Accessing the CLOSE dataset

Accessing the CLOSE dataset#

Author: Anna Windle (NASA GSFC, SSAI)

Last updated: August 12, 2026

An Earthdata Login account is required to access data from the NASA Earthdata system, including NASA ocean color data.

Summary#

Accurate evaluation of satellite ocean color algorithms is fundamentally limited by the absence of comprehensive datasets in which the true ocean state is fully known. Here we present CLOSE: a Closed-Loop Ocean Simulation for Evaluation. The CLOSE dataset is provided in a realistic satellite instrument coordinate framework to support the development and assessment of retrieval algorithms for NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

The input data includes remote sensing reflectance (Rrs(λ)), inherent optical properties (IOPs), phytoplankton community composition (PCC), and associated environmental variables derived from the NASA Ocean Biogeochemical Model (NOBM) coupled to the Ocean-Atmosphere Spectral Irradiance Model (OASIM).

These data are used as input to the Python Top-Of-Atmosphere Simulation Tool (PyTOAST) to simulate Level-1B top-of-atmosphere radiances, which are subsequently processed to Level-2 ocean color products. This framework provides end-to-end traceability from modeled ocean variables to retrieved geophysical retrievals, providing a unique resource for robust algorithm development, validation, and uncertainty quantification.

This openly available dataset represents the first fully closed, spatially explicit simulation of PACE-like measurements, establishing a foundation for evaluation of atmospheric correction and bio-optical (empirical and semi-analytical) methods. It is designed to accelerate algorithm development, enhance confidence in satellite-derived products, and ultimately advance global ocean biogeochemical observing capabilities. More information on the CLOSE dataset can be found in this manuscript. (link to be added)

The entire CLOSE dataset is hosted and provided free of charge through NASA Earthdata. The dataset can be accessed via the DOI landing page which provides links to the corresponding NASA Earthdata dataset landing pages. This tutorial will demonstrate how to access the CLOSE dataset using earthaccess.

Learning Objectives#

At the end of this notebook, you will know how to:

Discover and access the CLOSE dataset using earthaccess
Open CLOSE data as an xarray dataset
Explore and visualize CLOSE dataset variables through plots

1. Setup#

Begin by importing all of the packages used in this notebook.

import earthaccess
import matplotlib.pyplot as plt
import xarray as xr

Set your Earthdata Login credentials. You can add persist=True to save your credentials in a .netrc file, allowing you to authenticate automatically in the future sessions without re-entering your Earthdata credentials.

auth = earthaccess.login()

2. Availability#

The CLOSE dataset is available for four months: Sep 2024, Jan 2025, Mar 2025, and Jun 2025. Let’s start by counting how many files exist for each month.

months = ("2024-09", "2025-01", "2025-03", "2025-06")
datasets = {
    "Model Inputs": "PACE_OCI_MI_CLOSE",
    "L1B": "PACE_OCI_L1B_CLOSE",
    "L2": "PACE_OCI_L2_CLOSE",
    "L3M": "PACE_OCI_L3M_CLOSE",
}

for m in months:
    print(m)
    for label, short_name in datasets.items():
        results = earthaccess.search_data(
            short_name=short_name,
            temporal=(m, m),
        )
        print(f"  {label}: {len(results)} files")
    print()

2024-09
  Model Inputs: 138 files
  L1B: 138 files
  L2: 138 files
  L3M: 1 files

2025-01
  Model Inputs: 117 files
  L1B: 117 files
  L2: 117 files
  L3M: 1 files

2025-03
  Model Inputs: 157 files
  L1B: 157 files
  L2: 157 files
  L3M: 1 files

2025-06
  Model Inputs: 134 files
  L1B: 134 files
  L2: 134 files
  L3M: 1 files

3. Search and access#

Let’s search for files corresponding to a date and granule discussed in the manuscript: Granule B in Figure 1.

target = "2025-03-15 15:26:16"
files = {}
for label, short_name in datasets.items():
    results = earthaccess.search_data(
        short_name=short_name,
        temporal=(target, target),
        count=1,
    )
    files[label] = results[0]
    print(f"{label}: {files[label].data_links()[0] if files[label] else 'Not found'}")

Model Inputs: https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI_SIM.20250315T152616.MODEL_INPUTS.CLOSE.v1.nc
L1B: https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI_SIM.20250315T152616.L1B.CLOSE.v1.nc
L2: https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI_SIM.20250315T152616.L2.CLOSE.v1.nc
L3M: https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI_SIM.20250315.L3m.DAY.CLOSE.v1.4km.nc

paths = earthaccess.open(list(files.values()))

4. Open and plot#

Let’s open the data using xarray and prepare it for subsequent analysis.

model_inputs = xr.open_dataset(paths[0])
model_inputs = model_inputs.set_coords(("longitude", "latitude"))

l1b = xr.open_datatree(paths[1])
l1b = xr.merge(l1b.to_dict().values())
l1b = l1b.set_coords(("longitude", "latitude"))

l2 = xr.open_datatree(paths[2])
l2 = xr.merge(l2.to_dict().values())
l2 = l2.set_coords(("longitude", "latitude"))

l3m = xr.open_dataset(paths[3])

We can plot either remote sensing reflectance (Rrs) or top-of-atmopshere reflectance (rhot) from each file:

fig = plt.figure(figsize=(10, 4), constrained_layout=True)
gs = fig.add_gridspec(2, 3)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[0, 2])
ax4 = fig.add_subplot(gs[1, 0:3])

m1 = (
    model_inputs["Rrs"]
    .sel({"wavelength": 442}, method="nearest")
    .plot(ax=ax1, x="longitude", y="latitude", add_colorbar=True)
)
ax1.set_title("MODEL_INPUTS")

m2 = (
    l1b["rhot_blue"]
    .sel({"blue_bands": 53})
    .plot(ax=ax2, x="longitude", y="latitude", add_colorbar=True)
)
ax2.set_title("L1B")

m3 = (
    l2["Rrs"]
    .sel({"wavelength_3d": 442}, method="nearest")
    .plot(ax=ax3, x="longitude", y="latitude", add_colorbar=True)
)
ax3.set_title("L2")

m4 = (
    l3m["Rrs"]
    .sel({"wavelength": 442}, method="nearest")
    .plot(ax=ax4, x="lon", y="lat", add_colorbar=True)
)
ax4.set_title("L3M")

m1.colorbar.set_label("Rrs(442) (sr⁻¹)")
m2.colorbar.set_label("Rhot(442)")
m3.colorbar.set_label("Rrs(442) (sr⁻¹)")
m4.colorbar.set_label("Rrs(442) (sr⁻¹)")

for ax in [ax1, ax2, ax3, ax4]:
    ax.set_xlabel("")
    ax.set_ylabel("")
    ax.tick_params(length=0)
    for spine in ax.spines.values():
        spine.set_visible(False)

pos = ax4.get_position()
ax4.set_position([pos.x0 + 0.05, pos.y0 - 0.07, pos.width, pos.height])

# shift colorbar
cbar = m4.colorbar
cbar.ax.set_position(
    [
        cbar.ax.get_position().x0 + 0.05,
        cbar.ax.get_position().y0 - 0.07,
        cbar.ax.get_position().width,
        cbar.ax.get_position().height,
    ]
)

plt.show()

../../_images/9be218723ac1412ea39d1e6bb797201f996fe3ab7c778ec0f57831d737ad8aa6.png

5. Closing the loop on chlorophyll a#

By providing known “truth” fields in native satellite geometry, CLOSE offers a powerful testbed for atmospheric correction, bio-optical inversion, and emerging machine learning approaches.

Here is an example of comparing input chlor_a (derived mechanistically from NOBM) to output chlor_a derived from NASA OB.DAAC (Chl a ATBD).

plot = model_inputs["chlor_a"].plot.hist(
    bins=50,
    range=[0.007, 0.4],
    alpha=0.4,
    label="MODEL_INPUTS",
    color="blue",
)

plot = l2["chlor_a"].plot.hist(
    bins=50,
    range=[0.007, 0.4],
    alpha=0.4,
    label="L2",
    color="red",
)

median_model_inputs_chl = model_inputs["chlor_a"].median()
median_l2_chl = l2["chlor_a"].median()

plt.axvline(
    median_model_inputs_chl,
    color="blue",
    linewidth=2,
    label=f"MODEL_INPUTS median = {median_model_inputs_chl:.2f}",
)
plt.axvline(
    median_l2_chl,
    color="red",
    linestyle="--",
    linewidth=2,
    label=f"L2 median = {median_l2_chl:.2f}",
)

plt.xlabel("chlorophyll a (mg m$^{-3}$)", fontsize=10)
plt.ylabel("Frequency", fontsize=10)

plt.legend(frameon=False)
plt.show()

../../_images/650d420e329355da4a6e760c65ba0bf47ee9fa0efee1b1b79ddb61cb7ed9791f.png

Overall, Chl a distributions are similar, with only slight differences in their medians. This example evaluates the standard Chl a algorithm, but it could be replaced with any alternative algorithm to assess how well its output compares to the input (“truth”) Chl a. The same approach can be applied to other data products as well.

You have completed the notebook on “Accessing the CLOSE Dataset”!