Visualizing PACE OCI L2 data products with HyperCoast¶

This notebook demonstrates how to visualize Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) OCI L2 data products, including the concentration of chlorophyll-a, concentration of phytoplankton carbon, and concentration of particulate organic carbon.

In [1]:

# %pip install "hypercoast[extra]"

# %pip install "hypercoast[extra]"

In [2]:

import hypercoast

import hypercoast

To download and access the data, you will need to create an Earthdata login. You can register for an account at urs.earthdata.nasa.gov. Once you have an account, you can uncomment and run the following cell to search and download PACE OCI L2 data products.

In [3]:

# hypercoast.nasa_earth_login()

# short_name = "PACE_OCI_L2_BGC_NRT"
# results, gdf = hypercoast.search_nasa_data(
#     short_name=short_name,
#     bbox=(-90.5642, 29.9749, -89.7143, 30.42),
#     temporal=("2024-06-15", "2024-06-16"),
#     return_gdf=True
#     )
# hypercoast.download_nasa_data(results, out_dir="bgc")

# hypercoast.nasa_earth_login() # short_name = "PACE_OCI_L2_BGC_NRT" # results, gdf = hypercoast.search_nasa_data( # short_name=short_name, # bbox=(-90.5642, 29.9749, -89.7143, 30.42), # temporal=("2024-06-15", "2024-06-16"), # return_gdf=True # ) # hypercoast.download_nasa_data(results, out_dir="bgc")

Alternatively, use the following code block to download a sample dataset from here.

In [4]:

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc"
filepath = "data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc"
hypercoast.download_file(url, filepath)

url = "https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc" filepath = "data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc" hypercoast.download_file(url, filepath)

Out[4]:

'/home/runner/work/HyperCoast/HyperCoast/docs/examples/data/PACE_OCI.20240615T182549.L2.OC_BGC.V1_0_0.NRT.nc'

Load the downloaded dataset as an xarray.Dataset:

In [5]:

dataset = hypercoast.read_pace_bgc(filepath)

dataset = hypercoast.read_pace_bgc(filepath)

Let's inspect the data variables contained in the dataset:

In [6]:

dataset.variables

dataset.variables

Out[6]:

Frozen({'chlor_a': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    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 alg..., 'carbon_phyto': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:  Phytoplankton Carbon
    units:      mg m^-3
    valid_min:  0.0
    valid_max:  1000.0
    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'poc': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:  Particulate Organic Carbon, D. Stramski, 2022 (hybrid version)
    units:      mg m^-3
    valid_min:  -32000
    valid_max:  -22000
    reference:  Stramski, D., et al. "Ocean color algorithms to estimate the ..., 'chlor_a_unc': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Uncertainty in chlorophyll a concentration
    units:          mg m^-3
    standard_name:  chlorophyll_concentration_in_sea_water standard_error
    valid_min:      0.001
    valid_max:      100.0, 'carbon_phyto_unc': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:  Phytoplankton Carbon standard uncertainty
    units:      mg m^-3
    valid_min:  0.0
    valid_max:  1000.0
    reference:  Graff, J.R., Westberry, T.K., Milligan, A.J., Brown, M.B., Da..., 'l2_flags': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=int32]
Attributes:
    long_name:      Level-2 Processing Flags
    valid_min:      -2147483648
    valid_max:      2147483647
    flag_masks:     [          1           2           4           8         ...
    flag_meanings:  ATMFAIL LAND PRODWARN HIGLINT HILT HISATZEN COASTZ SPARE ..., 'longitude': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Longitude
    units:          degrees_east
    standard_name:  longitude
    valid_min:      -180.0
    valid_max:      180.0, 'latitude': <xarray.Variable (latitude: 1710, longitude: 1272)> Size: 9MB
[2175120 values with dtype=float32]
Attributes:
    long_name:      Latitude
    units:          degrees_north
    standard_name:  latitude
    valid_min:      -90.0
    valid_max:      90.0})

We can see that the dataset contains the following variables:

• Chlorophyll Concentration
• Phytoplankton Carbon
• Particulate Organic Carbon

Transform the xarray dataset into gridded data.

Plot the Chlorophyll Concentration.

In [7]:

chlor_a = hypercoast.grid_pace_bgc(dataset, variable="chlor_a", method="linear")
chlor_a.plot(vmin=0, vmax=20, cmap="jet", size=6)

chlor_a = hypercoast.grid_pace_bgc(dataset, variable="chlor_a", method="linear") chlor_a.plot(vmin=0, vmax=20, cmap="jet", size=6)

Out[7]:

<matplotlib.collections.QuadMesh at 0x7f00de7e0950>

Plot the Phytoplankton Carbon.

In [8]:

carbon_phyto = hypercoast.grid_pace_bgc(
    dataset, variable="carbon_phyto", method="linear"
)
carbon_phyto.plot(vmin=0, vmax=120, cmap="jet", size=6)

carbon_phyto = hypercoast.grid_pace_bgc( dataset, variable="carbon_phyto", method="linear" ) carbon_phyto.plot(vmin=0, vmax=120, cmap="jet", size=6)

Out[8]:

<matplotlib.collections.QuadMesh at 0x7f00dd9a2750>

Particulate Organic Carbon.

In [9]:

poc = hypercoast.grid_pace_bgc(dataset, variable="poc", method="linear")
poc.plot(vmin=0, vmax=1000, cmap="jet")

poc = hypercoast.grid_pace_bgc(dataset, variable="poc", method="linear") poc.plot(vmin=0, vmax=1000, cmap="jet")

Out[9]:

<matplotlib.collections.QuadMesh at 0x7f00dd970d10>

Plot the data on an interactive map.

In [10]:

m = hypercoast.Map()
m.add_basemap("Hybrid")
m.add_raster(chlor_a, layer_name="Chlorophyll-a", colormap="jet", vmin=0, vmax=20)
m.add_raster(
    carbon_phyto, layer_name="Phytoplankton Carbon", colormap="plasma", vmin=0, vmax=120
)
m.add_raster(
    poc, layer_name="Particulate Organic Carbon", colormap="coolwarm", vmin=0, vmax=1000
)
m.add_layer_manager()

m.add_colormap(cmap="jet", vmin=0, vmax=20, label="Chlorophyll-a (mg/m3)")
m.add_colormap(cmap="plasma", vmin=0, vmax=120, label="Phytoplankton Carbon (mg/m3)")
m.add_colormap(
    cmap="coolwarm", vmin=0, vmax=1000, label="Particulate Organic Carbon (mg/m3)"
)
m

m = hypercoast.Map() m.add_basemap("Hybrid") m.add_raster(chlor_a, layer_name="Chlorophyll-a", colormap="jet", vmin=0, vmax=20) m.add_raster( carbon_phyto, layer_name="Phytoplankton Carbon", colormap="plasma", vmin=0, vmax=120 ) m.add_raster( poc, layer_name="Particulate Organic Carbon", colormap="coolwarm", vmin=0, vmax=1000 ) m.add_layer_manager() m.add_colormap(cmap="jet", vmin=0, vmax=20, label="Chlorophyll-a (mg/m3)") m.add_colormap(cmap="plasma", vmin=0, vmax=120, label="Phytoplankton Carbon (mg/m3)") m.add_colormap( cmap="coolwarm", vmin=0, vmax=1000, label="Particulate Organic Carbon (mg/m3)" ) m

Out[10]: