大気水圏科学(A)
セッション小記号 計測技術・研究手法(TT)
セッションID A-TT49
タイトル 和文 Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
英文 Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
タイトル短縮名 和文 Machine learning for climate
英文 Machine learning for climate
代表コンビーナ 氏名 和文 Jayanthi Venkata Ratnam
英文 Venkata Ratnam Jayanthi
所属 和文 Application Laboratory, JAMSTEC
英文 Application Laboratory, JAMSTEC
共同コンビーナ 1 氏名 和文 Patrick Martineau
英文 Patrick Martineau
所属 和文 Japan Agency for Marine-Earth Science and Technology
英文 Japan Agency for Marine-Earth Science and Technology
共同コンビーナ 2 氏名 和文 土井 威志
英文 Takeshi Doi
所属 和文 JAMSTEC
英文 JAMSTEC
共同コンビーナ 3 氏名 和文 Behera Swadhin
英文 Swadhin Behera
所属 和文 Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
英文 Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
発表言語 E
スコープ 和文 
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.
英文 
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.
発表方法 口頭および（または）ポスターセッション
時間 講演番号 タイトル 発表者
口頭発表 5月27日 PM1
13:45 - 14:00 ATT49-01 Fine-tuning an AI weather model (GraphCast) for improved ensemble subseasonal forecasting Takeshi Izumo
14:00 - 14:15 ATT49-02 Predicting Monsoon Intraseasonal Oscillations with Deep Learning: A Skill Assessment Sandeep Sukumaran
14:15 - 14:30 ATT49-03 Assessing deterministic and probabilistic skill of deep learning post-processing for S2S wind forecasts Damiani Alessandro
14:30 - 14:45 ATT49-04 DiffRoute: Global Climatic River Routing Simulations in less than 10s on a single GPU. Tristan Hascoet
14:45 - 15:00 ATT49-05 Machine Learning based prediction of Tropical Cyclone Rapid Intensification in the Western North Pacific JinHo Yoon
15:00 - 15:15 ATT49-06 Deep learning for kilometer-scale downscaling of near-surface meteorological variables in a temperate megacity Alen Kospanov
口頭発表 5月27日 PM2
15:30 - 15:45 ATT49-07 Emulating atmospheric transport and plume evolution with deep learning Donald D Lucas
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
16:00 - 16:15 ATT49-09 Information Quantity, Quality, and Machine Learning Accuracy in Hydrologic Prediction Young gu Her
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
16:30 - 16:45 ATT49-11 Vision Transformer-Based Sea Surface Wind Field Retrieval from SAR Imagery in Taiwan’s Offshore Waters De-Ming Wang
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 Patil
講演番号 タイトル 発表者
ポスター発表 5月27日 PM3
ATT49-P01 Seasonal Prediction of Heat Waves in Japan Using Machine Learning Patrick Martineau
ATT49-P02 Improving the Accuracy of Visibility Forecasting in the Chiayi and Kinmen Regions Using Neural Network Models Yu Chen Weng
ATT49-P03 GNSS-R Wind Speed Retrieval under High Wind Conditions:
A Neural Network Approach with TRITO 		ZAN HAN HUANG
ATT49-P04 Rice Yield Prediction Using Machine Learning and Unmanned Aerial Vehicle-based Red Green Blue Imagery Julien Eric Boulange
ATT49-P05 Efficient Generative Downscaling of Climate Extremes: Optimizing DDPM Ensembles for Heatwaves and Cold Spells Shailesh Kumar Jha
ATT49-P06 Simulating Nighttime Visible Imagery Using CGAN Yubo Wang
ATT49-P07 Estmation of Maximum Toe Scour Depth for Embankment under Oblique Flow Using Machine Learning Algorithms CHI EN CHIU
ATT49-P08 Improving intraseasonal predictions of Indian summer monsoon using super resolution convolutional neural networks Jayanthi Venkata Ratnam
ATT49-P09 気象因子が辣木のNDVI変動に及ぼす影響のSHAPによる解析 王 子健
ATT49-P10 The Hidden Patterns of Marine Heatwaves: An Unsupervised Approach Ratu Almira Kismawardhani