日本地球惑星科学連合2022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-TT32 | |||
| 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 Earth Sciences | |||
| 開催日時 | 口頭セッション | 5/24(火) PM1, PM2 |
現地会場 | |
| 現地ポスターコアタイム | 5/24(火) 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 | 松岡 大祐 | ||
| 所属 | 海洋研究開発機構 | |||
| 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 |
The 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 widely used to predict extreme events such as malaria outbreaks, heat waves, cold waves, flooding, droughts, tropical cyclones, typhoons, El Nino/Indian Ocean Dipole and many others. The techniques are helping the researchers improve the parameterization schemes in the numerical models. The techniques are also being used to improve the numerical model predications by providing methods to reduce the biases and also to improve the horizontal resolution of the forecasts. The aim of the session is to bring together the researchers working on various techniques of machine learning to enhance the understanding and predictions of weather, climate, ocean, hydrology and disease predictions for the benefit of the society. |
|||
| 発表方法 | 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 | - | |||