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Deep generative model super-resolves spatially correlated multiregional climate data.

Norihiro Oyama1, Noriko N Ishizaki2, Satoshi Koide3

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This study introduces a new machine learning method for climate model downscaling, accurately preserving spatial correlations crucial for infrastructure planning. The Physics Informed Super-Resolution Generative Adversarial Network (PI-SRGAN) improves climate change impact assessments.

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Area of Science:

  • Climate Science
  • Machine Learning
  • Geophysics

Background:

  • Climate model downscaling is essential for long-term climate change projections impacting societal decisions.
  • Current super-resolution methods struggle to maintain spatial correlations in climatological data, vital for large-scale systems like transportation infrastructure.

Purpose of the Study:

  • To develop a machine learning approach for climate model downscaling that preserves inter-regional spatial correlations.
  • To enhance the accuracy of climate change impact assessments for spatially extensive systems.

Main Methods:

  • An adversarial network-based machine learning approach was employed for super-resolution (downscaling) of climate simulation outputs.
  • A novel Physics Informed Super-Resolution Generative Adversarial Network (PI-SRGAN) was developed, integrating physical information.
  • A variant, Precipitation Source Inaccessible SRGAN (PS-SRGAN), was explored using pressure fields for precipitation downscaling.

Main Results:

  • The PI-SRGAN successfully reconstructed inter-regional spatial correlations with high magnification (up to 50x) while maintaining pixel-wise statistical consistency.
  • Integration of climatologically relevant physical information significantly improved downscaling performance compared to standard methods.
  • The PS-SRGAN demonstrated unexpectedly effective downscaling performance for precipitation fields, even when the direct precipitation field was unavailable.

Conclusions:

  • Machine learning, specifically adversarial networks, can effectively perform climate model downscaling while preserving critical spatial correlations.
  • The PI-SRGAN offers a promising tool for inter-regionally consistent climate change impact assessments.
  • The PS-SRGAN highlights the potential of using related meteorological fields for downscaling precipitation, opening new avenues for climate data reconstruction.