日本地球惑星科学連合2025年大会
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-TT35 | |||
| タイトル | Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions | |||
| タイトル短縮名 | Machine Learning Techniques applications | |||
| 開催日時 | 口頭セッション | 5/30(金) PM1-PM2 | ||
| 現地ポスター コアタイム |
5/30(金) PM3 | |||
| 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 |
Advances in the machine learning techniques such as deep learning have led to an increase in the application of the techniques to a wide range of topics such as weather, climate, ocean, hydrology, and disease predictions. In the recent times, these techniques are being increasingly used to predict extreme events such as malaria outbreaks, heat waves, cold spells, flooding, droughts, tropical cyclones, typhoons, El Nino/Indian Ocean Dipole events among many others. In addition, machine-learning techniques are helping researchers to improve parameterization schemes in numerical prediction models. Machine-learning is also being used to improve numerical model predictions by providing methods to reduce biases and improve the horizontal resolution of the predictions. This session aims to bring together the researchers working on various machine learning techniques to discuss and enhance our understanding of weather, climate, Ocean, hydrology and tropical diseases as well as their predictions and applications for societal benefits and well-being. |
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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 | |||
| 共催情報 | 学協会 | 日本海洋学会 | ||
| ジョイント | - | |||
| 団体会員以外の組織との共催 | - | |||
| 国際連携団体 | - | |||