -140.25, -140.15, -140.05],
          dtype=float32)

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    array(0)

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([ 71.94999694824219,   71.8499984741211,              71.75,
             71.6500015258789,  71.55000305175781,  71.44999694824219,
             71.3499984741211,              71.25,   71.1500015258789,
            71.05000305175781,
           ...
            50.95000076293945, 50.849998474121094,              50.75,
            50.64999771118164,  50.54999923706055,  50.45000076293945,
           50.349998474121094,              50.25,  50.14999771118164,
            50.04999923706055],
          dtype='float32', name='lat', length=220))

  • lon
    PandasIndex

    PandasIndex(Index([ -178.9499969482422, -178.85000610351562,             -178.75,
           -178.64999389648438,  -178.5500030517578,  -178.4499969482422,
           -178.35000610351562,             -178.25, -178.14999389648438,
            -178.0500030517578,
           ...
            -140.9499969482422, -140.85000610351562,             -140.75,
           -140.64999389648438,  -140.5500030517578,  -140.4499969482422,
           -140.35000610351562,             -140.25, -140.14999389648438,
            -140.0500030517578],
          dtype='float32', name='lon', length=390))

• Attributes: (2)

  long_name :

  PACE Chlorophyll Concentration, OCI Algorithm

  units :

  lg(lg(mg m^-3))

# Plot PACE data
m = regional_plot.plot_regional_map(array_mean_clip, min_lon, max_lon, min_lat, max_lat, 
                 title='{} to {}'.format(temporal[0],temporal[1]), 
                 vmin=-1.5, vmax=1.5, size=(5,5))

back to top

2. Download and process SNPP VIIRS data

There are many different ways to obtain L3 data (e.g., from https://oceancolor.gsfc.nasa.gov/l3/)
Here we show how to get data from NOAA’s ERDDAP server (https://oceanwatch.pifsc.noaa.gov/erddap/index.html). ERDDAP is both a data server and a web application developed by NOAA’s Environmental Research Division. It provides a user-friendly web interface for searching, accessing, and visualizing data.

### Uncomment the following lines if you need to download the data for the first time

# import urllib.request 
# url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/noaa_snpp_chla_daily.nc?chlor_a%5B(2024-06-01T12:00:00Z):1:(2024-06-15T12:00:00Z)%5D%5B(0.0):1:(0.0)%5D%5B(77.00625):1:(50.006249999999994)%5D%5B(180.01875):1:(221.98125000000002)%5D'
# urllib.request.urlretrieve(url, "../data/viirs.nc")

viirs_ds = xr.open_dataset('../data/viirs.nc')

Since the dataset is already subsetted for the area of interest, the next step is to compute the 15-day mean and create plots.

viirs_chla = np.log10(viirs_ds['chlor_a'])
# Calculate the temporal mean
viirs_chla_mean = viirs_chla.mean('time')

viirs_chla_mean.attrs.update(
    {
        "long_name": "SNPP VIIRS, L3 Chlorophyll a Concentration",
        "units": 'lg[lg[{}]]'.format(viirs_ds['chlor_a'].attrs["units"]),
    }
)

# Plot VIIRS data
m = regional_plot.plot_regional_map(viirs_chla_mean, min_lon, max_lon, min_lat, max_lat, 
                 title='{} to {}'.format(temporal[0],temporal[1]), 
                 vmin=-1.5, vmax=1.5, size=(5,5))

back to top

3. Download and process MODIS data

You may visit NASA Earthdata Search and enter the short names to read about each data collection. We want to use the MODISA_L3m_CHL data collection for our plot. You can retrieve the files (granules) in that collection using earthaccess.search_data().

tspan = ("2023-06-01", "2023-06-15")

# auth = earthaccess.login(persist=True)

# results = earthaccess.search_data(
#     short_name="MODISA_L3m_CHL",
#     granule_name="*.8D*.9km*",
#     temporal=tspan,
# )

# results[0]

By checking the first data, it indicates that the file is not cloud-hosted. We will need to download the data file with earthaccess.download().

# paths = earthaccess.download(results, "../data/modis_chla")

paths = [
    '../data/modis_chla/AQUA_MODIS.20230525_20230601.L3m.8D.CHL.chlor_a.9km.nc',
    '../data/modis_chla/AQUA_MODIS.20230602_20230609.L3m.8D.CHL.chlor_a.9km.nc',
    '../data/modis_chla/AQUA_MODIS.20230610_20230617.L3m.8D.CHL.chlor_a.9km.nc'
]

modis_ds = xr.open_mfdataset(
    paths,
    combine="nested",
    concat_dim="date",
)
modis_ds

<xarray.Dataset> Size: 112MB
Dimensions:  (date: 3, lat: 2160, lon: 4320, rgb: 3, eightbitcolor: 256)
Coordinates:
  * lat      (lat) float32 9kB 89.96 89.88 89.79 89.71 ... -89.79 -89.88 -89.96
  * lon      (lon) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9 180.0
Dimensions without coordinates: date, rgb, eightbitcolor
Data variables:
    chlor_a  (date, lat, lon) float32 112MB dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>
    palette  (date, rgb, eightbitcolor) uint8 2kB dask.array<chunksize=(1, 3, 256), meta=np.ndarray>
Attributes: (12/64)
    product_name:                      AQUA_MODIS.20230525_20230601.L3m.8D.CH...
    instrument:                        MODIS
    title:                             MODISA Level-3 Standard Mapped Image
    project:                           Ocean Biology Processing Group (NASA/G...
    platform:                          Aqua
    source:                            satellite observations from MODIS-Aqua
    ...                                ...
    identifier_product_doi:            10.5067/AQUA/MODIS/L3M/CHL/2022
    keywords:                          Earth Science > Oceans > Ocean Chemist...
    keywords_vocabulary:               NASA Global Change Master Directory (G...
    data_bins:                         3087431
    data_minimum:                      0.0031313652
    data_maximum:                      86.3219

xarray.Dataset

• Dimensions:
  • date: 3
  • lat: 2160
  • lon: 4320
  • rgb: 3
  • eightbitcolor: 256
• Coordinates: (2)
  • lat
    (lat)
    float32
    89.96 89.88 89.79 ... -89.88 -89.96

    long_name :

    Latitude

    units :

    degrees_north

    standard_name :

    latitude

    valid_min :

    -90.0

    valid_max :

    90.0

    array([ 89.958336,  89.875   ,  89.791664, ..., -89.79167 , -89.87501 ,
           -89.958336], dtype=float32)

  • lon
    (lon)
    float32
    -180.0 -179.9 ... 179.9 180.0

    long_name :

    Longitude

    units :

    degrees_east

    standard_name :

    longitude

    valid_min :

    -180.0

    valid_max :

    180.0

    array([-179.95833, -179.875  , -179.79167, ...,  179.79167,  179.87502,
            179.95834], dtype=float32)

• Data variables: (2)
  • chlor_a
    (date, lat, lon)
    float32
    dask.array<chunksize=(1, 512, 1024), meta=np.ndarray>

    long_name :

    Chlorophyll Concentration, OCI Algorithm

    units :

    mg m^-3

    standard_name :

    mass_concentration_of_chlorophyll_in_sea_water

    valid_min :

    0.001

    valid_max :

    100.0

    reference :

    Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.

    display_scale :

    log

    display_min :

    0.01

    display_max :

    20.0

    Array Chunk Bytes 106.79 MiB 2.00 MiB Shape (3, 2160, 4320) (1, 512, 1024) Dask graph 75 chunks in 10 graph layers Data type float32 numpy.ndarray

  • palette
    (date, rgb, eightbitcolor)
    uint8
    dask.array<chunksize=(1, 3, 256), meta=np.ndarray>

    Array Chunk Bytes 2.25 kiB 768 B Shape (3, 3, 256) (1, 3, 256) Dask graph 3 chunks in 10 graph layers Data type uint8 numpy.ndarray

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.95833587646484,             89.875,  89.79166412353516,
            89.70833587646484,             89.625,  89.54166412353516,
            89.45833587646484,             89.375,  89.29166412353516,
            89.20833587646484,
           ...
           -89.20833587646484, -89.29167175292969, -89.37500762939453,
           -89.45833587646484, -89.54167175292969, -89.62500762939453,
           -89.70833587646484, -89.79167175292969, -89.87500762939453,
           -89.95833587646484],
          dtype='float32', name='lat', length=2160))

  • lon
    PandasIndex

    PandasIndex(Index([-179.9583282470703,           -179.875, -179.7916717529297,
           -179.7083282470703,           -179.625, -179.5416717529297,
           -179.4583282470703,           -179.375, -179.2916717529297,
           -179.2083282470703,
           ...
           179.20834350585938,  179.2916717529297, 179.37501525878906,
           179.45834350585938,  179.5416717529297, 179.62501525878906,
           179.70834350585938,  179.7916717529297, 179.87501525878906,
           179.95834350585938],
          dtype='float32', name='lon', length=4320))

• Attributes: (64)

  product_name :

  AQUA_MODIS.20230525_20230601.L3m.8D.CHL.chlor_a.9km.nc

  instrument :

  MODIS

  title :

  MODISA Level-3 Standard Mapped Image

  project :

  Ocean Biology Processing Group (NASA/GSFC/OBPG)

  platform :

  Aqua

  source :

  satellite observations from MODIS-Aqua

  temporal_range :

  8-day

  processing_version :

  R2022.0.1

  date_created :

  2024-07-16T03:30:21.000Z

  history :

  l3mapgen par=AQUA_MODIS.20230525_20230601.L3m.8D.CHL.chlor_a.9km.nc.param

  l2_flag_names :

  ATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,CLDICE,COCCOLITH,LOWLW,CHLWARN,CHLFAIL,NAVWARN,MAXAERITER,ATMWARN,HISOLZEN,NAVFAIL,FILTER,HIGLINT

  time_coverage_start :

  2023-05-25T00:15:01.000Z

  time_coverage_end :

  2023-06-02T02:50:00.000Z

  start_orbit_number :

  111999

  end_orbit_number :

  112117

  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 :

  9.276624 km

  geospatial_lon_resolution :

  9.276624 km

  geospatial_lat_resolution :

  9.276624 km

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_east

  number_of_lines :

  2160

  number_of_columns :

  4320

  measure :

  Mean

  suggested_image_scaling_minimum :

  0.01

  suggested_image_scaling_maximum :

  20.0

  suggested_image_scaling_type :

  LOG

  suggested_image_scaling_applied :

  No

  _lastModified :

  2024-07-16T03:30:21.000Z

  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 :

  R2022.0.1/L3/AQUA_MODIS.20230525_20230601.L3b.8D.CHL.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

  identifier_product_doi_authority :

  http://dx.doi.org

  identifier_product_doi :

  10.5067/AQUA/MODIS/L3M/CHL/2022

  keywords :

  Earth Science > Oceans > Ocean Chemistry > Pigments > Chlorophyll; Earth Science > Oceans > Ocean Chemistry > Chlorophyll

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  data_bins :

  3087431

  data_minimum :

  0.0031313652

  data_maximum :

  86.3219

Similar to previous steps, we calculate the log10 of the chlorophyll concentration, compute the 15-day average, and perform the reduction.

modis_chla = np.log10(modis_ds["chlor_a"])
modis_chla_mean = modis_chla.mean('date')
modis_chla_mean.attrs.update(
    {
        "long_name": f'MODIS {modis_ds["chlor_a"].attrs["long_name"]}',
        "units": f'log({modis_ds["chlor_a"].attrs["units"]})',
    }
)
modis_chla_mean_clip = modis_chla_mean.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))

modis_chla_mean_clip

<xarray.DataArray 'chlor_a' (lat: 264, lon: 468)> Size: 494kB
dask.array<getitem, shape=(264, 468), dtype=float32, chunksize=(264, 468), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float32 1kB 71.96 71.88 71.79 71.71 ... 50.21 50.12 50.04
  * lon      (lon) float32 2kB -179.0 -178.9 -178.8 ... -140.2 -140.1 -140.0
Attributes:
    long_name:  MODIS Chlorophyll Concentration, OCI Algorithm
    units:      log(mg m^-3)

xarray.DataArray

'chlor_a'

• lat: 264
• lon: 468

• dask.array<chunksize=(264, 468), meta=np.ndarray>

  Array Chunk Bytes 482.62 kiB 482.62 kiB Shape (264, 468) (264, 468) Dask graph 1 chunks in 14 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • lat
    (lat)
    float32
    71.96 71.88 71.79 ... 50.12 50.04

    long_name :

    Latitude

    units :

    degrees_north

    standard_name :

    latitude

    valid_min :

    -90.0

    valid_max :

    90.0

    array([71.958336, 71.875   , 71.791664, ..., 50.208332, 50.125   , 50.041664],
          dtype=float32)

  • lon
    (lon)
    float32
    -179.0 -178.9 ... -140.1 -140.0

    long_name :

    Longitude

    units :

    degrees_east

    standard_name :

    longitude

    valid_min :

    -180.0

    valid_max :

    180.0

    array([-178.95833, -178.875  , -178.79167, ..., -140.20833, -140.125  ,
           -140.04167], dtype=float32)

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([ 71.95833587646484,             71.875,  71.79166412353516,
            71.70833587646484,             71.625,  71.54166412353516,
            71.45833587646484,             71.375,  71.29166412353516,
            71.20833587646484,
           ...
           50.791664123535156,  50.70833206176758,             50.625,
           50.541664123535156,  50.45833206176758,             50.375,
           50.291664123535156,  50.20833206176758,             50.125,
           50.041664123535156],
          dtype='float32', name='lat', length=264))

  • lon
    PandasIndex

    PandasIndex(Index([-178.9583282470703,           -178.875, -178.7916717529297,
           -178.7083282470703,           -178.625, -178.5416717529297,
           -178.4583282470703,           -178.375, -178.2916717529297,
           -178.2083282470703,
           ...
           -140.7916717529297, -140.7083282470703,           -140.625,
           -140.5416717529297, -140.4583282470703,           -140.375,
           -140.2916717529297, -140.2083282470703,           -140.125,
           -140.0416717529297],
          dtype='float32', name='lon', length=468))

• Attributes: (2)

  long_name :

  MODIS Chlorophyll Concentration, OCI Algorithm

  units :

  log(mg m^-3)

# Plot MODIS data
m = regional_plot.plot_regional_map(modis_chla_mean_clip, min_lon, max_lon, min_lat, max_lat, 
                 title='{} to {}'.format(tspan[0],tspan[1]), 
                 vmin=-1.5, vmax=1.5, size=(5,5))

back to top

4. Download and process merged data (OC CCI)

The Ocean Colour Climate Change Initiative (OC CCI) composite of merged sensors is a high-resolution dataset that combines ocean color data from multiple satellite sensors. This composite product aims to provide consistent, global coverage of ocean color, which includes key parameters such as chlorophyll concentration, Kd. By merging data from various sensors, it minimizes gaps and inconsistencies, offering a more reliable and comprehensive view of oceanic biogeochemical properties.

Here we download the daily composit of merged sensor at 1-km resolution from NOAA’s ERDDAP server (https://comet.nefsc.noaa.gov/erddap/griddap).

# import urllib.request 
# url = 'https://comet.nefsc.noaa.gov/erddap/griddap/occci_v6_daily_1km.nc?chlor_a%5B(2024-06-01):1:(2024-06-15)%5D%5B(75.00520833333333):1:(49.99479166666667)%5D%5B(-179.99479166666666):1:(-139.99479166666669)%5D'
# urllib.request.urlretrieve(url, "../../data/occci_v6_daily_1km.nc")

occci_ds = xr.open_dataset('../data/occci_v6_daily_1km.nc')

occci_ds

<xarray.Dataset> Size: 554MB
Dimensions:    (time: 15, latitude: 2402, longitude: 3841)
Coordinates:
  * time       (time) datetime64[ns] 120B 2024-06-01 2024-06-02 ... 2024-06-15
  * latitude   (latitude) float64 19kB 75.01 74.99 74.98 ... 50.02 50.01 49.99
  * longitude  (longitude) float64 31kB -180.0 -180.0 -180.0 ... -140.0 -140.0
Data variables:
    chlor_a    (time, latitude, longitude) float32 554MB ...
Attributes: (12/52)
    cdm_data_type:                     Grid
    comment:                           See summary attribute
    Conventions:                       CF-1.10, COARDS, ACDD-1.3
    creation_date:                     20240807T105724Z
    creator_email:                     help@esa-oceancolour-cci.org
    creator_name:                      Plymouth Marine Laboratory
    ...                                ...
    time_coverage_end:                 2024-06-15T00:00:00Z
    time_coverage_resolution:          P1D
    time_coverage_start:               2024-06-01T00:00:00Z
    title:                             Ocean Color, ESA CCI Ocean Colour Prod...
    tracking_id:                       f70a045f-3da8-4ab1-ae9a-7c75a620f07d
    Westernmost_Easting:               -179.99479166666666

xarray.Dataset

• Dimensions:
  • time: 15
  • latitude: 2402
  • longitude: 3841
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2024-06-01 ... 2024-06-15

    _CoordinateAxisType :

    Time

    actual_range :

    [1.7172000e+09 1.7184096e+09]

    axis :

    T

    ioos_category :

    Time

    long_name :

    Time

    standard_name :

    time

    time_origin :

    01-JAN-1970 00:00:00

    array(['2024-06-01T00:00:00.000000000', '2024-06-02T00:00:00.000000000',
           '2024-06-03T00:00:00.000000000', '2024-06-04T00:00:00.000000000',
           '2024-06-05T00:00:00.000000000', '2024-06-06T00:00:00.000000000',
           '2024-06-07T00:00:00.000000000', '2024-06-08T00:00:00.000000000',
           '2024-06-09T00:00:00.000000000', '2024-06-10T00:00:00.000000000',
           '2024-06-11T00:00:00.000000000', '2024-06-12T00:00:00.000000000',
           '2024-06-13T00:00:00.000000000', '2024-06-14T00:00:00.000000000',
           '2024-06-15T00:00:00.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float64
    75.01 74.99 74.98 ... 50.01 49.99

    _CoordinateAxisType :

    Lat

    actual_range :

    [49.99479167 75.00520833]

    axis :

    Y

    ioos_category :

    Location

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    89.99479166666667

    valid_min :

    -89.99479166666666

    array([75.005208, 74.994792, 74.984375, ..., 50.015625, 50.005208, 49.994792])

  • longitude
    (longitude)
    float64
    -180.0 -180.0 ... -140.0 -140.0

    _CoordinateAxisType :

    Lon

    actual_range :

    [-179.99479167 -139.99479167]

    axis :

    X

    ioos_category :

    Location

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    179.99479166666663

    valid_min :

    -179.99479166666666

    array([-179.994792, -179.984375, -179.973958, ..., -140.015625, -140.005208,
           -139.994792])

• Data variables: (1)
  • chlor_a
    (time, latitude, longitude)
    float32
    ...

    ancillary_variables :

    chlor_a_log10_rmsd chlor_a_log10_bias

    colorBarMaximum :

    30.0

    colorBarMinimum :

    0.03

    colorBarScale :

    Log

    ioos_category :

    Ocean Color

    long_name :

    Chlorophyll-a concentration in seawater (not log-transformed), generated by as a blended combination of OCI, OCI2, OC2 and OCx algorithms, depending on water class memberships

    parameter_vocab_uri :

    http://vocab.ndg.nerc.ac.uk/term/P011/current/CHLTVOLU

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    units :

    milligram m-3

    units_nonstandard :

    mg m^-3

    [138391230 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04',
                   '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08',
                   '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12',
                   '2024-06-13', '2024-06-14', '2024-06-15'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 75.00520833333333,  74.99479166666667,          74.984375,
            74.97395833333333,  74.96354166666667,          74.953125,
            74.94270833333333,  74.93229166666667,          74.921875,
            74.91145833333333,
           ...
            50.08854166666667,          50.078125, 50.067708333333336,
            50.05729166666667,          50.046875, 50.036458333333336,
            50.02604166666667,          50.015625, 50.005208333333336,
            49.99479166666667],
          dtype='float64', name='latitude', length=2402))

  • longitude
    PandasIndex

    PandasIndex(Index([-179.99479166666666,         -179.984375, -179.97395833333334,
           -179.96354166666666,         -179.953125, -179.94270833333334,
           -179.93229166666666,         -179.921875, -179.91145833333334,
           -179.90104166666666,
           ...
           -140.08854166666669,         -140.078125, -140.06770833333334,
           -140.05729166666669,         -140.046875, -140.03645833333334,
           -140.02604166666669,         -140.015625, -140.00520833333334,
           -139.99479166666669],
          dtype='float64', name='longitude', length=3841))

• Attributes: (52)

  cdm_data_type :

  Grid

  comment :

  See summary attribute

  Conventions :

  CF-1.10, COARDS, ACDD-1.3

  creation_date :

  20240807T105724Z

  creator_email :

  help@esa-oceancolour-cci.org

  creator_name :

  Plymouth Marine Laboratory

  creator_url :

  https://esa-oceancolour-cci.org

  date_created :

  2024-03-18T17:58:43Z

  Easternmost_Easting :

  -139.99479166666669

  geospatial_lat_max :

  75.00520833333333

  geospatial_lat_min :

  49.99479166666667

  geospatial_lat_resolution :

  0.010416666666666666

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  -139.99479166666669

  geospatial_lon_min :

  -179.99479166666666

  geospatial_lon_resolution :

  0.010416666666666664

  geospatial_lon_units :

  degrees_east

  git_commit_hash :

  96e2d8a5fb609f69dbdde86199b5de144d1026ec

  grid_mapping_name :

  latitude_longitude

  history :

  Wed Aug 7 12:16:20 2024: ncks -x -v crs standardised_geo/ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_1km_GEO_PML_OCx_QAA-20240726-fv6.0_1km.nc ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_1km_GEO_PML_OCx_QAA-20240726-fv6.0_1km.nc Source data were: NASA OBPG SeaWiFS level1 R2018.0 LAC and GAC [A/C via l2gen], NASA OBPG VIIRS L1 R2018.0 [A/C via polymer], NASA OBPG MODIS Aqua L1 R2018.0 (A/C via polymer), ESA MERIS L1B (3rd reprocessing inc OCL correction) [Polymer v4.12] and OLCI L1B [Polymer v4.12]; Derived products were mainly produced with functions validated from the current NASA SeaDAS release and some custom implementations. Uncertainty generation determined by the fuzzy classifier scheme of Tim Moore (2009) as updated by Jackson et al. (2017) and Jackson et al. (in prep). 2024-08-08T01:32:16Z https://www.oceancolour.org/thredds/dodsC/CCI_ALL-v6.0-1km-DAILY 2024-08-08T01:32:16Z http://comet.nefsc.noaa.gov/erddap/griddap/occci_v6_daily_1km.nc?chlor_a%5B(2024-06-01):1:(2024-06-15)%5D%5B(75.00520833333333):1:(49.99479166666667)%5D%5B(-179.99479166666666):1:(-139.99479166666669)%5D

  id :

  ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_1km_GEO_PML_OCx_QAA-20240726-fv6.0_1km.nc

  infoUrl :

  https://esa-oceancolour-cci.org/

  institution :

  Plymouth Marine Laboratory

  keywords :

  1km, 490nm, absorption, adapted, adg_443, air, algorithm, algorithms, all, all-v6.0-1km-daily, aph_443, aqua, area, array, array-data, atmosphere, atmospheric, atot_443, attenuation, bbw, best, bin, blended, cci, cell, chemistry, chlor_a, chlorophyll, chlorophyll-a, class, classes, classification, coefficient, color, colour, combination, comprehensive, concentration, contributing, count, coverage, custom, custom-developed, daily, data, day, defined, depending, derived, determined, detrital, developed, dissolved, downwelling, earth, Earth Science > Atmosphere > Atmospheric Radiation > Radiative Flux, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, Earth Science > Oceans > Ocean Optics > Absorption, Earth Science > Oceans > Ocean Optics > Attenuation/Transmission, Earth Science > Oceans > Ocean Optics > Radiance, emerging, equation, esa, field, field-of-view, flux, following, fuzzy, gac, generated, global, imager, imager/radiometer, imaging, infrared, irradiance, jackson, kd_490, laboratory, lac, large, latitude, leaving, lee, local, log, log-transformed, longitude, marine, mass, mass_concentration_of_chlorophyll_a_in_sea_water, material, membership, memberships, merged, meris, MERIS_nobs, model, moderate, modis, MODISA_nobs, moore, normalised, not, number, observation, observations, oc2, ocean, ocean color, ocean colour, oceans, oci, oci2, ocx, olci, OLCI_A_nobs, OLCI_B_nobs, optical, optical properties, optics, parameterised, phytoplankton, plymouth, prep, product, properties, qaa, quality, radiance, radiation, radiative, radiometer, ratio, reflectance, represent, resolution, Rrs_412, Rrs_443, Rrs_490, Rrs_510, Rrs_560, Rrs_665, satellite, science, sea, sea-wide, seadas, seawater, seawifs, SeaWiFS_nobs, sensor, spectroradiometer, statistics, stewardship, suite, surface, surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air, system, tim, time, total, total_nobs, transformed, transmission, updates, upwelling, using, v6.0, view, viirs, VIIRS_nobs, visible, volume, volume_absorption_coefficient_of_radiative_flux_in_sea_water, volume_attenuation_coefficient_of_downwelling_radiative_flux_in_sea_water, water, water-leaving, water_class1, water_class10, water_class11, water_class12, water_class13, water_class14, water_class2, water_class3, water_class4, water_class5, water_class6, water_class7, water_class8, water_class9, wide, with, within, zhang

  keywords_vocabulary :

  GCMD Science Keywords

  license :

  ESA CCI Data Policy: free and open access. When referencing, please use: Ocean Colour Climate Change Initiative dataset, Version <Version Number>, European Space Agency, available online at https://esa-oceancolour-cci.org. We would also appreciate being notified of publications so that we can list them on the project website at https://esa-oceancolour-cci.org/?q=publications

  naming_authority :

  uk.ac.pml

  NCO :

  netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)

  Northernmost_Northing :

  75.00520833333333

  number_of_bands_used_to_classify :

  4

  number_of_optical_water_types :

  14

  platform :

  Orbview-2,Aqua,Envisat,Suomi-NPP, Sentinel-3a, Sentinel-3b

  processing_level :

  Level-3

  product_version :

  6.0_1km

  project :

  Climate Change Initiative - European Space Agency

  references :

  https://esa-oceancolour-cci.org/

  sensor :

  SeaWiFS,MODIS,MERIS,VIIRS,OLCI

  sensors_present :

  OLCIa OLCIb

  source :

  NASA SeaWiFS L1A and L2 R2018.0 LAC and GAC, MODIS-Aqua L1A and L2 R2018.0, MERIS L1B 3rd reprocessing inc OCL corrections, NASA VIIRS L1A and L2 R2018.0, OLCI L1B

  sourceUrl :

  https://www.oceancolour.org/thredds/dodsC/CCI_ALL-v6.0-1km-DAILY

  Southernmost_Northing :

  49.99479166666667

  spatial_resolution :

  1km nominal at equator

  standard_name_vocabulary :

  CF Standard Name Table v70

  summary :

  Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are daily composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products. MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007. VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30. VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer. The Rrs were binned to a sinusoidal 1km level-3 grid, and later to 1km geographic projection, by Brockmann Consult's SNAP. Derived products were generally computed with the standard algorithmsthrough SeaDAS. QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting. The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships. Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

  testOutOfDate :

  now-40days

  time_coverage_duration :

  P1D

  time_coverage_end :

  2024-06-15T00:00:00Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  2024-06-01T00:00:00Z

  title :

  Ocean Color, ESA CCI Ocean Colour Products (v6.0), Global 0.0104°, Daily, 1997-present

  tracking_id :

  f70a045f-3da8-4ab1-ae9a-7c75a620f07d

  Westernmost_Easting :

  -179.99479166666666

occci_chla = np.log10(occci_ds['chlor_a'])
# Calculate the temporal mean
occci_chla_mean = occci_chla.mean('time')

occci_chla_mean.attrs.update(
    {
        "long_name": "OCCCI Chlorophyll-a Concentration",
        "units": 'lg[lg[{}]]'.format(occci_ds['chlor_a'].attrs["units"]),
    }
)

# Plot OCCCI data
m = regional_plot.plot_regional_map(occci_chla_mean, min_lon, max_lon, min_lat, max_lat, 
                 title='{} to {}'.format(temporal[0],temporal[1]), 
                 vmin=-1.5, vmax=1.5, size=(5,5))

back to top

5. Combine the plots side by side

In this step, we will generate a 2x2 figure with four subplots.

# Calculate central longitude and latitude
central_longitude = (min_lon + max_lon) / 2
central_latitude = (min_lat + max_lat) / 2

# Define projection
projection = ccrs.LambertConformal(central_longitude=central_longitude, 
                                   central_latitude=central_latitude)

# Create the figure and axes
fig, axs = plt.subplots(2, 2, figsize=(7, 9), 
                        subplot_kw={'projection': projection})

# List of datasets and titles
datasets = [array_mean_clip, viirs_chla_mean, modis_chla_mean_clip, occci_chla_mean]
titles = ['{} to {}'.format(temporal[0], temporal[1]),
          '{} to {}'.format(temporal[0], temporal[1]),
          '{} to {}'.format(tspan[0], tspan[1]),
          '{} to {}'.format(temporal[0], temporal[1])]

# Loop through datasets and axes to plot each one
for i, (dataset, title) in enumerate(zip(datasets, titles)):
    ax = axs[i // 2, i % 2]
    regional_plot.plot_regional_map(dataset, min_lon, max_lon, min_lat, max_lat, 
                                    title=title, vmin=-1.5, vmax=1.5, ax=ax)

fig.tight_layout()

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