Abstract

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 complex forward model is 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. Within each distinct spectral band, the emulator represents radiance output at irregular wavelengths as 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.

摘要

观测系统不确定性实验 (OSUE) 最近被提议作为一种经济有效的方式来对 NASA 的轨道碳观测站 2 (OCO-2) 任务的检索进行概率评估。OCO-2 反演算法中的一个重要组成部分是一个全物理正演模型，该模型描述了大气变量（如二氧化碳）与遥感仪器测量的辐射之间的数学关系。这种复杂的前向模型计算量大，但大规模 OSUE 需要多次评估该模型，这使得它无法进行综合实验。为了解决这个问题，我们开发了一个统计模拟器来促进 OCO-2 任务中的大规模 OSUE。在每个不同的光谱带内，仿真器将不规则波长的辐射输出表示为基函数和随机系数的线性组合。然后，这些随机系数使用最近邻高斯过程建模，并通过活动子空间内置输入降维。所提出的仿真器降低了输入空间和输出空间的维度，从而在完全贝叶斯推理框架内实现了快速计算。验证实验表明，该仿真器优于其他竞争统计方法和近似全物理正向模型的降阶模型。所提出的仿真器降低了输入空间和输出空间的维度，从而在完全贝叶斯推理框架内实现了快速计算。验证实验表明，该仿真器优于其他竞争统计方法和近似全物理正向模型的降阶模型。所提出的仿真器降低了输入空间和输出空间的维度，从而在完全贝叶斯推理框架内实现了快速计算。验证实验表明，该仿真器优于其他竞争统计方法和近似全物理正向模型的降阶模型。