CALIOP LIDAR data

Introduction

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar dataset provides globally distributed measurements of ocean particulate backscattering coefficients at 532nm (bbp(532)), valuable for ocean biology and biogeochemistry research. These measurements originate from the CALIOP instrument aboard NASA’s Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. CALIOP's lidar technology uniquely penetrates optically thin clouds and collects data during both day and night, enabling studies of diurnal ocean processes and observations in high-latitude regions challenging for passive ocean color sensors due to extreme solar zenith angles and polar darkness.

Historically, CALIOP ocean optical data have significantly contributed to scientific discoveries, including detecting diel vertical migrations of zooplankton from space (Behrenfeld et al., 2019), documenting annual phytoplankton cycles in polar regions (Behrenfeld et al., 2017), and identifying biases in satellite-derived ocean color products (Bisson et al., 2021). The dataset has undergone rigorous validation, demonstrating strong performance against in situ measurements (Bisson et al., 2021; Lacour et al., 2020; Vadakke-Chanat & Jamet, 2023).

Previously hosted on a university server, these ocean lidar data are now archived via NASA's Ocean Biology Distributed Active Archive Center (OB.DAAC), providing secure and reliable access. Hosting CALIOP data at OB.DAAC supports cohesive scientific approaches for current and future ocean lidar missions, including upcoming missions like PACE and potential dedicated ocean lidar platforms.

Providing access to CALIOP lidar bbp data through NASA OB.DAAC aims to facilitate scientific collaboration and advance oceanographic research worldwide.

import earthaccess
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# Authenticate to NASA Earthdata
auth = earthaccess.login()

# Search for cloud-hosted CALIOP bbp granules
granules = earthaccess.search_data(
    short_name="CALIOP_BBP532_L3_OB",
    cloud_hosted=True,
    temporal=("2010-01-01", "2010-01-31")
)

# Get the first S3 data link
granule_url = granules[0].data_links(access="direct")[0]

# Open the datasets using earthaccess.open, which returns a list of file-like objects
file_like_objects = earthaccess.open(granules)

# Now, open each file-like object as an xarray Dataset
datasets = [xr.open_dataset(f) for f in file_like_objects]

# Print the first dataset to inspect its structure
print(datasets[0])

# Plot the bbp variable
datasets[0]['bbp'].plot()

# Plot the bbp map
# Select the bbp, lat, and lon variables from the first dataset
bbp_data = datasets[0]['bbp']
lat_data = datasets[0]['lat']
lon_data = datasets[0]['lon']

# Create a figure and a geographical axes
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())

# Add coastlines and gridlines
ax.coastlines()
ax.gridlines(draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--')

# Create a scatter plot of bbp data on the map
scatter = ax.scatter(lon_data, lat_data, c=bbp_data, s=1, cmap='viridis', transform=ccrs.Geodetic())

# Add a colorbar
plt.colorbar(scatter, label='bbp')

# Set the title
plt.title('CALIOP BBP on a Global Map')

# Show the plot
plt.show()
  

References

Behrenfeld, M. J., Hu, Y., O’Malley, R. T., Boss, E. S., Hostetler, C. A., Siegel, D. A., ... & Scarino, A. J. (2017). Annual boom–bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nature Geoscience, 10(2), 118-122. doi.org/10.1038/ngeo2861

Behrenfeld, M. J., Gaube, P., Della Penna, A., O’malley, R. T., Burt, W. J., Hu, Y., ... & Doney, S. C. (2019). Global satellite-observed daily vertical migrations of ocean animals. Nature, 576(7786), 257-261. doi.org/10.1038/s41586-019-1796-9

Behrenfeld, M. J., Hu, Y., Bisson, K. M., Lu, X., & Westberry, T. K. (2022). Retrieval of ocean optical and plankton properties with the satellite Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) sensor: Background, data processing, and validation status. Remote Sensing of Environment, 281, 113235. doi.org/10.1016/j.rse.2022.113235

Bisson, K. M., Boss, E., Werdell, P. J., Ibrahim, A., & Behrenfeld, M. J. (2021). Particulate backscattering in the global ocean: a comparison of independent assessments. Geophysical research letters, 48(2), e2020GL090909. doi.org/10.1029/2020GL090909

Lacour, L., Larouche, R., & Babin, M. (2020). In situ evaluation of spaceborne CALIOP lidar measurements of the upper-ocean particle backscattering coefficient. Optics Express, 28(18), 26989-26999. doi.org/10.1364/OE.397126

Lu, X., Hu, Y., Yang, Y., Neumann, T., Omar, A., Baize, R., ... & Winker, D. (2021). New Ocean Subsurface Optical Properties From Space Lidars: CALIOP/CALIPSO and ATLAS/ICESat‐2. Earth and Space Science, 8(10), e2021EA001839. doi.org/10.1029/2021EA001839

Vadakke-Chanat, S., & Jamet, C. (2023). Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp. Frontiers in Remote Sensing, 4, 1194580. doi.org/10.3389/frsen.2023.1194580