Multidisciplinary and Interdisciplinary (M)
Session Sub-category General Geosciences, Information Geosciences & Simulations(GI)
Session ID M-GI26
Title Data-driven approaches for weather and hydrological predictions
Short Title Data driven study in weather prediction
Main Convener Name Shunji Kotsuki
Affiliation Center for Environmental Remote Sensing, Chiba University
Co-Convener 1 Name Daisuke Matsuoka
Affiliation Japan Agency for Marine-Earth Science and Technology
Co-Convener 2 Name Atsushi Okazaki
Affiliation Chiba University
Co-Convener 3 Name Yohei Sawada
Affiliation The University of Tokyo
Session Language 
E
Scope 
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.
Presentation Format Oral and Poster presentation
Invited Authors Takumi Honda (Faculty of Science, Hokkaido University)
Yuki Yasuda (Tokyo Institute of Technology)
Kenta Shiraishi (Chiba University)
Time Presentation No Title Presenter
Oral Presentation May 30 PM1
13:45 - 14:00 MGI26-01 Machine learning enables real-time proactive quality control: A proof-of-concept study Takumi Honda
14:00 - 14:15 MGI26-02 Predictability of climate tipping: data assimilation approach Amane Kubo
14:15 - 14:30 MGI26-03 Short-term deep-learning rainfall prediction in Japan Ryo Kaneko
14:30 - 14:45 MGI26-04 Advancing Multi-Hazard Worst-Case Scenario Analysis: Maximizing the Potential of Large Ensemble Tropical Cyclone Forecasts Md. Rezuanul Islam
14:45 - 15:00 MGI26-05 Forecasting monthly sea surface temperature using Koopman mode decomposition Daiya Shiojiri
15:00 - 15:15 MGI26-06 Earth System Modeling in Latent Space: A Time-Varying Parameter Approach Yohei Sawada
Oral Presentation May 30 PM2
15:30 - 15:45 MGI26-07 Super-Resolution Data Assimilation Using Denoising Diffusion Probabilistic Models Yuki Yasuda
15:45 - 16:00 MGI26-08 Parameter estimation of an atmospheric model using geostationary satellite observation to improve prediction of tropical cyclones: an idealized experiment Yuki Hirose
16:00 - 16:15 MGI26-09 Uncertainty Quantification of Flood Hazards Focusing on Parameters of a Rainfall-Runoff-Inundation Coupled Model Naoki Hiura
16:15 - 16:30 MGI26-10 Deep Learning for Boreal Summer Intraseasonal Oscillation (BSISO) Prediction and Explainability Yuki Maeda
16:30 - 16:45 MGI26-11 Downscaling Precipitation Data using Convolutional Neural Network Coupled with Wasserstein Generative Adversarial Networks Kenta Shiraishi
Presentation No Title Presenter
Poster Presentation May 30 PM3
MGI26-P01 Real-Time Forecasting of Energy Consumption in Residential Buildings Jui Sheng Chou
MGI26-P02 Disentanglement of the features in Rankine vortices using VAE Keitaro Inuki
MGI26-P03 Machine learning based post-processing to enhance daily rainfall forecasts of July 2019-2021 over the Kyushu region focusing on surface conditions at remote moisture sources Muditha Madusanka Dantanarayana
MGI26-P04 Improving global precipitation estimates from rain gauge observations using local ensemble data assimilation Yuka Muto