Dispersion Simulation Using the 1-km Gridded Wind Fields Constructed by Super-Resolution Surrogate Downscaling-Reference-Cited by-同舟云学术

Dispersion Simulation Using the 1-km Gridded Wind Fields Constructed by Super-Resolution Surrogate Downscaling

Published:2025 Issue:3 Volume:103 Page:321-333
ISSN:0026-1165
Container-title:Journal of the Meteorological Society of Japan. Ser. II
language:en
Short-container-title:Journal of the Meteorological Society of Japan

Author:

SEKIYAMA Tsuyoshi Thomas¹,KAJINO Mizuo¹

Affiliation:

1. Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

Publisher

Meteorological Society of Japan

Link

https://www.jstage.jst.go.jp/article/jmsj/103/3/103_2025-016/_pdf

Reference31 articles.

1. Baño-Medina, J., R. Manzanas, and J. M. Gutiérrez, 2020: Configuration and intercomparison of deep learning neural models for statistical downscaling. Geosci. Model Dev., 13, 2109–2124.

2. Bi, K., L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, 2023: Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619, 533–538.

3. Bodnar, C., W. P. Bruinsma, A. Lucic, M. Stanley, A. Vaughan, J. Brandstetter, P. Garvan, M. Riechert, J. A. Weyn, H. Dong, J. K. Gupta, K. Thambiratnam, A. T. Archibald, C.-C. Wu, E. Heider, M. Welling, R. E. Turner, and P. Perdikaris, 2024: Aurora: A foundation model of the atmos582222phere. arXiv: physics, 2405.13063v2 [physics.ao-ph], doi: 10.48550/arXiv. 2405.13063.

4. Doan, Q.-V., H. Kusaka, T. Sato, and F. Chen, 2021: S-SOM v1.0: A structural self-organizing map algorithm for weather typing. Geosci. Model Dev., 14, 2097–2111.

5. He, K., X. Zhang, S. Ren, and J. Sun, 2016: Deep residual learning for image recognition. Proceeding of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vegas, NV, USA, 770–778.