[A-TT49] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

2026年5月27日(水) 15:30 〜 17:00 101 (幕張メッセ国際会議場)

座長:Martineau Patrick(Japan Agency for Marine-Earth Science and Technology)、Jayanthi Venkata Ratnam(Application Laboratory, JAMSTEC)

Recent advances in machine learning, particularly deep learning, have enabled transformative applications across diverse fields, including weather, climate, oceanography, hydrology, and disease prediction. Increasingly, these techniques are being employed to forecast high-impact extreme events such as malaria outbreaks, heatwaves, cold spells, floods, droughts, tropical cyclones, typhoons, and large-scale climate phenomena like El Nino and the Indian Ocean Dipole. Beyond prediction, machine learning is proving valuable in improving parameterization schemes within numerical models, reducing systematic biases, and enhancing horizontal resolution in forecasts. This session aims to bring together researchers advancing machine learning methodologies to improve prediction and understanding of weather, climate, oceans, hydrology, and tropical diseases. Discussions will emphasize both scientific progress and practical applications that support societal resilience, well-being, and informed decision-making.

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15:30 〜 15:45

[ATT49-07] Emulating atmospheric transport and plume evolution with deep learning

★Invited Papers

*Donald D Lucas^1,2 (1. Lawrence Livermore National Laboratory, 2. Editor of JGR Machine Learning and Computation)

15:45 〜 16:00

[ATT49-08] Super-Resolution Surrogate Downscaling of MRI-ESM2(CMIP6) Black Carbon Surface Concentration Using Attention Based Convolutional Neural Networks

*Kunal Mishra ^1,2, Mizuo Kajino^2,3, Thomas Sekiyama⁴, Naga Oshima² (1. Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan, 2. Department of Atmosphere, Ocean, and Earth System Modeling Research, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba Ibaraki 305-0052, Japan, 3. Institute of Life and Environmental Sciences University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan, 4. Department of Physical Meteorology Research, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki 305-0052, Japan)

16:00 〜 16:15

[ATT49-09] Information Quantity, Quality, and Machine Learning Accuracy in Hydrologic Prediction

*Young gu Her¹, Minhyuk Jeung¹ (1. University of Florida)

16:15 〜 16:30

[ATT49-10] Multivariate recursive short-term forecasting of salinity and water level using deep learning in the Sai Gon–Dong Nai river system, Vietnam

*Nguyen Thi Diem Thuy^1,2, Hiroshi Ishidaira¹ (1. University of Yamanashi, Japan, 2. University of Science, Vietnam National University Ho Chi Minh City, Vietnam)

16:30 〜 16:45

[ATT49-11] Vision Transformer-Based Sea Surface Wind Field Retrieval from SAR Imagery in Taiwan’s Offshore Waters

*De-Ming Wang¹, Chiung-Shen Ku², Yunung Nina Lin³, Chian-Yi Liu⁴ (1. Department of Electrical Engineering, National Taiwan University, 2. Institute of Aerospace and Systems Engineering, National Taipei University of Technology, 3. Institute of Earth Sciences, Academia Sinica, 4. Research Center for Environmental Changes, Academia Sinica)

16:45 〜 17:00

[ATT49-12] Deep Learning Prediction of 30-Day Sea Surface Height Anomalies in the Kuroshio–Extension Region Using a Conv3D Encoder–Decoder Framework

*Kalpesh Ravindra Patil¹, Toru Miyama², Yasumasa Miyazawa³, Ratnam J. V.², Alessandro Damiani² (1. Researcher, APL, VAiG, JAMSTEC, 2. Senior Researcher, APL, VAiG, JAMSTEC, 3. Deputy Director, APL, VAiG, JAMSTEC)

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