Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales
Date
2022-12-21
Authors
Annau, Nicolaas J.
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Abstract
Providing timely and accurate small-scale information about weather and climate
is challenging – especially for variables strongly controlled by processes that
are unresolved by low-resolution (LR) models. Motivated by this challenge, this
thesis employed emerging artificial intelligence methods from the fields of computer
vision and deep learning for the statistical downscaling of surface wind variables to
convection-permitting scales. Specifically, Generative Adversarial Networks (GANs)
were conditioned on LR inputs to deterministically generate possible arrangements of
high-resolution (HR) surface wind patterns using super-resolution (SR). SR models
were trained over several subregions in the contiguous United States and southern
Canada, allowing for a systematic analysis of the methods. In addition to matching
the statistical properties of the target dataset, GANs generate fields with impressive
realism and computational efficiency, making them attractive for operational statistical
downscaling. However, objectively assessing the realism of the SR models required
a careful selection of evaluation metrics. Using power spectra successfully revealed
how altered GAN configurations changed spatial structures in the generated fields,
where biases in variability originate, and the role of including additional LR covariates
in the SR methods. Inspired by recent work in the computer vision field, a novel
methodology that separates spatial frequencies in HR fields was used in an attempt to
optimize the SR GANs further. This method, called frequency separation, ultimately
deteriorated the realism of the generated HR fields. However, frequency separation
revealed how spatial structures are influenced by the metrics used to optimize the
SR methods. A covariate-sensitivity analysis was also performed, illustrating how
physical relationships between the large and small scales are mirrored in the GANs.
Furthermore, a methodology was used to evaluate serial dependence in the SR methods.
Even without explicitly including temporal information while training, serial
dependence in the fine-scale structures of the generated fields was present. Although
this thesis did not use fully-stochastic SR models (as has been done in existing work),
the results provide valuable insights into variability, biases, covariates, and the optimization problem of SR.
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Keywords
climate science, artificial intelligence, deep learning, downscaling, super-resolution, computer vision, generative modelling