Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 28 - Computation, Design, and Quality Assurance of Physical Science and Engineering Applications
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #319008
Title: Statistical Emulation with Dimension Reduction for Complex Forward Models in Remote Sensing
Author(s): Gang Yang* and Emily Kang and Bledar Alex Konomi and Jonathan Hobbs
Companies: University of Cincinnati and University of Cincinnati and University of Cincinnati and Jet Propulsion Laboratory, California Institute of Technology
Keywords: Remote sensing; Forward model; Statistical emulation; Gaussian process; Functional principal component analysis; Gradient-based kernel dimension reduction

The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to quantify uncertainty, calibrate model parameters and improve computational efficiency of the retrieval algorithms. Motivated by this, we introduce a framework of building statistical emulators by combining dimension reduction of input and output spaces and Gaussian process modeling. The functional principal component analysis (FPCA) via a conditional expectation method is chosen to reduce the dimension of the output space of the forward model. In addition, the gradient-based kernel dimension reduction method is applied to reduce the dimension of input space when the gradients of the complex forward model are unavailable or computationally prohibitive. A Gaussian process with feasible computation is then constructed at the low-dimensional input and output spaces. Theoretical properties of the resulting statistical emulator are explored, and the proposed method is illustrated by application to NASA’s Orbiting Carbon Observatory-2 (OCO2) data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program