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Emulation of greenhouse-gas sensitivities using variational autoencoders
Environmetrics ( IF 1.5 ) Pub Date : 2022-09-11 , DOI: 10.1002/env.2754
Laura Cartwright 1 , Andrew Zammit‐Mangion 1 , Nicholas M. Deutscher 2
Affiliation  

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 simulations of the gas movement over a 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, a two-piece neural network capable of condensing and reconstructing images). 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.

中文翻译:

使用变分自动编码器模拟温室气体敏感性

通量反演是通过观察气体摩尔分数来识别气体源和汇的过程。反演通常涉及运行拉格朗日粒子分散模型 (LPDM) 以生成感兴趣区域上气体运动的模拟。对于每次气体测量,LPDM 都必须及时倒退运行,这在计算上可能会令人望而却步。为了解决这个问题,我们在这里开发了一种用于 LPDM 灵敏度的新型时空模拟器,它是使用卷积变分自动编码器(CVAE,一种能够压缩和重建图像的两件式神经网络)构建的。通过 CVAE 的编码器部分,我们获得了低维空间中潜在变量的近似(变分)后验分布。然后,我们在低维空间上使用时空高斯过程模拟器来模拟预测位置和时间点的新变量。仿真变量然后通过 CVAE 的解码器部分传递以产生仿真灵敏度。我们展示了我们基于 CVAE 的仿真器优于使用经验正交函数构建的更传统的仿真器,并且它可以与不同的 LPDM 一起使用。我们得出结论,我们基于仿真的方法可用于可靠地减少生成用于高分辨率通量反演的 LPDM 输出所需的计算时间。我们展示了我们基于 CVAE 的仿真器优于使用经验正交函数构建的更传统的仿真器,并且它可以与不同的 LPDM 一起使用。我们得出结论,我们基于仿真的方法可用于可靠地减少生成用于高分辨率通量反演的 LPDM 输出所需的计算时间。我们展示了我们基于 CVAE 的仿真器优于使用经验正交函数构建的更传统的仿真器,并且它可以与不同的 LPDM 一起使用。我们得出结论,我们基于仿真的方法可用于可靠地减少生成用于高分辨率通量反演的 LPDM 输出所需的计算时间。
更新日期:2022-09-11
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