Session Outline
| Multidisciplinary and Interdisciplinary (M) | ||||
|---|---|---|---|---|
| Session Sub-category | General Geosciences, Information Geosciences & Simulations (GI) | |||
| Session ID | M-GI34 | |||
| 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 | Carolynne Hultquist | ||
| Affiliation | University of Canterbury | |||
| Co-Convener 2 | Name | Yohei Sawada | ||
| Affiliation | The University of Tokyo | |||
| Co-Convener 3 | Name | Ana Carolina Vaz | ||
| Affiliation | Instituto Nacional de Pesquisas Oceanicas | |||
| Co-Convener 4 | Name | Thomas Sekiyama | ||
| Affiliation | Meteorological Research Institute | |||
| Co-Convener 5 | Name | Yuki Yasuda | ||
| Affiliation | Japan Agency for Marine-Earth Science and Technology | |||
| 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.
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| Session Format | Orals and Posters session | |||