セッション概要
| 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 | Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions | |||
| タイトル短縮名 | Machine learning for climate | |||
| 代表コンビーナ | 氏名 | Jayanthi Venkata Ratnam | ||
| 所属 | Application Laboratory, JAMSTEC | |||
| 共同コンビーナ 1 | 氏名 | Patrick Martineau | ||
| 所属 | Japan Agency for Marine-Earth Science and Technology | |||
| 共同コンビーナ 2 | 氏名 | 土井 威志 | ||
| 所属 | JAMSTEC | |||
| 共同コンビーナ 3 | 氏名 | Behera Swadhin | ||
| 所属 | Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001 | |||
| セッション言語 | E | |||
| スコープ | 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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| セッション形式 | 口頭およびポスターセッション | |||