Emulation of greenhouse-gas sensitivities using variational autoencoders

TL;DR

This paper introduces a novel variational autoencoder-based emulator for gas flux sensitivities that significantly reduces computational costs in flux inversion processes by accurately mimicking traditional LPDM outputs.

Contribution

The paper develops a convolutional variational autoencoder emulator for LPDM sensitivities, outperforming traditional methods and applicable across different LPDM models.

Findings

CVAE emulator outperforms empirical orthogonal functions-based emulator

Emulator reliably reduces computational time for flux inversions

Applicable to various LPDMs for flexible use

Abstract

Flux inversion is the process by which sources and sinks of a gas are identified from observations of gas mole fraction. The inversion often involves running a Lagrangian particle dispersion model (LPDM) to generate sensitivities between observations and fluxes over a spatial domain of interest. The LPDM must be run backward in time for every gas measurement, and this can be computationally prohibitive. To address this problem, here we develop a novel spatio-temporal emulator for LPDM sensitivities that is built using a convolutional variational autoencoder (CVAE). With the encoder segment of the CVAE, we obtain approximate (variational) posterior distributions over latent variables in a low-dimensional space. We then use a spatio-temporal Gaussian process emulator on the low-dimensional space to emulate new variables at prediction locations and time points. Emulated variables are then passed through the decoder segment of the CVAE to yield emulated sensitivities. We show that our CVAE-based emulator outperforms the more traditional emulator built using empirical orthogonal functions and that it can be used with different LPDMs. We conclude that our emulation-based approach can be used to reliably reduce the computing time needed to generate LPDM outputs for use in high-resolution flux inversions.