Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach

Abstract

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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.

Keywords

• radiative transfer
• machine learning
• optimal estimation (OE)
• speed-up
• OCO-2 retrievals