Session information

[E] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI26] Data-driven approaches for weather and hydrological predictions

Thu. May 30, 2024 1:45 PM - 3:15 PM 106 (International Conference Hall, Makuhari Messe)

convener:Shunji Kotsuki(Center for Environmental Remote Sensing, Chiba University), Daisuke Matsuoka(Japan Agency for Marine-Earth Science and Technology), Atsushi Okazaki(Chiba University), Yohei Sawada(The University of Tokyo), Chairperson:Atsushi Okazaki(Chiba University)

In the digital era, data-driven techniques are transforming our understanding and prediction capabilities of complex earth systems. This session aims to explore the cutting-edge methodological and applicational studies for weather, climate and hydrological predictions.
Key themes includes: (1) methodological studies to deepen data-driven approaches for geoscience problems, (2) machine/deep learning studies applied for extreme weather-related disasters, (3) climate predictive analysis to discern climate variability, trends, and anomalies, (4) integrating remote sensing and ground data to refine prediction models.
This session aims to foster a rich dialogue among experts, highlighting both the advancements and challenges in data-driven environmental modeling. Participants will gain insights into current best practices and envision the future trajectory of this rapidly evolving domain.

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1:45 PM - 2:00 PM

[MGI26-01] Machine learning enables real-time proactive quality control: A proof-of-concept study

★Invited Papers

*Takumi Honda¹, Akira Yamazaki² (1.Faculty of Science, Hokkaido University, 2.Application Laboratory, JAMSTEC)

2:00 PM - 2:15 PM

[MGI26-02] Predictability of climate tipping: data assimilation approach

*Amane Kubo¹ (1.University of Tokyo)

2:15 PM - 2:30 PM

[MGI26-03] Short-term deep-learning rainfall prediction in Japan

*Ryo Kaneko^1,2, Kei Yoshimura² (1.Chiba University, 2.The University of Tokyo)

2:30 PM - 2:45 PM

[MGI26-04] Advancing Multi-Hazard Worst-Case Scenario Analysis: Maximizing the Potential of Large Ensemble Tropical Cyclone Forecasts

*Md. Rezuanul Islam¹, Le Duc^1,2, Yohei Sawada^1,2 (1.The Univ. of Tokyo, 2.Meteorological Research Institute, JMA)

2:45 PM - 3:00 PM

[MGI26-05] Forecasting monthly sea surface temperature using Koopman mode decomposition

*Daiya Shiojiri¹, Yoshihiko Susuki², Shunji Kotsuki¹ (1.Chiba University, 2.Kyoto University)

3:00 PM - 3:15 PM

[MGI26-06] Earth System Modeling in Latent Space: A Time-Varying Parameter Approach

*Yohei Sawada^1,2, Le Duc -^1,2 (1.The University of Tokyo, 2.Meteorological Research Institute, Japan Meteorological Agency)