Accelerating 3D radiative transfer for realistic OCO-2 cloud-aerosol scenes

The recently launched NASA OCO-2 satellite is expected to provide important information about the carbon dioxide distribution in the troposphere down to Earth's surface. Among the challenges in accurately retrieving CO2 concentration from the hyperspectral observations in each of the three OCO-2 bands are cloud and aerosol impacts on the observed radiances. Preliminary studies based on idealized cloud fields have shown that they can lead to spectrally dependent radiance perturbations which differ from band to band and may lead to biases in the derived products. Since OCO-2 was inserted into the A-Train, it is only natural to capitalize on sensor synergies with other instruments, in this case on the cloud and aerosol scene context that is provided by MODIS and CALIOP. Our approach is to use cloud imagery (especially for inhomogeneous scenes) for predicting the hyperspectral observations within a collocated OCO-2 footprint and comparing with the observations, which allows a systematic assessment of the causes for biases in the retrievals themselves, and their manifestation in spectral residuals for various different cloud types and distributions. Simulating a large number of cases with line-by-line calculations using a 3D code is computationally prohibitive even on large parallel computers. Therefore, we developed a number of acceleration approaches. In this contribution, we will analyze them in terms of their speed and accuracy, using cloud fields from airborne imagery collected during a recent NASA field experiment (SEAC4RS) as proxy for different types of inhomogeneous cloud fields. The broader goal of this effort is to improve OCO-2 retrievals in the vicinity of cloud fields, and to extend the range of conditions under which the instrument will provide useful results.