Home >

news Help

Publication Information


Title
Japanese: 
English:Four-Dimensional Super-Resolution Data Assimilation (4D-SRDA) for Prediction of Three-Dimensional Quasi-Geostrophic Flows 
Author
Japanese: Notsu Arinori, Yasuda Yuki, 大西領.  
English: Notsu Arinori, Yasuda Yuki, Onishi Ryo.  
Language English 
Journal/Book name
Japanese: 
English:SOLA 
Volume, Number, Page Vol. 21B    No. Special_Edition    pp. 1-7
Published date Feb. 2025 
Publisher
Japanese: 
English:公益社団法人 日本気象学会 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Abstract <p>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.</p>

©2007 Institute of Science Tokyo All rights reserved.