2024年5月30日(木) 09:00 〜 10:15 304 (幕張メッセ国際会議場)

コンビーナ:Jayanthi Venkata Ratnam(Application Laboratory, JAMSTEC)、Martineau Patrick(Japan Agency for Marine-Earth Science and Technology)、土井 威志(JAMSTEC)、Behera Swadhin(Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001)、座長:Jayanthi Venkata Ratnam(Application Laboratory, JAMSTEC)、Patrick Martineau(Japan Agency for Marine-Earth Science and Technology)、Behera Swadhin(Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001)

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.