日本地球惑星科学連合2023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-TT29 | |||
| 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 application | |||
| 開催日時 | ||||
| 口頭 セッション |
5/22(月) PM1 |
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| 現地 ポスター コアタイム |
5/22(月) PM3 | |||
| オンライン ポスター セッション |
5/23(火) AM2 | |||
| 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 |
Machine learning techniques have found a wide range of applications in weather, climate, ocean, hydrology, and disease predictions. In 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. The aim of this session is to bring together researchers working on various techniques of machine learning to enhance the understanding and skill of weather, climate, ocean, hydrology and disease predictions for the benefit of the society. |
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| 発表方法 | 口頭およびポスター | |||
| 共催情報 | 学協会 | 日本海洋学会 | ||
| ジョイント | - | |||