Addressing the challenges of climate change requires accurate and
high-resolution mapping of climate and weather variables. However, many
existing climate datasets, such as the gridded outputs of the state-of-the-art
numerical climate models (e.g., general circulation models), are only available
at very coarse spatial resolutions due to the model complexity and extremely
high computational demand. Deep-learning-based methods, particularly generative
adversarial networks (GANs) and their variants, have proved effective for
refining natural images, and have shown great promise in improving scientific
datasets. In this paper, we describe a conditional GAN-based geospatial
downscaling method for extreme downscaling of gridded climate datasets.
Compared to most existing methods, the method can generate high-resolution
accurate climate datasets from very low-resolution inputs. More importantly,
the method explicitly considers the uncertainty inherent to the downscaling
process that tends to be ignored in existing methods. Given an input, the
method can produce a multitude of plausible high-resolution samples instead of
one single deterministic result. These samples allow for an empirical
exploration and inferences of model uncertainty and robustness. With a case
study of gridded climate datasets (wind velocity and solar irradiance), we
demonstrate the performances of the framework in downscaling tasks with very
high scaling factors (up to $64\times$) and highlight the advantages of the
framework with a comprehensive comparison with commonly used downscaling
methods, including area-to-point (ATP) kriging, deep image prior (DIP),
enhanced deep super-resolution network (EDSR), enhanced super-resolution
generative adversarial networks (ESRGAN), and physics-informed
resolution-enhancing GAN (PhIRE GAN).

使用基于生成对抗网络 (GANs) 的有条件 GAN 的 geospatial downscaling 方法，从低分辨率输入中生成高分辨率准确的气候数据集，并明确考虑了下尺度过程中的不确定性。