3 research outputs found
Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach
The Orbiting Carbon Observatories-2 and -3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands using the Atmospheric Carbon Observations from Space (ACOS) retrieval. Within ACOS, a Bayesian optimal estimation approach is employed to retrieve the column-averaged CO2 dry air mole fraction from these measurements. This retrieval requires a large number of polarized, multiple-scattering radiative transfer calculations for each iteration. These calculations take up the majority of the processing time for each retrieval and slow down the algorithm to the point that reprocessing data from the mission over multiple years becomes especially time consuming. To accelerate the radiative transfer model and, thereby, ease this bottleneck, we have developed a novel approach that enables modeling of the full spectra for the three OCO-2/3 instrument bands from radiances calculated at a small subset of monochromatic wavelengths. This allows for a reduction of the number of monochromatic calculations by a factor of 10, which can be achieved with radiance errors of less than 0.01% with respect to the existing algorithm and is easily tunable to a desired accuracy-speed trade-off. For the ACOS retrieval, this speeds up the over-retrievals by about a factor of two. The technique may be applicable to similar retrieval algorithms for other greenhouse gas sensors with large data volumes, such as GeoCarb, GOSAT-3, and CO2M
Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
The quest to find extraterrestrial life is a critical scientific endeavor
with civilization-level implications. Icy moons in our solar system are
promising targets for exploration because their liquid oceans make them
potential habitats for microscopic life. However, the lack of a precise
definition of life poses a fundamental challenge to formulating detection
strategies. To increase the chances of unambiguous detection, a suite of
complementary instruments must sample multiple independent biosignatures (e.g.,
composition, motility/behavior, and visible structure). Such an instrument
suite could generate 10,000x more raw data than is possible to transmit from
distant ocean worlds like Enceladus or Europa. To address this bandwidth
limitation, Onboard Science Instrument Autonomy (OSIA) is an emerging
discipline of flight systems capable of evaluating, summarizing, and
prioritizing observational instrument data to maximize science return. We
describe two OSIA implementations developed as part of the Ocean Worlds Life
Surveyor (OWLS) prototype instrument suite at the Jet Propulsion Laboratory.
The first identifies life-like motion in digital holographic microscopy videos,
and the second identifies cellular structure and composition via innate and
dye-induced fluorescence. Flight-like requirements and computational
constraints were used to lower barriers to infusion, similar to those available
on the Mars helicopter, "Ingenuity." We evaluated the OSIA's performance using
simulated and laboratory data and conducted a live field test at the
hypersaline Mono Lake planetary analog site. Our study demonstrates the
potential of OSIA for enabling biosignature detection and provides insights and
lessons learned for future mission concepts aimed at exploring the outer solar
system.Comment: 49 pages, 18 figures, submitted to The Planetary Science Journal on
2023-04-2