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

Wed. May 27, 2026 1:45 PM - 3:15 PM 101 (International Conference Hall, Makuhari Messe)

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

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.

+Add all to schedule

1:45 PM - 2:00 PM

[ATT49-01] Fine-tuning an AI weather model (GraphCast) for improved ensemble subseasonal forecasting

★Invited Papers

Vateanui Sansine^1,2,3, *Takeshi Izumo^1,2,3, Marania Hopuare^4,3, Damien Specq⁵, Sophie Martinoni-LaPierre⁵ (1. IRD (Institut de Recherche pour le Developpement; French National Research Institute for Sustainable Development), 2. UMR241 SECOPOL (Tahiti, French Polynesia), 3. Universite de Polynesie Francaise (UPF), 4. GEPASUD, University of Polynesia, Campus d ’ Outumaoro, 98718 Puna ’ auia, Tahiti, French Polynesia, 5. Météo - France, 42 Av. Gaspard Coriolis, 31100 Toulouse)

2:00 PM - 2:15 PM

[ATT49-02] Predicting Monsoon Intraseasonal Oscillations with Deep Learning: A Skill Assessment

*Sandeep Sukumaran¹, K M Anirudh¹, Hariprasad Kodamana¹ (1. Indian Institute of Technology Delhi, New Delhi 110016, India)

2:15 PM - 2:30 PM

[ATT49-03] Assessing deterministic and probabilistic skill of deep learning post-processing for S2S wind forecasts

*Alessandro Damiani¹, Yasumasa Miyazawa ¹, Masami Nonaka ¹, Jayanthi V. Ratnam ¹, Kalpesh R. Patil ¹ (1. Japan Agency for Marine-Earth Science and Technology)

2:30 PM - 2:45 PM

[ATT49-04] DiffRoute: Global Climatic River Routing Simulations in less than 10s on a single GPU.

*Tristan Hascoet^1,2, Takemasa Miyoshi^2,1 (1. RIKEN Center for Computational Science, 2. RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences)

2:45 PM - 3:00 PM

[ATT49-05] Machine Learning based prediction of Tropical Cyclone Rapid Intensification in the Western North Pacific

*JinHo Yoon¹, Sanghyuk An¹, Jiwon Jeong¹, Seok-Woo Son³, Hyungjun Kim⁴, Jee-Hoon Jeong² (1. GIST Gwangju Institute of Science and Technology, 2. Sejong University, 3. Seoul National University, 4. KAIST)

3:00 PM - 3:15 PM

[ATT49-06] Deep learning for kilometer-scale downscaling of near-surface meteorological variables in a temperate megacity

*Alen Kospanov^1,2,4, Mikhail Varentsov^2,4,3, Mikhail Krinitskiy^5,6, Viktor Stepanenko^1,3 (1. Lomonosov Moscow State University, 2. Hydromencenter of Russia, 3. RCC MSU, 4. RSHMU, 5. MIPT, 6. Shirshov Institute of Oceanology)

Session information