SOLA
Online ISSN : 1349-6476
ISSN-L : 1349-6476
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Special Edition on the Frontier of Atmospheric Science with High Performance Computing
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Article
  • Arinori Notsu, Yuki Yasuda, Ryo Onishi
    2025 Volume 21B Issue Special_Edition Pages 1-7
    Published: 2025
    Released on J-STAGE: March 29, 2025
    Advance online publication: February 14, 2025
    JOURNAL OPEN ACCESS
    Supplementary material

    Super-resolution (SR) in deep learning is a technique to generate high-resolution (HR) outputs from low-resolution (LR) inputs. Recently, combining SR with data assimilation (DA) has been proposed, leading to the development of super-resolution data assimilation (SRDA). The SRDA method simultaneously performs SR and DA by inputting LR simulation results and observations into a neural network. This study develops a four-dimensional SRDA (4D-SRDA) model to predict temporal evolutions of three-dimensional quasi-geostrophic flows in a baroclinic jet system. To evaluate the performance of 4D-SRDA, we compare it with a Local Ensemble Transform Kalman Filter (LETKF), which uses an HR model. 4D-SRDA successfully reproduces both small- and large-scale structures of potential vorticity, visually similar to those produced by the LETKF. We compare grid-wise and pattern-similarity errors to quantify the accuracy of the analysis and forecast states. Despite using an LR fluid model, 4D-SRDA achieves accuracy comparable to that of the LETKF. Comparing the computational time required for prediction reveals that 4D-SRDA is substantially more efficient than the LETKF. These results suggest that 4D-SRDA is a promising approach for predicting HR atmospheric flows.

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