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<p>Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables derived from low-resolution simulations. Despite notable advancements, the contemporary cutting-edge downscaling algorithms are tailored to specific variables. Handling meteorological variables in isolation involves overlooking their interconnectedness, leading to an incomplete understanding of atmospheric dynamics. Additionally, the laborious processes of data collection and processing and the computational resources required to downscale individual variables are significant hurdles. Given the limited versatility of the existing models across different meteorological variables and their failure to account for intervariable relationships, this paper proposes a unified downscaling approach leveraging meta-learning, with its architecture based on the Enhanced Deep Super-Resolution (EDSR) model. The proposed framework supports downscaling across diverse variables and xsclimate datasets. It exhibits strong extensibility by generalizing to 18 unseen during training variables from CFS, S2S (CMA), and CMIP6 (CMCC-ESM2)—such as mean sea-level pressure, temperature at 850&#xa0;hPa, and the 500&#xa0;hPa U- and V-wind components—marking a step toward a universal downscaling solution. Experimental evidence demonstrates that the proposed model outperforms the best existing methods, such as EDSR and ClimateSD, in both quantitative and qualitative assessments.</p>

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MetaSD: a unified framework for scalable downscaling of meteorological variables in diverse situations

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Jing Hu,
  • Honghu Zhang,
  • Peng Zheng,
  • Jialing Mu,
  • Xi Wu,
  • Xiaomeng Huang

摘要

Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables derived from low-resolution simulations. Despite notable advancements, the contemporary cutting-edge downscaling algorithms are tailored to specific variables. Handling meteorological variables in isolation involves overlooking their interconnectedness, leading to an incomplete understanding of atmospheric dynamics. Additionally, the laborious processes of data collection and processing and the computational resources required to downscale individual variables are significant hurdles. Given the limited versatility of the existing models across different meteorological variables and their failure to account for intervariable relationships, this paper proposes a unified downscaling approach leveraging meta-learning, with its architecture based on the Enhanced Deep Super-Resolution (EDSR) model. The proposed framework supports downscaling across diverse variables and xsclimate datasets. It exhibits strong extensibility by generalizing to 18 unseen during training variables from CFS, S2S (CMA), and CMIP6 (CMCC-ESM2)—such as mean sea-level pressure, temperature at 850 hPa, and the 500 hPa U- and V-wind components—marking a step toward a universal downscaling solution. Experimental evidence demonstrates that the proposed model outperforms the best existing methods, such as EDSR and ClimateSD, in both quantitative and qualitative assessments.