  Observing system uncertainty experiments (OSUEs) have been recently proposed
as a cost-effective way to perform probabilistic assessment of retrievals for
NASA's Orbiting Carbon Observatory-2 (OCO-2) mission. One important component
in the OCO-2 retrieval algorithm is a full-physics forward model that describes
the mathematical relationship between atmospheric variables such as carbon
dioxide and radiances measured by the remote sensing instrument. This forward
model is complicated and computationally expensive but large-scale OSUEs
require evaluation of this model numerous times, which makes it infeasible for
comprehensive experiments. To tackle this issue, we develop a statistical
emulator to facilitate large-scale OSUEs in the OCO-2 mission with independent
emulation. Within each distinct spectral band, the emulator represents
radiances output at irregular wavelengths via a linear combination of basis
functions and random coefficients. These random coefficients are then modeled
with nearest-neighbor Gaussian processes with built-in input dimension
reduction via active subspace. The proposed emulator reduces dimensionality in
both input space and output space, so that fast computation is achieved within
a fully Bayesian inference framework. Validation experiments demonstrate that
this emulator outperforms other competing statistical methods and a reduced
order model that approximates the full-physics forward model.
