Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments
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The observing system uncertainty experiments (OSUEs) have been widely used as a cost-effective way to make retrieval quality assessment in 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 relationship between the atmospheric variables such as carbon dioxide and radiances measured by the remote sensing instrument. This forward model is complicated and computationally expensive but a large-scale OSUE requires evaluation of this model numerous times, which makes it infeasible for operational usage. To tackle this issue, we develop a statistical emulator to facilitate efficient large-scale OSUEs in remote sensing. This emulator represents radiances output at irregular wavelengths via a linear combination of basis functions and random coefficients. These random coefficients are then modeled with a nearest-neighbor Gaussian process 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 a reduced order model that approximates the full-physics forward model.
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