[A-CG34] 地球規模環境変化の予測と検出

[ACG34-P05] Application of Super-resolution convolutional neural network to estimate fine-resolution regional sea level variations

*鈴木 立郎¹、大越智 幸司¹、山上 遥航¹、建部 洋晶¹、松岡 大祐¹、河宮 未知生¹ (1.国立研究開発法人海洋研究開発機構)

キーワード：海面水位変動、深層学習、ダウンスケーリング

Sea level rise is one of the most apparent manifestations of global warming, and it directly threatens coastal areas due to increased erosion and more frequent storm-surge flooding. Detailed information on future sea level fluctuations will be required to address these risks. Nishikawa et al. (2021) have driven a 0.1-degree horizontal North Pacific model incorporating a high-resolution Japanese domain model to produce a 2-km-resolution dataset of sea-level changes. This approach is physically consistent but requires a massive amount of computational resources. To conserve computational resources, we would like to propose a downscaling method from a 10 km scale to a 2 km scale using machine learning. In this study, we applied a Super-resolution (SR) simulation based on a deep convolutional neural network (Dong et al., 2014) to estimate future regional sea level variations in fine resolution. The 2-km resolution FORP-JPN02 data by Nishikawa et al. was used as training data. Machine learning was performed by comparing the original 2 km resolution data with horizontally filtered data equivalent to 10 km resolution. The SR simulation reproduced, for example, higher-resolution sea level changes along the coast. Further, we plan to apply this machine learning method to the results of a global eddy-resolving ocean model to evaluate the occurrence risk of sea level rise and heat waves on a 2km scale.
The "PyTorch" code and the Earth Simulator were used for machine learning.