Japanese

Multidisciplinary and Interdisciplinary (M)
Session Sub-categoryGeneral Geosciences, Information Geosciences & Simulations (GI)
Session IDM-GI27
Session Title Data-driven approaches for weather and hydrological predictions
Short Title Data driven study in weather prediction
Date & Time Oral
Session		 AM1-AM2 Thu, 29 MAY
On-site Poster
Coretime		 PM3 Thu. 29 MAY
Main Convener Name Shunji Kotsuki
Affiliation Center for Environmental Remote Sensing, Chiba University
Co-Convener 1 Name Daisuke Hotta
Affiliation Meteorological Research Institute
Co-Convener 2 Name Yuki Yasuda
Affiliation Tokyo Institute of Technology
Co-Convener 3 Name Thomas Sekiyama
Affiliation Meteorological Research Institute
Session Language E
Scope (Session Description) 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.
Session Format Orals and Posters session
Co-sponsorship Partner Union(s) -
JpGU Society Member(s) Meteorological Society of Japan, Japan Society of Hydrology & Water Resources
International Collaborative Society -
Organizations Other Than JpGU Society Members -
Time Presentation No Title Presenter
Oral Presentation May 29 AM1
9:00 - 9:15 MGI27-01 Global precipitation nowcasting with ConvLSTM and adversarial training Shigenori Otsuka
9:15 - 9:30 MGI27-02 Conditional Deep Diffusion Modeling for GSMaP Inpainting Daiko Kishikawa
9:30 - 9:45 MGI27-03 Deep learning approach to subseasonal prediction of the western North Pacific subtropical high: transfer and multitask learning Yuki Maeda
9:45 - 10:00 MGI27-04 Sequential analysis of tipping in high-dimensional complex systems with partially known dynamics Tomomasa Hirose
10:00 - 10:15 MGI27-05 Automatic Front Detection Using Deep Learning: Leveraging Temporal Data and Local Explanations with Attention Mechanisms Takumi Matsuda
10:15 - 10:30 MGI27-06 ClimaX-LETKF: A pure data-driven artificial intelligence-based ensemble weather forecasting system Akira Takeshima
Oral Presentation May 29 AM2
10:45 - 11:00 MGI27-07 Synchronization in Turbulence and Its Significance for Data-Driven Approaches Masanobu Inubushi
11:00 - 11:15 MGI27-08 Multi-Model Ensemble and Reservoir Computing for Efficient River Discharge Prediction in Ungauged Basins Mizuki Funato
11:15 - 11:30 MGI27-09 Leveraging Japan's National Streamflow Records for End-to-End Data-Driven Hydrological Modeling at National Scale Tristan Hascoet
11:30 - 11:45 MGI27-10 Precipitation super-resolution using diffusion model with d4PDF Ryo Kaneko
11:45 - 12:00 MGI27-11 Toward enhancing the ensemble Kalman filter with a diffusion model Takumi Honda
12:00 - 12:15 MGI27-12 Real-Time 3D Super-Resolution for Urban Micrometeorology Using Diffusion Models and Schrödinger Bridge Yuki Yasuda
Presentation No Title Presenter
Poster Presentation May 29 PM3
MGI27-P01 Manifold learning-aided offline parameter estimation of an Earth system model using observation of changing climate Amane Kubo
MGI27-P02 Weather field super-resolution using Restricted Boltzmann Machines Ryo Kaneko
MGI27-P03 Probabilistic Ensemble Generation Using a Diffusion Model Trained on JMA MSM Data Natsumi Saito