Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation
Creators
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1.
Jet Propulsion Lab
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2.
California Institute of Technology
Abstract
Remote sensing of atmospheric carbon dioxide (CO2) carried out by NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite mission and the related uncertainty quantification effort involve repeated evaluations of a state-of-the-art atmospheric physics model. The retrieval, or solving an inverse problem, requires substantial computational resources. In this work, we propose and implement a statistical emulator to speed up the computations in the OCO-2 physics model. Our approach is based on Gaussian process (GP) regression, leveraging recent research on kernel flows and cross validation to efficiently learn the kernel function in the GP. We demonstrate our method by replicating the behavior of OCO-2 forward model within measurement error precision and further show that in simulated cases, our method reproduces the CO2 retrieval performance of OCO-2 setup with computational time that is orders of magnitude faster. The underlying emulation problem is challenging because it is high-dimensional. It is related to operator learning in the sense that the function to be approximated maps high-dimensional vectors to high-dimensional vectors. Our proposed approach is not only fast but also highly accurate (its relative error is less than 1 %). In contrast with artificial neural network (ANN)-based methods, it is interpretable, and its efficiency is based on learning a kernel in an engineered and expressive family of kernels.
Copyright and License
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. © 2025 California Institute of Technology. Government sponsorship acknowledged.
Published by Copernicus Publications on behalf of the European Geosciences Union.
Acknowledgement
The research described in this paper was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. The authors thank Pulong Ma and Chris O'Dell for helpful guidance.
Contributions
OL: project administration, conceptualization, methodology, software, formal analysis, data curation, writing (original draft and review and editing), visualization. JS: conceptualization, methodology, software, writing (original draft and review and editing). JH: project administration, conceptualization, methodology, supervision, software, data curation, writing (review and editing). JM: methodology, software, data curation, writing (original draft). AB: conceptualization, methodology, writing (review and editing). HO: conceptualization, methodology, writing (review and editing).
Data Availability
Code and data are available on an OSF repository at https://doi.org/10.17605/OSF.IO/U2T8A (Lamminpää, 2024). The software requires ReFRACtor and ReFRACtorUQ GitHub repositories, which are freely available as well.
Additional Information
This paper was edited by Peer Nowack and reviewed by two anonymous referees.
Files
amt-18-673-2025.pdf
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Additional details
Related works
- Is supplemented by
- Dataset: 10.17605/OSF.IO/U2T8A (DOI)
Funding
- Jet Propulsion Laboratory
- National Aeronautics and Space Administration
Dates
- Accepted
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2024-11-28Accepted