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Simulating PACE Global Ocean Radiances
Watson W Gregg1, Cécile S Rousseaux1
1NASA Global Modeling and Assimilation Office, Greenbelt, MD, USA.
Frontiers in Marine Science
|January 3, 2018
Summary
NASA
Area of Science:
- Oceanography and Remote Sensing
- Biogeochemical ocean modeling
- Radiative transfer modeling
Background:
- NASA's PACE mission will utilize a hyper-spectral radiometer for global ocean biogeochemical analysis.
- Accurate global ocean radiance simulations are crucial for PACE mission design and capability assessment.
- Existing models require validation against satellite data for ocean constituent retrieval.
Purpose of the Study:
- To assess the skill of a dynamic global ocean radiance simulation model.
- To validate the model's ability to represent ocean biogeochemical constituents and water-leaving radiances.
- To support pre-launch mission development and evaluate on-orbit capabilities for the PACE mission.
Main Methods:
- Developed a dynamic global ocean model simulating phytoplankton groups, detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC).
- Incorporated 1 nm spectral resolution for optical absorption and scattering processes.
- Assimilated total chlorophyll, PIC, and aCDOC using satellite data and coupled the model with the Ocean-Atmosphere Spectral Irradiance Model (OASIM) for radiance estimation.
Main Results:
- The 5-year simulation demonstrated statistically significant correlations for chlorophyll (r=0.869), PIC (r=0.868), and aCDOC (r=0.890) against satellite data.
- Phytoplankton groups (diatoms, cyanobacteria, coccolithophores) showed significant correlations with in situ data.
- Estimated normalized water-leaving radiances were within -10.4% bias of MODIS-Aqua data, with a mean correlation of 0.706.
Conclusions:
- The dynamic global assimilated model shows significant skill in representing ocean optical constituents and water-leaving radiances.
- The simulation's high spectral resolution and global coverage support PACE mission design, band selection, and observing scenario investigations.
- The model provides a valuable tool for error characterization and assessing future satellite mission capabilities.
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