Deep generative model super-resolves spatially correlated multiregional
climate data
- URL: http://arxiv.org/abs/2209.12433v2
- Date: Sat, 15 Apr 2023 13:14:47 GMT
- Title: Deep generative model super-resolves spatially correlated multiregional
climate data
- Authors: Norihiro Oyama, Noriko N. Ishizaki, Satoshi Koide, and Hiroaki Yoshida
- Abstract summary: We show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling.
The proposed method has a potential application to the inter-regionally consistent assessment of the climate change impact.
We present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field.
- Score: 5.678539713361703
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Super-resolving the coarse outputs of global climate simulations, termed
downscaling, is crucial in making political and social decisions on systems
requiring long-term climate change projections. Existing fast super-resolution
techniques, however, have yet to preserve the spatially correlated nature of
climatological data, which is particularly important when we address systems
with spatial expanse, such as the development of transportation infrastructure.
Herein, we show an adversarial network-based machine learning enables us to
correctly reconstruct the inter-regional spatial correlations in downscaling
with high magnification of up to fifty while maintaining pixel-wise statistical
consistency. Direct comparison with the measured meteorological data of
temperature and precipitation distributions reveals that integrating
climatologically important physical information improves the downscaling
performance, which prompts us to call this approach $\pi$SRGAN (Physics
Informed Super-Resolution Generative Adversarial Network). The proposed method
has a potential application to the inter-regionally consistent assessment of
the climate change impact. Additionally, we present the outcomes of another
variant of the deep generative model-based downscaling approach in which the
low-resolution precipitation field is substituted with the pressure field,
referred to as $\psi$SRGAN (Precipitation Source Inaccessible SRGAN).
Remarkably, this method demonstrates unexpectedly good downscaling performance
for the precipitation field.
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