Algorithmic Hallucinations of Near-Surface Winds: Statistical
Downscaling with Generative Adversarial Networks to Convection-Permitting
Scales
- URL: http://arxiv.org/abs/2302.08720v3
- Date: Mon, 18 Sep 2023 18:32:22 GMT
- Title: Algorithmic Hallucinations of Near-Surface Winds: Statistical
Downscaling with Generative Adversarial Networks to Convection-Permitting
Scales
- Authors: Nicolaas J. Annau, Alex J. Cannon, Adam H. Monahan
- Abstract summary: We focus on convolutional neural network-based Generative Adversarial Networks (GANs)
Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting model simulations over North America.
Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper explores the application of emerging machine learning methods from
image super-resolution (SR) to the task of statistical downscaling. We
specifically focus on convolutional neural network-based Generative Adversarial
Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to
generate high-resolution (HR) surface winds emulating Weather Research and
Forecasting (WRF) model simulations over North America. Unlike traditional SR
models, where LR inputs are idealized coarsened versions of the HR images, WRF
emulation involves using non-idealized LR and HR pairs resulting in
shared-scale mismatches due to internal variability. Our study builds upon
current SR-based statistical downscaling by experimenting with a novel
frequency-separation (FS) approach from the computer vision field. To assess
the skill of SR models, we carefully select evaluation metrics, and focus on
performance measures based on spatial power spectra. Our analyses reveal how
GAN configurations influence spatial structures in the generated fields,
particularly biases in spatial variability spectra. Using power spectra to
evaluate the FS experiments reveals that successful applications of FS in
computer vision do not translate to climate fields. However, the FS experiments
demonstrate the sensitivity of power spectra to a commonly used GAN-based SR
objective function, which helps interpret and understand its role in
determining spatial structures. This result motivates the development of a
novel partial frequency-separation scheme as a promising configuration option.
We also quantify the influence on GAN performance of non-idealized LR fields
resulting from internal variability. Furthermore, we conduct a spectra-based
feature-importance experiment allowing us to explore the dependence of the
spatial structure of generated fields on different physically relevant LR
covariates.
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